Ground Truths

Dr. Trisha Pasricha is a gastroenterologist, Assistant Professor at Harvard Medical School, and the Ask a Doctor columnist for The Washington Post. Her new book gets deep into the stigmatized, taboo, rarely discussed topics of our bowel movements and farts, no less the science that backs that up, with lucid explainers for the mechanisms (such as the gut-brain axis). Her research is on the gut origin of Parkinson’s disease. Here’s a quote from Trisha early in the book: “I’m not a wellness guru. I’m a scientist.” Another quote from a Vanderbilt professor: “An effortless, socially appropriately timed bowel movement is the second greatest bodily pleasure known to man.” The proportion of people with bowel issues is remarkable, a brief summary: Some of the topics covered in our lively, fun and highly informative conversation: —The 3 P’s Propulsion, Pliability, Pelvic Floor —Ten Myths —99% of farts have no odor; the 1% that do are from hydrogen sulfide, Pepto-Bismol substantially reduces the latter (and an incredible experiment of people with rectal tubes and judges smelling their gas) —What is the normal frequency of pooping? —the Bristol Scale of poop appearance and what it means —Transit time —The uselessness and danger of bowel cleanses —Fibermaxxing —5 minutes max on the toilet and positions to help propulsion —Psyllium and laxatives —The art of wiping and bidets —Fecal incontinence —Not to miss urges and the TRPV1 and PIEZO2 receptors —Impact of coffee, alcohol, NSAIDs (Ibuprofen, aspirin, naproxen, etc), and hydration —What are the different definitions of constipation? —What gives poop its color? ******************************************** Thank you YOUR DOCTOR KLOVER, Nickhil Jakatdar, Ph.D., Bob Fleischman, karemm, Jessica Nguyen, and >600 others for tuning into our live video with Trisha Pasricha! Join me for my next live video and newsletters in the app. And a big thanks to Ground Truths subscribers (> 200,000) from every US state and 212 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026. Get full access to Ground Truths at erictopol.substack.com/subscribe

Adrian is a molecular biologist and co-founder and CEO of Genyro, a synthetic biology company. He has published 2 previous books on life science (Life Without Genes and An Intelligent Person’s Guide to Genetics). In this conversation we discussed his new book, cover below and the title of this post. ABI. As Adrian puts it, a monumental shift, a second genesis, the “artivolution,” ability to use a molecular Gutenberg press, it’s post-Darwinian. I made an infographic about the book and our conversation with NotebookLM The expansion of genome sequences beyond “Fred’s library” (referring to Fred Sanger, father of genome sequencing, little green box) We talked about the jump from DNA reading to editing and now writing genomes, with the potential of the latter for both good and harm. I disagreed with Adrian about the bright prospects for curing diseases, but there are many possibilities for positive impact, such as intervening vs the climate crisis and sustainability. We also discussed the large language of life models (LLLMs) and by coincidence my colleagues and I just published a review of these in this Nature Biotechnology (←free access is hyperlinked). “It should be possible to meet most of humanity’s needs through biologically inspired designs—to solve many global problems, revolutionize health care, extend human lifespan, and create other organisms to order.”—Adrian Woolfson If you are into life science, I think you’ll find Adrian’s book thoughtful and provocative, even if you don’t agree with some of his optimism like a disease-free organism. The topic connects with digital biology, a theme that is approached in many editions of Ground Truths, such as this one with Patrick Hsu *************************************************** Thank you Ric Bayly, Kevin Johnson, MD, YOUR DOCTOR KLOVER, Patricia Scott, Anne, and more than 600 others for tuning into my live video with Adrian Woolfson! Join me for my next live video in the app. And a big thanks to Ground Truths subscribers (> 200,000) from every US state and 212 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026. Get full access to Ground Truths at erictopol.substack.com/subscribe

Yo-El Ju is the Barbara Burton and Reuben Morris Professor of Neurology at Washington University, St. Louis. She got her AB from Harvard and MD from Columbia and actively practices sleep medicine and is a prolific researcher, one of the top sleep scientists in the country. Here are some of the topics we discussed and a few related hyperlinked citations: —Importance of Deep Sleep (and her paper on what happens when deep sleep is purposely disrupted, how it is modulated, effect of alcohol —The Orexin Antagonist drugs that promote sleep (and a study that shows they can reduce p-tau217 and other neuroinflammation markers —Link of sleep regularity with less all-cause, cardiovascular, and cancer related mortality Figure 3.9 from SUPER AGERS (SRI-sleep regularity index) More things we discussed: —A sleep foundation model that predicts 130 diseases —Impact of menopause on sleep —Getting to sleep vs staying asleep —Role of naps —Impact of interruptions of sleep —Sleep apnea and new interventions —Vagal nerve stimulation and sleep —Cerebrospinal fluid wave that occurs during attention lapse after poor sleep —cognitive behavioral therapy for improved sleep —Wearables, sleep scores, and effect on sleep —Any supplements that help sleep? —The rare genetically endowed short sleepers Thank you Jeoffry Gordon, MD, MPH, Dr. Sara Wolfson, Lynn L, Vau Geha, Bernie Newman, and more than 600 others for tuning into my live video with Yo-El Ju! Join me for my next live video in the app. ************************************************** Thanks to Ground Truths subscribers (> 200,000) from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026. +++++++++++++++++++++++++++++ I also want to thank the National Academies of Science, Medicine and Engineering for their recent recognition. Get full access to Ground Truths at erictopol.substack.com/subscribe

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The vagus nerve, also known as the Great Nerve, connects the brain to all parts of the body, like an internet (Figure below). Every week we’re learning more about its importance for health and disease. Until recent years the brain and immune system were thought to be in separate “firewalled” compartments. But that turned out to be far from the truth. Dr. Kevin Tracey, who directs the Feinstein Institute at Northwell Health, has been studying the vagus nerve for more than 3 decades. In the early 1990’s he made the seminal observation that stimulating the vagus reduces the inflammatory cytokine known as tumor necrosis factor (TNF), what he described at the time as a “holy s**t” moment. In 2025 he published the book THE GREAT NERVE with many rich patient anecdotes and the history for how this field developed. I wrote about the brain-immune axis previously on Ground Truths, spotlighting the vagus nerve’s role. That included much more on depth of the pathways if you are interested. We discussed the recent FDA July approval for refractory rheumatoid arthritis (RA)(unresponsive to medications or intolerance to medical therapy) based on a sham-controlled randomized trial published in December 2025 in Nature Medicine. It is striking that the benefits were derived from 1 minute of vagal stimulation per day. That stimulation is barely perceived (tingling, many not at all) by device recipients. The surgery takes one hour to implant the tiny stimulator device along side the vagus nerve in the neck. But this is much bigger than a treatment for RA. It is now being tested for lupus, Crohn’s disease, and other autoimmune conditions. That’s beyond the role vagus nerve stimulation (VNS) has played for epilepsy and depression, independent of VNS’s anti-inflammatory impact (Tracey named it “the inflammatory reflex”). We also discussed handheld VNS devices, potential use for Long Covid and POTS, cold plunges, heart rate variability, stress, and other topics related to the vagus nerve and parasympathetic nervous system (the brakes, “rest and digest” as opposed to the sympathetic nervous system (the gas, “fight or flight”). Most people are not aware of this device approved treatment for an autoimmune disease. Our treatments are so drug-centric but they are immunosuppressive, have important side-effects, and are expensive. It’s good to see a non-drug approach get compelling data as an alternative. Moreover, as I recently wrote about, there’s a shift ongoing from treatment to cures vs autoimmune diseases that will be adding to the mix. Here are some very recent papers about the vagus nerve to give you a sense about how its prominence is getting appreciated more all the time Gut-brain-vagus axis for reward circuit and addiction, 30 Jan 2026, Science Advances Randomized trial of VNS for depression , Int J of Neuropsychopharm 2026 The vagus nerve role in heart function after a heart attack, Cell, February 2026 Vagus receptors and hemorrhage, blood volume, Nature, 28 Jan 2026 A Quick Poll Thank you Harshi Peiris, Ph.D., Tay MacIntyre, David Dansereau, MSPT, Max Manwaring-Mueller, RJ, and over 600 others for tuning into my live video with Kevin J. Tracey, MD! Join me for my next live video with Robert Wachter Feb 4th, 12:30 PM PT in the app. ********************************************** Thanks to Ground Truths subscribers (approaching 200,000) from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026. Get full access to Ground Truths at erictopol.substack.com/subscribe

Back in 2005 Dan Buettner patented Blue Zones. He’s written 9 books, of which 5 were NY Times bestsellers, and has another one in the works. His 2023 Netflix documentary series “Live to 100: Secrets of the Blue Zones” won Emmy awards and was widely viewed. Many of the main points that Dan has emphasized over the years—healthy diet (he highlighted whole grains, beans, nuts, greens, and “peasant food” here), physical activity, social engagement, and sense of purpose—are backed up by randomized trials and/or large, prospective studies, as I reviewed in Super Agers. In this interview, I asked Dan about the critique he’s faced, predominantly from Saul Newman, a biologist in the UK, about accuracy of the data for extreme longevity in the blue zones. In December 2025, two of his collaborators published in The Gerontologist a rebuttal response (open-access) entitled “The validity of Blue Zones demography: a response to critiques” on the rigorous demographic work they’ve done. We also discussed the marketing and business operations of Blue Zones LLC, the company he sold to Adventist Health in 2020. His son, Danny, is the EVP of the company and likely successor to Dan’s work in the future. As Dan said, “He’s my genetic upgrade.” I hope you find this conversation informative. An AI-generated transcript is available in the tab at top right above. Some biomedical updates from the past week: * A vaccine to prevent colon cancer in carriers of mutations of Lynch Syndrome looks good for strong immune response and safety, ready for Phase 3 clinical trials * A review of the data of Tylenol during pregnancy reaffirms its safety and lack of increased risk for autism, ADHD, and intellectual disability. * Our perspective/review on how AI and large language models can reduce global health inequities * Another big jump vs refractory autoimmune diseases, this time for hemolytic anemia, using engineered T cells. See my recent review of this topic, from treatment to cures. * A pair of new large prospective cohort studies that reinforce the importance of even small increases in physical activity, along with sleep and diet, for reduction all-cause mortality and improvement in healthspan. Thanks to Ground Truths subscribers (nearly 200,000) from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026 Thank you Annie Fenn, MD, Harshi Peiris, Ph.D., Strategy Shots, Rebecca Moore, Lynn L, and many others for tuning into my live video with Dan Buettner! Join me for my next live video in the app. Get full access to Ground Truths at erictopol.substack.com/subscribe

Jonathan Kipnis is a neuroscientist, the Alan A. and Edith L. Wolff Distinguished Professor of Pathology and Immunology at Washington University, St. Louis, who discovered meningeal lymphatics and has been a prolific researcher in brain drainage and the continuous immune system surveillance of the brain. I made this infographic with the help of Notebook LM to summarize many of the concepts we discussed. (Notebook LM is free and worth trying) We went over his new review with 24 co-author leading experts in the recent issue of Neuron A Clever Cover The drainage system anatomy on influx and efflux (blue arrows) The 3 ways the flow of glymphatics are modulated. I mentioned the recent studies that show atrial fibrillation, via reduced cardiac pulsation, has an effect on reducing glymphatic flow. We also discussed his recent review on the immune surveillance system in Cell: A schematic of key channels for the “faucet” and “drain” and how the system changes from healthy to central nervous system autoimmune diseases (such as multiple sclerosis) and aging with different immune bar codes. The outsized role of astrocytes in the brain, a subject of recent Nature feature, was also mentioned. Our understanding of the brain’s immune system has been completely revamped. Kipnis’s recent review in Nature Immunology highlights the critical role of the outer layers —the skull, dura and meninges—as an immune reservoir that is ready to detect and react abnormalities in the brain with a continuous “intelligence report.” Notably, Kipnis touched on lymphatic-venous anastomosis (LVA) surgery (Figure below) for Alzheimer’s disease which is popular in China, available at 30 centers in multiple cities, and the subject of multiple randomized trials as a treatment for Alzheimer’s. Trials of LVA surgery are also getting started in the United States for treatment of Alzheimer’s and Parkinson’s diseases. Here is a Figure to show the surgical anastomoses (connections) from the deep cervical lymphatics to external jugular and internal jugular veins This podcast was packed with insights relevant to health, spanning sleep quality, sleep medications, autoimmune diseases, and Alzheimer’s disease. I hope you find it as informative and engaging as I did. A Poll ************************************ This is my 4-year anniversary of writing Ground Truths. Post number 250! That’s an average of more than 1 per week, nearly 5 per month. Hard for me to believe. Thanks to Ground Truths subscribers (approaching 200,000) from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. Please join! If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026 Thank you EG, Alan, Lynn L, Stacy Mattison, Jackie, and many others for tuning into my live video with Jonathan Kipnis! Join me for my next live video in the app. Get full access to Ground Truths at erictopol.substack.com/subscribe

A recording from my enthralling conversation with Prof Matthew Cobb about the life and science contributions of Francis Crick, regarded as one of the most influential biologists of all times, along with Darwin and Mendel. As you’ll see, there’s so much more to Crick’s story than cracking DNA’s double helix structure in a matter of weeks with James Watson. Matthew Cobb, Emeritus Professor of the University of Manchester, has written several award-winning books on life science, but I think this is his most important one to date, deeply researched and a thrilling account of Crick’s life, clearing up, as best as one can, many questions, and presenting some surprises. The transcript is available (A.I. generated) by clicking at the top right. A few things we discussed —Crick’s reaction to James Watson’s book Crick contrasted his own approach to science writing with Watson’s memoir: “The difference between my lecture and your book is that my lecture had a lot more intellectual content and nothing like so much gossip. (...) Your book on the other hand, is mainly gossip and I think it a pity in this way that there is so much of it that it obscures some of the important conclusions which can be drawn of what we did at the time”. —The Peyote Poem, by Michael McClure (part 1) that had a big influence on Crick —Crick’s 1994 neuroscience book “The Astonishing Hypothesis” “You, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free Weill, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules.”—Crick The book has been reviewed at Science, Nature, The Economist and many other journals. Here is a gift link to The Economist It has deservedly been named a best book of 2025 by The Guardian, The Economist, and many other media. Thank you Bruce Lanphear, Harshi Peiris, Ph.D., Elisabetta Pilotti, Allan Konopka, Stephen B. Thomas, PhD, and over 500 others for tuning into my live video with Matthew Cobb! ********************* Upcoming, this Wednesday 9AM PT, live podcast I will be interviewing Dan Buettner founder of the Blue Zones Join us! ********************** Thanks to US News for recently being named one of the 25 best leaders in the United States https://www.usnews.com/news/leaders/articles/best-leaders-2025-eric-topol ^^^^^^^^^^^^^^^^^^^^^^ Thanks to >190,000 Ground Truths subscribers from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026. Get full access to Ground Truths at erictopol.substack.com/subscribe

A couple of weeks ago, the FDA Commissioner published a WSJ oped “The FDA Liberates Women’s Hormone Replacement Therapy” (←gift link) and, with other FDA colleagues, a JAMA essay entitled “Updated Labeling for Menopausal Hormone Therapy” (open-access). That change, and the data cited, led to a series of articles in the days that followed, such as at STAT News “FDA reverses decades-old warning on hormone therapy products for menopause. Agency says the treatments o!fer heart, brain, and bone health benefits” and at the Washington Post “The FDA finally corrects its error on menopause hormone therapy. Women have been needlessly scared away from effective treatments.” If you read through these links, you’ll be confused. Does MHT have proven cognitive benefits? What about a study from 1991 that showed ~50% reduction of fatal heart events with MHT? Or the 35% decreased risk of Alzheimer’s disease? Or the breast cancer increased risk attributed to medroxyprogesterone acetate? I turned to my go-to gynecologist truth teller, Dr. Jen Gunter, to get her review of the evidence. This is a complex topic, with old data from the 2002 Women’s Heath Initiative (WHI), new reports since, population level analysis, changes in preparations of MHT including local delivery, and much more. Here is our conversation which isn’t just about MHT but includes “Big Wellness” marketing direct to middle aged women, the new FDA approved drug for hot flashes, the $14 million cut of the NIH’s Office of Women’s Health , marked increase in philanthropic support of women’s health research, the Surgeon General nominee, ovarian failure, and a lightning round on proven benefits of MHT. Here’s a brief clip on her views of the women’s health “wellness” predators We also discussed the reasons for Dr. Gunter’s planned move next year back to Canada after practicing gynecology for 3 decades in the United States. I referred to a recent GT I wrote about the WHI and the potential favorable impact of MHT on the immune system, as suggested by new data on organ clocks. That finding, which has been replicated, may be linked to healthy aging, extending healthspan. **************************** Thanks to the >190,000 Ground Truths subscribers from every US state and 210 countries. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you found this interesting PLEASE share it! Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don’t hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! We aim to accept even more of the several thousand who will apply for summer 2026.Thank you Debbie Weil, Cynthia Brumfield, Sara Garcia, Harshi Peiris, Ph.D., Liane Moccia, and over 1,000 others for tuning into my live video with Dr. Jen Gunter! Join me for my next live video in the app. Get full access to Ground Truths at erictopol.substack.com/subscribe

Dr. Susan Monarez was the first CDC Director to be confirmed by the Senate and served from July 31, 2025 – August 27, 2025. Because she refused to give approval to new vaccine recommendations without ever seeing them or their evidence and firing scientists without cause, she was fired. In my view, she’s a hero for standing up for science and speaking truth to power. In her first live interview since leaving the CDC, we review her background. That includes growing up in rural Wisconsin and getting her college and PhD education at UW-Madison, the latter in microbiology and immunology. She then went on to 18 years of government service with an extensive portfolio of jobs and management at BARDA, the White House, ARPA-H, and others, before becoming Acting Director of the CDC in early 2025. We discussed the horrific CDC shooting on August 8th, days after she started. Then we reviewed a conversation that we had on August 19th in which she laid out her exciting vision for the future of CDC, emphasizing the goal of prevention (BTW, CDC stands for Centers for Disease Control and Prevention) and asked me to help as an advisor. At the time, she was well aware, with growing tension, that her tenure at CDC might be limited. I asked about her perspective for the jobs of 4,300 people at CDC who have been terminated, which account for more than 1/3rd of the workforce, no less the gutting of the budget. Then we got into what she learned from this ordeal and her plans for the future, which includes a very ambitious initiative: 90/90/2035. As you’ll see from our conversation, Dr. Monarez is exceptionally resilient and an optimist. She’s got lots to do in the years ahead to carry out her mission of promoting human health! Dr. Monarez just started a Substack The Road Best Traveled so you can follow her there. It was a real privilege for me to do this interview with her. In deep admiration of her willingness to not only take on the job of CDC Director in tough circumstances, her professionalism during testimony at the Senate committee hearing, her impressive yet unrealized vision for transforming the CDC, and refusing to cave to immense pressure from the HHS Secretary to move ahead with his agenda. Thank you Julie, Stephen B. Thomas, PhD, David Dansereau, MSPT, Dr. Sara Wolfson, Vau Geha, and >500 others for tuning into my live video with The Road Best Traveled! Thanks for being a Ground Truths subscriber! Please spread the word. Get full access to Ground Truths at erictopol.substack.com/subscribe

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Prof Shana Kelley is the Neena Schwartz Professor of Chemistry and Biomedical Engineering at Northwestern University and President of the CZI Chicago Biohub, which brings together life scientists at Northwestern, University of Chicago, and U. Illinois Urbana Champaign. Her lab’s website provides recent publications in the 3 major areas of biomolecular sensors, rare and single cell analysis, and intracellular molecular delivery. You are undoubtedly familiar with wearable biosensors on the wrist and rings, and continuous glucose monitoring (CGM), all of which can transmit physiologic data in real time to your smartphone. What is different about Prof Kelley’s work is the ingenious way of continuously tracking any proteins in our blood via a sensor that could function just like CGM in the future (hair thin sensor applied just below the skin and data relayed to your smartphone). A proof-of-concept paper in Science showed how exquisitely sensitive such a sensor worked to track inflammation markers [interleukin-6 (IL-6) and tumor necrosis factor (TNF)] in the diabetic rat model. As seen. below, just the injection of insulin evoked inflammation, and introduction of lipopolysaccharide (LPS) did so markedly. This capability opens up the potential for monitoring body-wide inflammation in real time, but also extends to many other conditions such as autoimmune diseases, heart failure (e.g. continuous brain natriuetic peptide monitoring), and neurodegenerative diseases (with specific markers of neuroinflammation). This innovation represents a new dimension in individualized (precision) medicine. In our conversation, Shana takes us through the discovery of these unique bimolecular sensors that have no reagents, and use electricity to shake off the protein from DNA strands. And she maps out the path to clinical trials and commercialization in the next couple of years. Thank you Stephen B. Thomas, PhD, Linda Kemp, Lynn L, Pat Mumby PhD, David Hobson, and many others for tuning into my live video with Shana Kelley! Join me for my next live video in the app, along with posts on biomedical news and analysis. *********************************************************************** Thanks you for your listening, reading and subscribing to Ground Truths. If you found this interesting PLEASE share it! That makes the work involved in putting these together especially worthwhile. All content on Ground Truths—its newsletters, analyses, and podcasts, are free, open-access. Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. It enabled us to accept and support 47 summer interns in 2025! Get full access to Ground Truths at erictopol.substack.com/subscribe

Thank you Bruce Lanphear, Clyde Wilson, Tracy Dennis-Tiwary, Diego Pereyra, Dr Mike Hunter, and many others for tuning into my live video with Charlotte Blease! Join me for my next live video in the app. Whether A.I. will transform the practice of medicine in a positive way remains controversial. Health researcher Prof Charlotte Blease, on faculty at Uppsala University in Sweden and researcher at Harvard University, has written a new book —Dr. Bot—that critically assesses the unmet needs in healthcare and whether A.I. can fulfill them. She provides an optimistic viewpoint (see the subtitle), and our conversation probes whether that is justified. She has a substack, too, here Thanks for listening to Ground Truths. Through analytic essays and podcasts, I try to cover the important issues and discoveries in life science and medicine. If you have suggestions for topics I should get into, please pass them along. Get full access to Ground Truths at erictopol.substack.com/subscribe

Thank you Sara Garcia, Andrew O'Malley BSc PhD, Sam Hester, Julie, Stephen B. Thomas, PhD, and so many others for tuning into my live video with Peter Hotez! Join me for my next live video in the app. Peter Hotez and I discuss his new book, co-authored with Michael Mann, SCIENCE UNDER SEIGE, on the anti-science superstorm culminating from the climate crisis, the Covid pandemic, and a vast interconnected network that has waged a direct assault on scientific truth. During our conversation we trace history of priors in civilization, such as Lysenko and Stalinism in the last century. And acknowledge the future role of A.I. for promoting infinite disinformation. Beyond human suffering and direct health outcome consequences (such as Red Covid), the toll this is taking on the career of young scientists, universities, public health agencies, and loss of public trust are reviewed. The interdependent role of the media and the wellness industry is touched on. The book and our conversation puts forth a call to arms, potential solutions, including the need to move away from invisible scientists and political activism. Thanks for listening to Ground Truths podcasts and reading the analytic posts. In case you missed any, these are a few recent and related ones: Podcasts with Michael Osterholm and Sanjay Gupta on their new books—The Big One and It Doesn’t Have to Hurt, respectively. Next up is Charlotte Blease and her new book Dr. Bot on where we are headed with medical A.I. If you found this interesting PLEASE share it! That makes the work involved in putting these together especially worthwhile. All content on Ground Truths—its newsletters, analyses, and podcasts, are free, open-access. Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Let me know topics that you would like to see covered. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. Get full access to Ground Truths at erictopol.substack.com/subscribe

Thank you Jose Bolanos MD, Dr. Zeest Khan, Lawrence Toole, Julie, Stephen B. Thomas, PhD, and many others for tuning into my live video with Dr. Sanjay Gupta. Join me for my next live video in the app. A Brief Summary of Our Conservation We discussed the new understanding and approach to chronic pain, which affects nearly 1 in 4 adults. Dr. Gupta gets personal telling the story of his wife, Rebecca, who has an autoimmune disease and at one point he had to carry her up stairs. He also tells the story of his mother who had a back injury and didn’t want to live because of the pain. How his family members got relief is illuminating. Our whole understanding and approach to pain has changed, with the acronym change from RICE to MEAT. A newly approved drug Suzetrigine (Journavx) exploits the sodium channel gene mutation initially discovered via a family of fire walkers. It’s the first new pain medicine approved for more than 2 decades. Many other new non-opioid treatments are reviewed, no less lifestyle changes (anti-inflammatory diet and sleep), and acupuncture. Sanjay’s research over the past few years has led to a video special on CNN with the same title as the book, set to air 9 PM EST Sunday. If you know someone suffering chronic pain, please share the post. Get full access to Ground Truths at erictopol.substack.com/subscribe

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This is a hybrid heart disease risk factor post of a podcast with Prof Bruce Lanphear on lead and a piece I was asked to write for the Washington Post on risk factors for heart disease. First, the podcast. You may have thought the problem with lead exposure was circumscribed to children, but it’s a much bigger issue than that. I’ll concentrate on the exposure risk to adults in this interview, including the lead-estrogen hypothesis. Bruce has been working on the subject of lead exposure for more than 30 years. Let me emphasize that the problem is not going away, as highlighted in a recent New England Journal of Medicine piece on lead contamination in Milwaukee schools, “The Latest Episode in an Ongoing Toxic Pandemic.” Transcript with links to the audio and citations Eric Topol (00:05): Well, hello. This is Eric Topol with Ground Truths, and I'm very delighted to welcome Professor Bruce Lanphear from Simon Fraser University in British Columbia for a very interesting topic, and that's about lead exposure. We tend to think about lead poisoning with the Flint, Michigan, but there's a lot more to this story. So welcome, Bruce. Bruce Lanphear (00:32): Thank you, Eric. It's great to be here. Eric Topol (00:33): Yeah. So you had a New England Journal of Medicine (NEJM) Review in October last year, which was probably a wake up to me, and I'm sure to many others. We'll link to that, where you reviewed the whole topic, the title is called Lead Poisoning. But of course it's not just about a big dose, but rather chronic exposure. So maybe you could give us a bit of an overview of that review that you wrote for NEJM. Bruce Lanphear (01:05): Yeah, so we really focused on the things where we feel like there's a definitive link. Things like lead and diminished IQ in children, lead and coronary heart disease, lead and chronic renal disease. As you mentioned, we've typically thought of lead as sort of the overt lead poisoning where somebody becomes acutely ill. But over the past century what we've learned is that lead is one of those toxic chemicals where it's the chronic wear and tear on our bodies that catches up and it's at the root of many of these chronic diseases that are causing problems today. Eric Topol (01:43): Yeah, it's pretty striking. The one that grabbed me and kind of almost fell out of my chair was that in 2019 when I guess the most recent data there is 5.5 million cardiovascular deaths ascribed to relatively low levels, or I guess there is no safe level of lead exposure, that's really striking. That's a lot of people dying from something that cardiology and medical community is not really aware of. And there's a figure 3 [BELOW] that we will also show in the transcript, where you show the level where you start to see a takeoff. It starts very low and by 50 μg/liter, you're seeing a twofold risk and there's no threshold, it keeps going up. How many of us do you think are exposed to that type of level as adults, Bruce? Bruce Lanphear (02:39): Well, as adults, if we go back in time, all of us. If you go back to the 1970s when lead was still in gasoline, the median blood lead level of Americans was about 13 to 15 µg/dL. So we've all been exposed historically to those levels, and part of the reason we've begun to see a striking decline in coronary heart disease, which peaked in 1968. And by 1978, there was a 20% decline, 190,000 more people were alive than expected. So even in that first decade, there was this striking decline in coronary heart disease. And so, in addition to the prospective studies that have found this link between an increase in lead exposure and death from cardiovascular disease and more specifically coronary heart disease. We can look back in time and see how the decline in leaded gasoline led to a decline in heart disease and hypertension. Eric Topol (03:41): Yeah, but it looks like it's still a problem. And you have a phenomenal graph that's encouraging, where you see this 95% reduction in the lead exposure from the 1970s. And as you said, the factors that can be ascribed to like getting rid of lead from gasoline and others. But what is troubling is that we still have a lot of people that this could be a problem. Now, one of the things that was fascinating is that you get into that herbal supplements could be a risk factor. That we don't do screening, of course, should we do screening? And there's certain people that particularly that you consider at high risk that should get screened. So I wasn't aware, I mean the one type of supplements that you zoomed in on, how do you say it? Ayurvedic? Supplements With Lead Bruce Lanphear (04:39): Oh yeah. So this is Ayurvedic medicine and in fact, I just was on a Zoom call three weeks ago with a husband and wife who live in India. The young woman had taken Ayurvedic medicine and because of that, her blood lead levels increased to 70 µg/dL, and several months later she was pregnant, and she was trying to figure out what to do with this. Ayurvedic medicine is not well regulated. And so, that's one of the most important sources when we think about India, for example. And I think you pointed out a really important thing is number one, we don't know that there's any safe level even though blood lead levels in the United States and Europe, for example, have come down by over 95%. The levels that we're exposed to and especially the levels in our bones are 10 to 100 times higher than our pre-industrial ancestors. Bruce Lanphear (05:36): So we haven't yet reached those levels that our ancestors were exposed to. Are there effects at even lower and lower levels? Everything would suggest, we should assume that there is, but we don't know down below, let's say one microgram per deciliter or that's the equivalent of 10 parts per billion of lead and blood. What we also know though is when leaded gasoline was restricted in the United States and Canada and elsewhere, the companies turned to the industrializing countries and started to market it there. And so, we saw first the epidemic of coronary heart disease in the United States, Canada, Europe. Then that's come down over the past 50 years. At the same time, it was rising in low to middle income countries. So today over 95% of the burden of disease from lead including heart disease is found in industrializing countries. Eric Topol (06:34): Right. Now, it's pretty striking, of course. Is it true that airlines fuel is still with lead today? Bruce Lanphear (06:45): Well, not commercial airlines. It's going to be a small single piston aircraft. So for example, when we did a study down around the Santa Clara County Airport, Reid-Hillview, and we can see that the children who live within a half mile of the airport had blood lead levels about 10% higher than children that live further away. And the children who live downwind, 25% higher still. Now, nobody's mapped out the health effects, but one of the things that's particularly troubling about emissions from small aircraft is that the particle size of lead is extraordinarily small, and we know how nanoparticles because they have larger surface area can be more problematic. They also can probably go straight up into the brain or across the pulmonary tissues, and so those small particles we should be particularly worried about. But it's been such a long journey to try to figure out how to get that out of aircraft. It's a problem. The EPA recognized it. They said it's an endangerment, but the industry is still pushing back. Eric Topol (07:55): Yeah, I mean, it's interesting that we still have these problems, and I am going to in a minute ask you what we can do to just eradicate lead as much as possible, but we're not there yet. But one study that seemed to be hard to believe that you cited in the review. A year after a ban leaded fuel in NASCAR races, mortality from coronary heart disease declined significantly in communities near racetracks. Can you talk about that one because it's a little bit like the one you just mentioned with the airports? Bruce Lanphear (08:30): Yeah. Now that study particularly, this was by Alex Hollingsworth, was particularly looking at people over 65. And we're working on a follow-up study that will look at people below 65, but it was quite striking. When NASCAR took lead out of their fuel, he compared the rates of coronary heart disease of people that live nearby compared to a control group populations that live further away. And he did see a pretty striking reduction. One of the things we also want to look at in our follow-up is how quickly does that risk begin to taper off? That's going to be really important in terms of trying to develop a strategy around preventing lead poisoning. How quickly do we expect to see it fall? I think it's probably going to be within 12 to 24 months that we'll see benefits. Eric Topol (09:20): That's interesting because as you show in a really nice graphic in adults, which are the people who would be listening to this podcast. Of course, they ought to be concerned too about children and all and reproductive health. But the point about the skeleton, 95% of the lead is there and the main organs, which we haven't mentioned the kidney and the kidney injury that occurs no less the cardiovascular, the blood pressure elevation. So these are really, and you mentioned not necessarily highlighted in that graphic, but potential cognitive hit as well. You also wrote about how people who have symptoms of abdominal pain, memory impairment, and high blood pressure that's unexplained, maybe they should get a blood level screening. I assume those are easy to get, right? Bruce Lanphear (10:17): Oh yeah, absolutely. You can get those in any hospital, any clinic across the country. We're still struggling with having those available where it's most needed in the industrializing countries, but certainly available here. Now, we don't expect that for most people who have those symptoms, lead poisoning is going to be the ca

Eric Topol (00:06): Hello, this is Eric Topol from Ground Truths, and I'm delighted to welcome Owen Tripp, who is a CEO of Included Health. And Owen, I'd like to start off if you would, with the story from 2016, because really what I'm interested in is patients and how to get the right doctor. So can you tell us about when you lost your hearing in your right ear back, what, nine years ago or so? Owen Tripp (00:38): Yeah, it's amazing to say nine years, Eric, but obviously as your listeners will soon understand a pretty vivid memory in my past. So I had been working as I do and noticed a loss of hearing in my right ear. I had never experienced any hearing loss before, and I went twice actually to a sort of national primary care chain that now owned by Amazon actually. And they described it as eustachian tube dysfunction, which is a pretty benign common thing that basically meant that my tubes were blocked and that I needed to have some drainage. They recommended Sudafed to no effect. And it was only a couple weeks later where I was walking some of the senior medical team at my company down to the San Francisco Giants game. And I was describing this experience of hearing loss and I said I was also losing a little bit of sensation in the right side of my face. And they said, that is not eustachian tube dysfunction. And well, I can let the story unfold from there. But basically my colleagues helped me quickly put together a plan to get this properly diagnosed and treated. The underlying condition is called vestibular schwannoma, even more commonly known as an acoustic neuroma. So a pretty rare benign brain tumor that exists on the vestibular nerve, and it would've cost my life had it not been treated. Eric Topol (02:28): So from what I gather, you saw an ENT physician, but that ENT physician was not really well versed in this condition, which is I guess a bit surprising. And then eventually you got to the right ENT physician in San Francisco. Is that right? Owen Tripp (02:49): Well, the first doctor was probably an internal medicine doctor, and I think it's fair to say that he had probably not seen many, if any cases. By the time I reached an ENT, they were interested in working me up for what's known as sudden sensorineural hearing loss (SSHL), which is basically a fancy term for you lose hearing for a variety of possible pathologies and reasons, but you go through a process of differential diagnosis to understand what's actually going on. By the time that I reached that ENT, the audio tests had showed that I had significant hearing loss in my right ear. And what an MRI would confirm was this mass that I just described to you, which was quite large. It was already about a centimeter large and growing into the inner ear canal. Eric Topol (03:49): Yeah, so I read that your Stanford brain scan suggested it was about size of a plum and that you then got the call that you had this mass in your brainstem tumor. So obviously that's a delicate operation to undergo. So the first thing was getting a diagnosis and then the next thing was getting the right surgeon to work on your brain to resect this. So how did you figure out who was the right person? Because there's only a few thousand of these operations done every year, as I understand. Owen Tripp (04:27): That's exactly right. Yeah, very few. And without putting your listeners to sleep too early in our discussion, what I'll say is that there are a lot of ways that you can actually do this. There are very few cases, any approach really requires either shrinking or removing that tumor entirely. My size of tumor meant it was really only going to be a surgical approach, and there I had to decide amongst multiple potential approaches. And this is what's interesting, Eric, you started saying you wanted to talk about the patient experience. You have to understand that I'm somebody, while not a doctor, I lead a very large healthcare company. We provide millions of visits and services per year on very complex medical diagnoses down to more standard day-to-day fare. And so, being in the world of medical complexity was not daunting on the basics, but then I'm the patient and now I have to make a surgical treatment decision amongst many possible choices, and I was able to get multiple opinions. Owen Tripp (05:42): I got an opinion from the House clinic, which is closer to you in LA. This is really the place where they invented the surgical approach to treating these things. I also got an approach shared with me from the Mayo Clinic and one from UCSF and one from Stanford, and ultimately, I picked the Stanford team. And these are fascinating and delicate structures as you know that you're dealing with in the brain, but the surgery is a long surgery performed by multiple surgeons. It's such an exhausting surgery that as you're sort of peeling away that tumor that you need relief. And so, after a 13 hour surgery, multiple nights in the hospital and some significant training to learn how to walk and move and not lose my balance, I am as you see me today, but it was possible under one of the surgical approaches that I would've lost the use of the right side of my face, which obviously was not an option given what I given what I do. Eric Topol (06:51): Yeah, well, I know there had to be a tough rehab and so glad that you recovered well, and I guess you still don't have hearing in that one ear, right? Owen Tripp: That's right. Eric Topol: But otherwise, you're walking well, and you've completely recovered from what could have been a very disastrous type of, not just the tumor itself, but also the way it would be operated on. 13 hours is a long time to be in the operating room as a patient. Owen Tripp (07:22): You've got a whole team in there. You've got people testing nerve function, you've got people obviously managing the anesthesiology, which is sufficiently complex given what's involved. You've got a specialized ENT called a neurotologist. You've got the neurosurgeon who creates access. So it's quite a team that does these things. Eric Topol (07:40): Yeah, wow. Now, the reason I wanted to delve into this from your past is because I get a call or email or whatever contact every week at least one, is can you help me find the right doctor for such and such? And this has been going on throughout my career. I mean, when I was back in 20 years ago at Cleveland Clinic, the people on the board, I said, well, I wrote about it in one of my books. Why did you become a trustee on the board? And he said, so I could get access to the right doctor. And so, this is amazing. We live in an information era supposedly where people can get information about this being the most precious part, which is they want to get the right diagnosis, they want to get the right treatment or prevention, whatever, and they can't get it. And I'm finding this just extraordinary given that we can do deep research through several different AI models and get reports generated on whatever you want, but you can't get the right doctor. So now let's go over to what you're working on. This company Included Health. When did you start that? Owen Tripp (08:59): Well, I started the company that was known as Grand Rounds in 2011. And Grand Rounds still to this day, we've rebranded as Included Health had a very simple but powerful idea, one you just obliquely referred to, which is if we get people to higher quality medicine by helping them find the right level and quality of care, that two good things would happen. One, the sort of obvious one, patients would get better, they'd move on with their lives, they'd return to health. But two and critically that we would actually help the system overall with the cost burden of unnecessary, inappropriate and low quality care because the coda to the example you gave of people calling you looking for a physician referral, and you and I both know this, my guess is you've probably had to clean plenty of it up in your career is if you go to the wrong doctor, you don't get out of the problem. The problem just persists. And that patient is likely to bounce around like a ping pong ball until they find what they actually need. And that costs the payers of healthcare in this country a lot of money. So I started the company in 2011 to try to solve that problem. Eric Topol (10:14): Yeah, one example, a patient of mine who I've looked after for some 35 years contacted me and said, a very close friend of mine lives in the Palm Springs region and he has this horrible skin condition and he's tortured and he's been to six centers, UCSF, Stanford, Oregon Health Science, Eisenhower, UCLA, and he had a full workup and he can't sleep because he's itching all the time. His whole skin is exfoliating and cellulitis and he had biopsies everywhere. He’s put on all kinds of drugs, monoclonal antibodies. And I said to this patient of mine I said, I don't know, this is way out of my area. I checked at Scripps and turns out there was this kind of the Columbo of dermatology, he can solve any mystery. And the patient went to see him, and he was diagnosed within about a minute that he had scabies, and he was treated and completely recovered after having thousands and thousands of dollars of all these workups at these leading medical centers that you would expect could make a diagnosis of scabies. Owen Tripp (11:38): That’s a pretty common diagnosis. Eric Topol (11:40): Yeah. I mean you might expect it more in somebody who was homeless perhaps, but that doesn't mean it can't happen in anyone. And within the first few minutes he did a scrape and showed the patient under the microscope and made a definitive diagnosis and the patient to this day is still trying to pay all his bills for all these biopsies and drugs and whatnot, and very upset that he went through all this for over a year and he thought he wanted to die, it was so bad. Now, I had never heard of Included Health and you have now links with a third of the Fo

Eric Topol (00:05): Hello, it's Eric Topol from Ground Truths, and I've got some really exciting stuff to talk to you about today. And it's about the announcement for a new Center for pediatric CRISPR Cures. And I'm delight to introduce doctors Jennifer Doudna and Priscilla Chan. And so, first let me say this is amazing to see this thing going forward. It's an outgrowth of a New England Journal paper and monumental report on CRISPR in May. [See the below post for more context] Let me introduce first, Dr. Doudna. Jennifer is the Li Ka Shing Chancellor's Chair and a Professor in the departments of chemistry and of molecular and cell biology at the University of California Berkeley. She's also the subject of this book, one of my favorite books of all time, the Code Breaker. And as you know, the 2020 Nobel Prize laureate for her work in CRISPR-Cas9 genome editing, and she founded the Innovative Genomics Institute (IGI) back 10 years ago. So Jennifer, welcome. Jennifer Doudna (01:08): Thank you, Eric. Great to be here. Eric Topol (01:10): And now Dr. Priscilla Chan, who is the co-founder of the Chan Zuckerberg Initiative (CZI) that also was started back in 2015. So here we are, a decade later, these two leaders. She is a pediatrician having trained at UCSF and is committed to the initiative which has as its mission statement, “to make it possible to cure, prevent, and manage all diseases in this century.” So today we're going to talk about a step closer to that. Welcome, Priscilla. Priscilla Chan (01:44): Thank you. Thanks for having me. Eric Topol (01:46): Alright, so I thought we'd start off by, how did you two get together? Have you known each other for over this past decade since you both got all your things going? Jennifer Doudna (01:56): Yes, we have. We've known each other for a while. And of course, I've admired the progress at the CZI on fundamental science. I was an advisor very early on and I think actually that's how we got to know each other. Right, Priscilla? Priscilla Chan (02:11): Yeah, that's right. We got to know each other then. And we've been crisscrossing paths. And I personally remember the day you won the Nobel Prize. It was in the heart of the pandemic and a lot of celebrations were happening over Zoom. And I grabbed my then 5-year-old and got onto the UCSF celebration and I was like, look, this is happening. And it was really cool for me and for my daughter. Eric Topol (02:46): Well, it's pretty remarkable convergence leading up to today's announcement, but I know Priscilla, that you've been active in this rare disease space, you've had at CZI a Rare As One Project. Maybe you could tell us a bit about that. Priscilla Chan (03:01): Yeah, so at CZI, we work on basic science research, and I think that often surprises people because they know that I'm a pediatrician. And so, they often think, oh, you must work in healthcare or healthcare delivery. And we've actually chosen very intentionally to work in basic science research. In part because my training as a pediatrician at UCSF. As you both know, UCSF is a tertiary coronary care center where we see very unusual and rare cases of pediatric presentations. And it was there where I learned how little we knew about rare diseases and diseases in general and how powerful patients were. And that research was the pipeline for hope and for new discoveries for these families that often otherwise don't have very much access to treatments or cures. They have a PDF that maybe describes what their child has. And so, I decided to invest in basic science through CZI, but always saw the power of bringing rare disease patient cohorts. One, because if you've ever met a parent of a child with rare disease, they are a force to be reckoned with. Two, they can make research so much better due to their insights as patients and patient advocates. And I think they close the distance between basic science and impact in patients. And so, we've been working on that since 2019 and has been a passion of ours. Eric Topol (04:40): Wow, that's great. Now Jennifer, this IGI that you founded a decade ago, it's doing all kinds of things that are even well beyond rare diseases. We recently spoke, I know on Ground Truths about things as diverse as editing the gut microbiome in asthma and potentially someday Alzheimer’s. But here you were very much involved at IGI with the baby KJ Muldoon. Maybe you could take us through this because this is such an extraordinary advance in the whole CRISPR Cures story. Jennifer Doudna (05:18): Yes, Eric. It's a very exciting story and we're very, very proud of the teamwork that went into making it possible to cure baby KJ of his very rare disease. And in brief, the story began back in August of last year when he was born with a metabolic disorder that prevented him from digesting protein, it's called a urea cycle disorder and rare, but extremely severe. And to the point where he was in the ICU and facing a very, very difficult prognosis. And so, fortunately his clinical team at Children's Hospital of Philadelphia (CHOP) reached out to Fyodor Urnov, who is the Director of Translational Medicine at the IGI here in the Bay Area. They teamed up and realized that they could quickly diagnose that child because we had an IRB approved here at the IGI that allowed us to collect patient samples and do diagnosis. So that was done. Jennifer Doudna (06:26): We created an off-the-shelf CRISPR therapy that would be targeted to the exact mutation that caused that young boy's disease. And then we worked with the FDA in Washington to make sure that we could very safely proceed with testing of that therapy initially in the lab and then ultimately in two different animal models. And then we opened a clinical trial that allowed that boy to be enrolled with, of course his parents' approval and for him to be dosed and the result was spectacular. And in fact, he was released from the hospital recently as a happy, healthy child, gaining lots of weight and looking very chunky. So it's really exciting. Eric Topol (07:16): It's so amazing. I don't think people necessarily grasp this. This timeline [see above] that we'll post with this is just mind boggling how you could, as you said Jennifer, in about six months to go from the birth and sequencing through cell specific cultures with the genome mutations through multiple experimental models with non-human primates even, looking at off-target effects, through the multiple FDA reviews and then dosing, cumulatively three dosing to save this baby's life. It really just amazing. Now that is a template. And before we go to this new Center, I just wanted to also mention not just the timeline of compression, which is unimaginable and the partnership that you've had at IGI with I guess Danaher to help manufacture, which is just another part of the story. But also the fact that you're not just even with CRISPR 1.0 as being used in approvals previously for sickle cell and β-thalassemia, but now we're talking about base editing in vivo in the body using mRNA delivery. So maybe you could comment on that, Jennifer. Jennifer Doudna (08:38): Yeah, very good point. So yeah, we used a version of CRISPR that was created by David Liu at the Broad Institute and published and available. And so, it was possible to create that, again, targeted to the exact mutation that caused baby KJ’s disease. And fortunately, there was also an off-the-shelf way to deliver it because we had access to lipid nanoparticles that were developed for other purposes including vaccinations. And the type of disease that KJ suffered from is one that is treatable by editing cells in the liver, which is where the lipid nanoparticle naturally goes. So there were definitely some serendipity here, but it was amazing how all of these pieces were available. We just had to pull them together to create this therapy. Eric Topol (09:30): Yeah, no, it is amazing. So that I think is a great substrate for starting a new Center. And so, maybe back to you Priscilla, as to what your vision was when working with Jennifer and IGI to go through with this. Priscilla Chan (09:45): I think the thing that's incredibly exciting, you mentioned that at CZI our mission is to cure, prevent, and manage all disease. And when we talked about this 10 years ago, it felt like this far off idea, but every day it seems closer and closer. And I think the part that's super exciting about this is the direct connection between the basic science that's happening in CRISPR and the molecular and down to the nucleotide understanding of these mutations and the ability to correct them. And I think many of us, our imaginations have included this possibility, but it's very exciting that it has happened with baby KJ and CHOP. And we need to be able to do the work to understand how we can treat more patients this way, how to understand the obstacles, unblock them, streamline the process, bring down the cost, so that we better understand this pathway for treatment, as well as to increasingly democratize access to this type of platform. And so, our hope is to be able to do that. Take the work and inspiration that IGI and the team at CHOP have done and continue to push forward and to look at more cases, look at more organ systems. We're going to be looking in addition to the liver, at the bone marrow and the immune system. Priscilla Chan (11:17): And to be able to really work through more of the steps so that we can bring this to more families and patients. Eric Topol (11:30): Yeah, well it's pretty remarkable because here you have incurable ultra-rare diseases. If you can help these babies, just think of what this could do in a much broader context. I mean there a lot of common diseases have their roots with some of these very rare ones. So how do you see going forward, Jennifer, as to where you UC Berkeley, Gladstone, UCSF. I'm envious of you all up there in Northern California I have to say, will pull this off.

“To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.”—Adam Kucharski My conversation with Professor Kucharski on what constitutes certainty and proof in science (and other domains), with emphasis on many of the learnings from Covid. Given the politicization of science and A.I.’s deepfakes and power for blurring of truth, it’s hard to think of a topic more important right now. Audio file (Ground Truths can also be downloaded on Apple Podcasts and Spotify) Eric Topol (00:06): Hello, it's Eric Topol from Ground Truths and I am really delighted to welcome Adam Kucharski, who is the author of a new book, Proof: The Art and Science of Certainty. He’s a distinguished mathematician, by the way, the first mathematician we've had on Ground Truths and a person who I had the real privilege of getting to know a bit through the Covid pandemic. So welcome, Adam. Adam Kucharski (00:28): Thanks for having me. Eric Topol (00:30): Yeah, I mean, I think just to let everybody know, you're a Professor at London School of Hygiene and Tropical Medicine and also noteworthy you won the Adams Prize, which is one of the most impressive recognitions in the field of mathematics. This is the book, it's a winner, Proof and there's so much to talk about. So Adam, maybe what I'd start off is the quote in the book that captivates in the beginning, “life is full of situations that can reveal remarkably large gaps in our understanding of what is true and why it's true. This is a book about those gaps.” So what was the motivation when you undertook this very big endeavor? Adam Kucharski (01:17): I think a lot of it comes to the work I do at my day job where we have to deal with a lot of evidence under pressure, particularly if you work in outbreaks or emerging health concerns. And often it really pushes the limits, our methodology and how we converge on what's true subject to potential revision in the future. I think particularly having a background in math’s, I think you kind of grow up with this idea that you can get to these concrete, almost immovable truths and then even just looking through the history, realizing that often isn't the case, that there's these kind of very human dynamics that play out around them. And it's something I think that everyone in science can reflect on that sometimes what convinces us doesn't convince other people, and particularly when you have that kind of urgency of time pressure, working out how to navigate that. Eric Topol (02:05): Yeah. Well, I mean I think these times of course have really gotten us to appreciate, particularly during Covid, the importance of understanding uncertainty. And I think one of the ways that we can dispel what people assume they know is the famous Monty Hall, which you get into a bit in the book. So I think everybody here is familiar with that show, Let's Make a Deal and maybe you can just take us through what happens with one of the doors are unveiled and how that changes the mathematics. Adam Kucharski (02:50): Yeah, sure. So I think it is a problem that's been around for a while and it's based on this game show. So you've got three doors that are closed. Behind two of the doors there is a goat and behind one of the doors is a luxury car. So obviously, you want to win the car. The host asks you to pick a door, so you point to one, maybe door number two, then the host who knows what's behind the doors opens another door to reveal a goat and then ask you, do you want to change your mind? Do you want to switch doors? And a lot of the, I think intuition people have, and certainly when I first came across this problem many years ago is well, you've got two doors left, right? You've picked one, there's another one, it's 50-50. And even some quite well-respected mathematicians. Adam Kucharski (03:27): People like Paul Erdős who was really published more papers than almost anyone else, that was their initial gut reaction. But if you work through all of the combinations, if you pick this door and then the host does this, and you switch or not switch and work through all of those options. You actually double your chances if you switch versus sticking with the door. So something that's counterintuitive, but I think one of the things that really struck me and even over the years trying to explain it is convincing myself of the answer, which was when I first came across it as a teenager, I did quite quickly is very different to convincing someone else. And even actually Paul Erdős, one of his colleagues showed him what I call proof by exhaustion. So go through every combination and that didn't really convince him. So then he started to simulate and said, well, let's do a computer simulation of the game a hundred thousand times. And again, switching was this optimal strategy, but Erdős wasn't really convinced because I accept that this is the case, but I'm not really satisfied with it. And I think that encapsulates for a lot of people, their experience of proof and evidence. It's a fact and you have to take it as given, but there's actually quite a big bridge often to really understanding why it's true and feeling convinced by it. Eric Topol (04:41): Yeah, I think it's a fabulous example because I think everyone would naturally assume it's 50-50 and it isn't. And I think that gets us to the topic at hand. What I love, there's many things I love about this book. One is that you don't just get into science and medicine, but you cut across all the domains, law, mathematics, AI. So it's a very comprehensive sweep of everything about proof and truth, and it couldn't come at a better time as we'll get into. Maybe just starting off with math, the term I love mathematical monsters. Can you tell us a little bit more about that? Adam Kucharski (05:25): Yeah, this was a fascinating situation that emerged in the late 19th century where a lot of math’s, certainly in Europe had been derived from geometry because a lot of the ancient Greek influence on how we shaped things and then Newton and his work on rates of change and calculus, it was really the natural world that provided a lot of inspiration, these kind of tangible objects, tangible movements. And as mathematicians started to build out the theory around rates of change and how we tackle these kinds of situations, they sometimes took that intuition a bit too seriously. And there was some theorems that they said were intuitively obvious, some of these French mathematicians. And so, one for example is this idea of you how things change smoothly over time and how you do those calculations. But what happened was some mathematicians came along and showed that when you have things that can be infinitely small, that intuition didn't necessarily hold in the same way. Adam Kucharski (06:26): And they came up with these examples that broke a lot of these theorems and a lot of the establishments at the time called these things monsters. They called them these aberrations against common sense and this idea that if Newton had known about them, he never would've done all of his discovery because they're just nuisances and we just need to get rid of them. And there's this real tension at the core of mathematics in the late 1800s where some people just wanted to disregard this and say, look, it works for most of the time, that's good enough. And then others really weren't happy with this quite vague logic. They wanted to put it on much sturdier ground. And what was remarkable actually is if you trace this then into the 20th century, a lot of these monsters and these particularly in some cases functions which could almost move constantly, this constant motion rather than our intuitive concept of movement as something that's smooth, if you drop an apple, it accelerates at a very smooth rate, would become foundational in our understanding of things like probability, Einstein's work on atomic theory. A lot of these concepts where geometry breaks down would be really important in relativity. So actually, these things that we thought were monsters actually were all around us all the time, and science couldn't advance without them. So I think it's just this remarkable example of this tension within a field that supposedly concrete and the things that were going to be shunned actually turn out to be quite important. Eric Topol (07:53): It's great how you convey how nature isn't so neat and tidy and things like Brownian motion, understanding that, I mean, just so many things that I think fit into that general category. In the legal, we won't get into too much because that's not so much the audience of Ground Truths, but the classic things about innocent and until proven guilty and proof beyond reasonable doubt, I mean these are obviously really important parts of that overall sense of proof and truth. We're going to get into one thing I'm fascinated about related to that subsequently and then in science. So before we get into the different types of proof, obviously the pandemic is still fresh in our minds and we're an endemic with Covid now, and there are so many things we got wrong along the way of uncertainty and didn't convey that science isn't always evolving search for what is the truth. There's plenty no shortage of uncertainty at any moment. So can you recap some of the, you did so much work during the pandemic and obviously some of it's in the book. What were some of the major things that you took out of proof and truth from the pandemic? Adam Kucharski (09:14): I think it was almost this story of two hearts because on the one hand, scie

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My conversation with Matthew Walker, PhD on faculty at UC Berkeley where he is a professor of neuroscience and psychology, the founder and director of the Center for Human Sleep Science, and has a long history of seminal contributions on sleep science and health. Audio File (also downloadable at Apple Podcast and Spotify) “Sleep is a non-negotiable biological state required for the maintenance of human life . . . our needs for sleep parallel those for air, food, and water.”—Grandner and Fernandez Eric Topol (00:07): Hello, it's Eric Topol with Ground Truths, and I am really delighted to welcome Matt Walker, who I believe has had more impact on sleep health than anyone I know. It's reflected by the fact that he is a Professor at UC Berkeley, heads up the center that he originated for Human Sleep Science. He wrote a remarkable book back in 2017, Why We Sleep, and also we'll link to that as well as the TED Talk of 2019. Sleep is Your Superpower with 24 million views. That's a lot of views here. Matt Walker: Striking, isn't it? Eric Topol: Wow. I think does reflect the kind of impact, you were onto the sleep story sooner, earlier than anyone I know. And what I wanted to do today was get to the updates because you taught us a lot back then and a lot of things have been happening in these years since. You're on it, of course, I think you have a podcast Sleep Diplomat, and you're obviously continued working on the science of sleep. But maybe the first thing I'd ask you about is in the last few years, what do you think has been, are there been any real changes or breakthroughs in the field? What Is New? Matt Walker (01:27): Yeah, I think there has been changes, and maybe we'll speak about one of them, which is the emergence of this brain cleansing system called the glymphatic system, but spreading that aside for potential future discussion. I would say that there are maybe at least two fascinating areas. The first is the broader impact of sleep on much more complex human social interactions. We think of sleep at maybe the level of the cell or systems or whole scale biology or even the entire organism. We forget that a lack of sleep, or at least the evidence suggests a lack of sleep will dislocate each other, one from the other. And there's been some great work by Dr. Eti Ben Simon for example, demonstrating that when you are sleep deprived, you become more asocial. So you basically become socially repellent. You want to withdraw, you become lonely. And what's also fascinating is that other people, even they don't know that you sleep deprived, they rate you as being less socially sort of attractive to engage with. Matt Walker (02:35): And after interacting with you, the sleep deprived individual, even though they don't know you're sleep deprived, they themselves walk away feeling more lonely themselves. So there is a social loneliness contagion that happens that a sleep deprived lonely individual can have almost a viral knock on effect that causes loneliness in another well-rested individual. And then that work spanned out and it started to demonstrate that another impact of a lack of sleep socially is that we stop wanting to help other people. And you think, well, helping behavior that's not really very impactful. Try to tell me of any major civilization that has not risen up through human cooperation and helping. There just isn't one. Human cooperative behavior is one of our innate traits as homo sapiens. And what they discovered is that when you are insufficiently slept, firstly, you don't wish to help other people. And you can see that at the individual level. Matt Walker (03:41): You can see it in groups. And then there was a great study again by Dr. Eti Ben Simon that demonstrated this at a national level because what she did was she looked at this wonderful manipulation of one hour of sleep that happens twice a year to 1.6 billion people. It's called daylight savings time at spring. Yeah, when you lose one hour of sleep opportunity. She looked at donations across the nation and sure enough, there was this big dent in donation giving in the sleepy Monday and Tuesday after the clock change. Because of that sleep, we become less willing to empathetically and selflessly help other individuals. And so, to me I think it's just a fascinating area. And then the other area I think is great, and I'm sorry I'm racing forward because I get so excited. But this work now looking at what we call genetic short sleepers and sort of idiots like me have been out there touting the importance of somewhere between seven to nine hours of sleep. Matt Walker (04:48): And once you get less than that, and we'll perhaps speak about that, you can see biological changes. But there is a subset of individuals who, and we've identified at least two different genes. One of them is what we call the DEC2 gene. And it seems to allow individuals to sleep about five hours, maybe even a little bit less and show no impairment whatsoever. Now we haven't tracked these individuals across the lifespan to truly understand does it lead to a higher mortality risk. But so far, they don't implode like you perhaps or I would do when you are limited to this anemic diet of five hours of sleep. They hang in there just fine. And I think philosophically what that tells me, and by the way, for people who are listening thinking, gosh, I think I'm probably one of those people. Statistically, I think you are more likely to be struck by lightning in your lifetime than you are to have the DEC2 gene. Think about what tells us, Eric. It tells us that there is a moment in biology in the evolution of this thing called the sleep physiological need that has changed such that mother nature has found a genetic way to ZIP file sleep. Matt Walker (06:14): You can essentially compress sleep from seven to nine hour need, down to five to six hour need. To me, that is absolutely fascinating. So now the race is on, what are the mechanisms that control this? How do we understand them? I'm sure much to my chagrin, society would like to then say, okay, is there a pill that I can take to basically ZIP file my own sleep and then it becomes an arms race in my mind, which is then all of a sudden six hours becomes the new eight hours and then everyone is saying, well, six hours is my need. Well I'll go to four hours and then it's this arms race of de-escalation of sleep. Anyway, I'm going on and on, does that help give you a sense of two of the what I feel the more fascinating areas? Eric Topol (07:01): Absolutely. When I saw the other recent report on the short sleep gene variant and thought about what the potential of that would be with respect to potential drug development or could you imagine genome editing early in life that you don't need any sleep? I mean crazy stuff. Matt Walker (07:19): It was amazing. Glymphatics and Deep Sleep for more, see previous Ground Truths on this topic Eric Topol (07:22): No, the mechanism of course we have to work out and also what you mentioned regarding the social and the behavior engagement, all that sort of thing, it was just fascinating stuff. Now we touched on one thing early on to come back to the glymphatics these channels to get rid of the waste metabolites from the brain each night that might be considered toxic metabolites. We've learned a lot about those and of course there's some controversy about it. What are your thoughts? Matt Walker (07:55): Yeah, I think there's really quite comprehensive evidence suggesting that the brain has this cleansing system like the body has one the lymphatic system, the brain has one the glymphatic system named after these glial cells that make it up. And I think there's been evidence from multiple groups across multiple different species types, from mouse models all the way up to human models suggesting that there is a state dependent control of the brain cleansing system, which is a fancy way of saying if you are awake in light NREM, deep NREM or perhaps you're just quiet and you are resting in your wakefulness, the glymphatic system is not switched on at the same rate across all of those different brain states. And I think the overwhelming evidence so far using different techniques in different species from different groups is that sleep is a preferential time. It's not an exclusive time, it's a preferential time when that brain cleansing system kicks into gear because as some people have, I think argued, and you could say it's hyperbolic, but wakefulness is low level from a biochemicals perspective, it's low level brain damage and sleep is therefore your sanitary salvation that combat that biochemical cascade. Matt Walker (09:15): So in other words, a better way of putting it would be, sleep is the price that you pay for wakefulness in some ways. And I think there was a recent controversial study that came out in 2022 or 2023, and they actually suggested quite the opposite. They said using their specific imaging methods, they found that the sort of clearance, the amount of cerebral spinal fluid, which is what washes through the brain to cleanse the toxins, the rate of that flow of cerebral spinal fluid was highest during wakefulness and lowest during deep NREM sleep, the exact opposite of what others have found. Now, I think the defendants of the glymphatic sleep dependent hypothesis pushed back and said, well, if you look at the imaging methods. Firstly, they’re nonstandard. Secondly, they were measuring the cerebral spinal flow in an artificial way because they were actually perfusing solutions through the brain rather than naturally letting it flow and therefore the artificial forcing of fluid changed the prototypical result you would get. Matt Walker (10:27): And they also argued that the essentially kind of the sampling rate, so how quickly are you taking snapshots of the cerebral spinal fluid flow. Those were different and they were probably missing some of the sleep dependent slow osc

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Audio File Ground Truths can also be found on Apple Podcasts, Spotify and YouTube. The UK is the world leader in human genomics, and laid the foundation for advancing medicine with the UK Biobank, Genomes England and now Our Future Health (w/ 5 million participants). Sir John Bell is a major force in driving and advising these and many other initiatives. After 22 years as the Regius Professor of Medicine at the University of Oxford he left in 2024 to be President of the Ellison Institute of Technology. Professor Bell has been duly recognized in the UK: knighted in 2015 and appointed Companion of Honor in 2023. In our conversation, you will get a sense for how EIT will be transformational for using A.I. and life science for promoting human health. Transcript with audio links Eric Topol (00:06): Hello, this is Eric Topol from Ground Truths. And I'm really delighted to welcome today, Sir John Bell who had an extraordinary career as a geneticist, immunologist, we'll talk about several initiatives he's been involved with during his long tenure at University of Oxford, recently became head of the Ellison Institute of Technology (EIT) in the UK. So welcome, John. Sir John Bell (00:30): Thanks, Eric. Thanks very much for having me. Eric Topol (00:34): Well, I think it's just extraordinary the contributions that you have made and continue to make to advance medicine, and I thought what we could do is get into that. I mean, what's interesting, you have had some notable migrations over your career, I think starting in Canada, at Stanford, then over as Rhodes Scholar in Oxford. And then you of course had a couple of decades in a very prestigious position, which as I understand was started in 1546 by King Henry VII, and served as the Regius Professor of Medicine at the University of Oxford. Do I have that right? Sir John Bell (01:11): It was actually Henry VIII, but you were close. Eric Topol (01:14): Henry VIII, that's great. Yeah. Okay, good. Well, that's a pretty notable professorship. And then of course in recent times you left to head up this pretty formidable new institute, which is something that's a big trend going on around the world, particularly in the US and we'll talk about. So maybe we can start with the new thing. Tell us more about the Ellison Institute of Technology (EIT), if you will. Sir John Bell (01:47): Yeah. So as you know, Larry Ellison has been one of the great tech entrepreneurs focused really on developing terrific databases over his career and through Oracle, which is the company that he founded. And Larry is really keen to try and give back something substantial to the world, which is based on science and technology. So he and I did quite a bit together over the Covid pandemic. He and I talked a lot about what we're doing and so on. He came to visit afterwards and he had, I think he decided that the right way to make his contributions would be to set up an institute that would be using the state-of-the-art science and technology with a lot of AI and machine learning, but also some of the other modern tools to address the major problems in healthcare, in food security, in green energy and climate change and in global governance. Sir John Bell (02:49): So anyway, he launched this about 18 months ago. He approached me to ask whether I would run it. He wanted to set it up outside Oxford, and he wanted to do something which is a bit different than others. And that is his view was that we needed to try and create solutions to these problems which are commercially viable and not all the solutions are going to be commercially viable, but where you can create those, you make them sustainable. So the idea is to make sure that we create solutions that people want to buy, and then if they buy them, you can create a sustainable solution to those issues. So we are actually a company, but we are addressing many of the same problems that the big foundations are addressing. And the big issues that you and I talk about in health, for example, are all on our list. So we're kind of optimistic as to where this will go and Larry's supporting the project and we're going to build out an institute here which will have about 5,000 people in it, and we'll be, I think a pretty exciting new addition to the science and technology ecosystem globally. Eric Topol (04:02): Well, I know the reverberations and the excitement is palpable and some of the colleagues I've spoken to, not just in England, but of course all over the world. So congratulations on that. It was a big move for you to leave the hardcore academics. And the other thing I wanted to ask you, John, is you had distinguished your career in immunology, in genetics, type 1 diabetes and other conditions, autoimmune conditions, and now you've really diversified, as you described with these different areas of emphasis at the new institute. Is that more fun to do it or do you have deputies that you can assign to things like climate change in other areas? Sir John Bell (04:50): Trust me, Eric, I'm not making any definitive decisions about areas I know nothing about, but part of this is about how do you set up leadership, run a team, get the right people in. And I have to say one of the really interesting things about the institute is we've been able to recruit some outstanding people across all those domains. And as you know, success is almost all dependent on people. So we're really pretty optimistic we're going to have a significant impact. And of course, we also want to take risks because not a lot of point in us doing stuff that everybody else is doing. So we're going to be doing some things that are pretty way out there and some of them will fail, so we are just going to get used to trying to make sure we get a few of them across the finish line. But the other thing is that, and you've experienced this too, you never get too old to learn. I mean, I'm sucking up stuff that I never thought I would ever learn about, which is fun actually, and really marvel. Eric Topol (05:55): It's fantastic. I mean, you've really broadened and it's great that you have the runway to get these people on board and I think you're having a big building that's under construction? Sir John Bell (06:07): Yeah, we've got the original building that Larry committed to is about 330,000 square feet of space. I mean, this is completely amazing, but we are of course to accommodate up to 5,000 people, we're going to need more than that. So we are looking at a much wider campus here that'll involve more than just that building. I think we'll end up with several million square feet of space by the time we're finished. So mean, it's a really big project, but we've already made progress in some domains to try and get projects and the beginnings of companies on the road to try and solve some of the big problems. So we're quite excited about it. Eric Topol (06:49): Now you, I assume it's pretty close to Oxford, and will you have some kind of inter interactions that are substantial? Sir John Bell (06:58): Yeah, so the university's been terrific about this actually, because of course most universities would say, well, why don't you do it inside the university and just give us the money and it'll all be fine. So of course Larry. Larry wasn't born yesterday, so I said, well, thank you very much, but I think we'll probably do this nearby. But the university also realized this is a really exciting opportunity for them and we've got a really good relationship with them. We've signed an agreement with them as to who will work where. We've agreed not to steal a lot of their staff. We're going to be bringing new people into the ecosystem. Some of the university people will spend some time with us and sometime in the university, so that will help. But we're also bringing quite a few new people into the setting. So the university has been really positive. And I think one of the things that's attractive to the university, and you'll be familiar with this problem in the UK, is that we're quite good. The discovery science here is pretty good. Sir John Bell (08:06): And we do startups now at scale. So Oxford does lots of little startup companies in the biotech space and all the rest of it, but we never scale any of these companies because there isn't the depth of capital for scaling capital to get these things scaled. And so, in a way what we're trying to do here at Ellison actually avoids that problem because Larry knows how to scale companies, and we've got the financial support now. If we have things that are really successful, we can build the full stack solution to some of these problems. So I think the university is really intrigued as to how we might do that. We're going to have to bring some people in that know how to do that and build billion dollar companies, but it's sufficiently attractive. We've already started to recruit some really outstanding people. So as a way to change the UK system broadly, it's actually quite a good disruptive influence on the way the thing works to try and fix some of the fundamental problems. Eric Topol (09:07): I love that model and the ability that you can go from small startups to really transformative companies have any impact. It fits in well with the overall objectives, I can see that. The thing that also is intriguing regarding this whole effort is that in parallel we've learned your influence. The UK is a genomics world leader without any question and no coincidence that that's been your area of emphasis in your career. So we've watched these three initiatives that I think you were involved in the UK Biobank, which has had more impact than any cohort ever assembled. Every day there's another paper using that data that's coming out. There's Genomes England, and then now Our Future Health, which a lot of people don't know about here, which is well into the 5 million people enrollment. Can you tell us about, this is now 15 years ago plus when these were started, and of co

Audio file, also on Apple and Spotify Tyler Cowen, Ph.D, is the Holbert L. Harris Professor of Economics at George Mason University. He is the author of 17 books, most recently Talent.: How to Identify Energizers, Creatives, and Winners Around the World. Tyler has been recognized as one of the most influential economists of the past decade. He initiated and directs the philanthropic project Emergent Ventures, writes a blog Marginal Revolution, and a podcast Conversations With Tyler, and also writes columns for The Free Press." He is writing a new book (and perhaps his last) on Mentors. “Maybe AGI [Artificial General Intelligence] is like porn — I know it when I see it. And I’ve seen it.”—Tyler Cowen Our conversation on acquiring information, A.I., A.G.I., the NIH, the assault on science, the role of doctors in the A.I. era,, the meaning of life, books of the future, and much more. Transcript with links Eric Topol (00:06): Well, hello. This is Eric Topol with Ground Truths, and I am really thrilled today to have the chance to have a conversation with Tyler Cowen, who is, when you look up polymath in the dictionary, you might see a picture of him. He is into everything. And recently in the Economist magazine 1843, John Phipps wrote a great piece profile, the man who wants to know everything. And actually, I think there's a lot to that. Tyler Cowen (00:36): That's why we need longevity work, right? Eric Topol (00:39): Right. So he's written a number of books. How many books now, Tyler? Tyler Cowen: 17, I'm not sure. Eric Topol: Only 17? And he also has a blog that's been going on for over 20 years, Marginal Revolution that he does with Alex Tabarrok. Tyler Cowen (00:57): Correct. Eric Topol (00:57): And yeah, and then Conversations with Tyler, a podcast, which I think an awful lot of people are tuned into that. So with that, I'm just thrilled to get a chance to talk with you because I used to think I read a lot, but then I learned about you. “Cowen calls himself “hyperlexic”. On a good day, he claims to read four or five books. Secretly, I timed him at 30 seconds per page reading a dense tract by Martin Luther. “—John Phipps, The Economist’s 1843 I've been reading more from the AIs lately and less from books. So I'll get one good book and ask the AI a lot of questions. Eric Topol (01:24): Yeah. Well, do you use NotebookLM for that? Tyler Cowen (01:28): No, just o3 from OpenAI at the moment, but a lot of the models are very good. Claude, there's others. Eric Topol (01:35): Yeah, yeah. No, I see how that's a whole different way to interrogate a book and it's great. And in fact, that gets me to a topic I was going to get to later, but I'll do it now. You're soon or you have already started writing for the Free Press with Barri Weiss. Tyler Cowen (01:54): That’s right, yes. I have a piece coming out later today. It's been about two weeks. It's been great so far. “Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James”—Bari Weiss Yeah, so that's interesting. I hadn't heard of it until I saw the announcement from Barri and I thought what was great about it is she introduced it. She said, “Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James. Now who could do that, right. So I thought, well, you know what? I need independent confirmation of that, that is as being a polymath. And then I saw Patrick Collison, who I know at Stripe and Arc Institute, “you can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.” So you're an information acquirer and one of the books you wrote, I love the title Infovore. Tyler Cowen (03:09): The Age of the Infovore, that’s right. Eric Topol (03:11): I mean, have people been using that term because you are emblematic of it? “You can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.”—Patrick Collison It was used on the internet at some obscure site, and I saw it and I fell in love with that word, and I thought I should try to popularize it, but it doesn't come from me, but I think I am the popularizer of it. Yeah, well, if anybody was ingesting more information and being able to work with it. That's what I didn't realize about you, Tyler, is restaurants and basketball and all these other fine arts, very impressive. Now, one of the topics I wanted to get into you is I guess related to a topic you've written about fair amount, which is the great stagnation, and right now we're seeing issues like an attack on science. And in the past, you've written about how you want to raise the social status of scientists. So how do you see this current, I would even characterize as a frontal assault on science? Tyler Cowen (04:16): Well, I'm very worried about current Trump administration policies. They change so frequently and so unpredictably, it's a little hard to even describe what they always are. So in that sense, it's a little hard to criticize them, but I think they're scaring away talent. They might scare away funding and especially the biomedical sciences, the fixed costs behind a lot of lab work, clinical trials, they're so high that if you scare money away, it does not come back very readily or very quickly. So I think the problem is biggest perhaps for a lot of the biomedical sciences. I do think a lot of reform there has been needed, and I hope somehow the Trump policies evolve to that sort of reform. So I think the NIH has become too high bound and far too conservative, and they take too long to give grants, and I don't like how the overhead system has been done. So there's plenty of room for improvement, but I don't see so far at least that the efforts have been constructive. They've been mostly destructive. Eric Topol (05:18): Yeah, I totally agree. Rather than creative destruction it’s just destruction and it's unfortunate because it seems to be haphazard and reckless to me at least. We of course, like so many institutions rely on NIH funding for the work, but I agree that reform is fine as long as it's done in a very thought out, careful way, so we can eke out the most productivity for the best investment. Now along with that, you started Emergent Ventures where you're funding young talent. Tyler Cowen (05:57): That's right. That's a philanthropic fund. And we now have slightly over 1000 winners. They're not all young, I'd say they're mostly young and a great number of them want to go into the biomedical sciences or have done so. And this is part of what made me realize what an incredible influx of talent we're seeing into those areas. I'm not sure this is widely appreciated by the world. I'm sure you see it. I also see how much of that talent actually is coming from Canada, from Ontario in particular, and I've just become far more optimistic about computational biology and progress in biology and medical cures, fixes, whatever you want to call it, extending lives. 10 years ago, I was like, yeah, who knows? A lot of things looked pretty stuck. Then we had a number of years where life expectancy was falling, and now I think we're on the verge of a true golden age. Eric Topol (06:52): I couldn't agree with you more on that. And I know some of the people that you funded like Anne Wylie who developed a saliva test for Covid out of Yale. But as you say, there's so many great young and maybe not so young scientists all over, Canada being one great reservoir. And now of course I'm worried that we're seeing emigration rather than more immigration of this talent. Any thoughts about that? Tyler Cowen (07:21): Well, the good news is this, I'm in contact with young people almost every day, often from other countries. They still want to come to the United States. I would say I sign an O-1 letter for someone about once a week, and at least not yet has the magic been dissipated. So I'm less pessimistic than some people are, but I absolutely do see the dangers. We’re just the biggest market, the freest place we have by far the most ambitious people. I think that's actually the most significant factor. And young people sense that, and they just want to come here and there's not really another place they can go that will fit them. Eric Topol (08:04): Yeah, I mean one of the things as you've probably noted is there's these new forces that are taking on big shouldering. In fact, Patrick Collison with Arc Institute and Chan Zuckerberg for their institute and others like that, where the work you're doing with Emergent Ventures, you're supporting important projects, talents, and if this whole freefall in NIH funding and other agency funding continues, it looks like we may have to rely more on that, especially if we're going to attract some talent from outside. I don't know how else we're going to make. You're absolutely right about how we are such a great destination and great collaborations and mentors and all that history, but I'm worried that it could be in kind of a threatened mode, if you will. Tyler Cowen (08:59): I hope AI lowers costs. As you probably know at Arc, they had Greg Brockman come in for some number of months and he's one of the people, well, he helped build up Stripe, but he also was highly significant in OpenAI behind the GPT-4 model. And to have Greg Brockman at your institute doing AI for what, six months, t

In our divided world we face or avoid conflicts on a frequent basis. I turned to Bob Bordone and Joel Salinas to find out the best strategies to deal with these, including having them take on a mock conflict between each other on the merits of Covid research. Audio file You can also find this on Spotify and Apple podcasts with Ground Truths. The video is also posted on YouTube Transcript with Audio Links Eric Topol (00:06): Well, hello. It's Eric Topol with Ground Truths, and we're going to get into a new book called Conflict Resilience: Negotiating Disagreement Without Giving Up or Giving In, and we're lucky to have its two authors, Bob Bordone, who is a Senior Fellow at Harvard Law School, and Joel Salinas, who is a physician, neurologist, a clinician scientist at NYU. So welcome both Bob and Joel. Bob Bordone and Joel Salinas (00:34): Thank you for having us. Yeah, looking forward to the conversation. Eric Topol (00:37): Yeah. So first, how did you guys get together? This is a pretty diverse, you got law and medicine, usually they don't talk to each other very much. Bob Bordone (00:46): Well, we were very fortunate. I mean, we basically were friends, but part of that friendship, I think emerged from work that I do around conflict issues in the Mass General system and then just the larger, bigger Mass General, Harvard community. Yeah, so this began really as a friendship where we were each swimming in very different waters, but then as we would start to talk, we realized there was a lot of connection and maybe the possibility to bring two different disciplines together in a way that might be practically useful and make an impact. And even when we started writing this, which was during Covid, what seemed to be some pretty polarizing times that were unlikely to resolve by the time the book would come out. Eric Topol (01:44): Yeah, well you sure hit it with the divisiveness and the polarized world that we live in is perhaps worse than ever, certainly in all my years, and probably long before then as well. So this topic of resilience, it's a very interesting concept because some people might think of resilience as just being tough. So go into a conflict and just go heavy tough. That obviously is not what you're writing about. And I guess maybe we can start off, what was the goal here? Obviously, there's other books that have addressed this topic, I'm sure, but yours is somewhat unique in many respects because it brings in the science of it and many strategies perhaps that have never been developed. But when you got together, what was the mission that you set out to do? Joel Salinas (02:38): Yeah, well maybe I can start out and then you can add on. So my research has been all around understanding how social relationships influenced brain health, and one of the things that I was seeing was social isolation and loneliness had been steadily increasing. Want to figure out what kind of interventions or what are the factors that are involved here? And I think one of the things that has stood out is just the difficulty with being able to navigate conflict in different contexts. And so, the idea around conflict resilience is really, even though there's been lots of books on what to say and what specific tactics to use, I think that there was this skillset around just being able to sit with the discomfort of that disagreement, which will ultimately help make it much more useful to take on those tactics. One way to think about it, if it's like all these tactics are like learning how to cook with a set of recipes in the kitchen, what we're really proposing here is that you also need to be able to stand the heat of the kitchen to even be able to cook. Eric Topol (03:47): Okay. Go ahead, Bob. Bob Bordone (03:49): Yeah, and I would say I was starting to write about my first kind of piece on this topic where I use the word conflict resilience was in 2018, and it really came from an observed dynamic that I was seeing in my teaching of Harvard Law School students. I was on the admissions committee, I'd been on the admissions committee for many years. I knew that we worked very hard and were quite successful in fact, at bringing together a very diverse student body, including politically. And people sometimes maybe think of elite law schools as being very progressive. But Harvard Law School, the biggest student organization is actually the Federalists, which is the conservative students. And despite that effort, what I noticed in the classroom was a reduction in conversation, diversity of viewpoint across the board, interesting classrooms became boring. And even though I was teaching around conflict and negotiation and difficult conversations, I would read in students' journals things like, I want to avoid conflict or I don't want to get into it. Bob Bordone (04:59): And so, it occurred to me that quite a part, as Joel said, from any skills, if we don't develop this capacity to sit with disagreement, then we will never get to problem solving. I'm in favor of problem solving. But this paper on conflict resilience, its original title was called Against Problem Solving. Mostly because I thought that if we had opened the possibility of problem solving as a precondition for entering the room, then we might never enter the room, particularly if we've told the demonized and dehumanized story about them. And so, that somehow we had to make the case that sitting with the discomfort of the disagreement, even if it didn't mean problem solving, although we hope for that, even if we didn't mean that it was worthwhile and it was important. And so, part of what was really attractive to me about joining up with Joel is that he just brought all of this brain science aspect to it that I had this kind of teaching and kind of academic in the negotiation and dispute resolution research experience, but couldn't bring to bear the kind of brain science parts of, well, what is going on in our brain when we do want to run or when we get into that really unproductive battle. Eric Topol (06:27): Yeah, I agree that the unique part here is that whole scaffolding with the neuroscience, the behavioral science, and those five Fs that you mentioned. You alluded to fight, flight, freeze, fawn, or fester. Yeah, so avoidance of conflict has kind of been the default for many people now because we have political divides, we have anti-science versus pro-science divides and on and on. There's a quote in the book that I thought we'd start off with because it really lays the groundwork from you both. “The biggest hidden barrier to being conflict resilient stems from the inability or unwillingness to face and sit with our own internal conflicts - the negotiations between our divided and sometimes contradictory “selves.” Even more surprising is that although there are dozens of self-help books on negotiation and conflict resolution, almost none of them spend any meaningful time on this critical intrapersonal barrier to handing conflict.” So maybe Joel, maybe start you off here. I guess you were bullied as a kid, and maybe that gives you a little background here. Joel, tell us about that if you would. Bob Bordone (07:46): Hey, Eric. On our bad days sometimes I probably inadvertently bully Joel still today, but he's pretty resilient now. Joel Salinas (07:53): Yeah, I'm a Teflon. So I think I am generally conflict of what an individual, and I think a lot of listeners and viewers can relate with that experience. And I think that also kind of speaks to some of the neuroscience that comes into this, which is that our brain has really evolved to be a fortune telling machine. It takes all of our past experiences, turns them into memories, and then makes projections about what's going to happen. And this projection or prediction of what's going to happen might as well be reality for our brain's sake. And so, if we had really negative experiences with conflict in the past growing up, whether through our families or the schoolyard or others, there'll be likely a very negative charge of negative emotional charge that comes with that. And what that does is that it increases the chances that you'll trigger this system for salience and arousal, which then sets off the alarms essentially in your body that then creates these fight or flight type responses where you're more likely to fall back on these really reflexive behaviors to make the bad thing less bad. Joel Salinas (09:08): And when you do that, whether it's through avoiding or to blowing through conflict like a battering ram that then trains your brain to assign some kind of a reward using the orbital frontal cortex, a system that kind of keeps tabs over how much reward you get for a behavior, it makes it much more likely that you'll do it again. And so, we from a very young age, develop a propensity to either avoid conflict or tackle it. And it varies depending on the context and how you're feeling, but it just makes it much, much harder to be able to bring on a much more thoughtful and deliberative approach to conflict. Eric Topol (09:49): Yeah, I mean, I think one of the salient points is that avoiding the conflict can make things worse. And as you described that it's not, I would've thought that there are some people who are just innately gifted to being diplomatic and artful about having to deal with the conflict issue and others, there's just no hope. But in fact, it can be acquired. And you alluded to this kind of neuroplasticity, the brain and you advocate for chair work. Can you tell us about chair work, because that's something I wouldn't have thought would help in this manner. Bob Bordone (10:30): Sure. I mean, I'll say a little bit work about that. A big part of this chair work idea, frankly, is influenced by work in internal family systems. And I was very fortunate early in my career, even though I was at teaching at law school to start partnering with some folks who did IFS work, they call it peace work oft

“Eventually, my dream would be to simulate a virtual cell.”—Demis Hassabis The aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube. TRANSCRIPT WITH LINKS TO AUDIO Eric Topol (00:06): Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford’s Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve. Steve Quake (00:42): Thanks, Eric. It's great to be here. Charlotte Bunne: Thanks for having me. Eric Topol (00:45): Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper? Steve Quake (01:33): We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article. Eric Topol (02:02): And Charlotte, how did you get to be drafting the paper? Charlotte Bunne (02:09): So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started. Eric Topol (02:29): And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there. Steve Quake (03:19): Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that. Eric Topol (04:09): And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell? Charlotte Bunne (05:41): So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect. Charlotte Bunne (06:34): Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection. Eric Topol (07:41): Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this? Steve Quake (09:06): Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there. Steve Quake (10:14): And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological info

Thank you Katelyn Jetelina, Andy Meyers, Tracy Paeschke, MD, FACC, Bruce Lanphear, Tay MacIntyre, and many others for tuning into my live video with Katelyn Jetelina! Join me for my next live video in the app. Get full access to Ground Truths at erictopol.substack.com/subscribe

Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH. A clip from our conversation The audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube. Transcript with audio and external links Eric Topol (00:06): Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute’s Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you. Anna Greka (00:48): Oh, thank you. It's a great honor to be here and glad to chat with you, Eric. Eric Topol (00:54): Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion. A Mysterious Kidney Disease Unraveled Anna Greka (01:59): Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising. Anna Greka (02:51): After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially results in a neoprotein like a toxic, what I call a mangled protein that sort of accumulates inside the kidney cells. Anna Greka (03:55): And that's where my sort of adventure began. It was Eric Lander’s group, who is the founding director of the Broad who discovered the mutation. And then through a conversation we had here in Boston, we sort of discovered that there was an opportunity to collaborate and so that’s how I came to the Broad, and that's the beginnings of this story. I think what's fascinating about this story though, that starts in a remote Mediterranean island and then turns out to be a disease that you can find in every continent all over the world. There are probably millions of patients with kidney disease in whom we haven't recognized the existence of this mutation. What's really interesting about it though is that what we discovered is that the mangled protein that's a result of this misspelling of this mutation is ultimately captured by a family of cargo receptors, they’re called the TMED cargo receptors and they end up sort of grabbing these misfolded proteins and holding onto them so tight that it's impossible for the cell to get rid of them. Anna Greka (04:55): And they become this growing heap of molecular trash, if you will, that becomes really hard to manage, and the cells ultimately die. So in the process of doing this molecular sleuthing, as I call it, we actually also identified a small molecule that actually disrupts these cargo receptors. And as I described in my TED Talk, it's a little bit like having these cargo trucks that ultimately need to go into the lysosome, the cells recycling facility. And this is exactly what this small molecule can do. And so, it was just like a remarkable story of discovery. And then I think the most exciting of all is that these cargo receptors turn out to be not only relevant to this one mangled misshapen protein, but they actually handle a completely different misshapen protein caused by a different genetic mutation in the eye, causing retinitis pigmentosa, a form of blindness, familial blindness. We're now studying familial Alzheimer's disease that's also involving these cargo receptors, and there are other mangled misshapen proteins in the liver, in the lung that we're now studying. So this becomes what I call a node, like a nodal mechanism that can be targeted for the benefit of many more patients than we had previously thought possible, which has been I think, the most satisfying part about this story of molecular sleuthing. Eric Topol (06:20): Yeah, and it's pretty extraordinary. We'll put the figure from your classic Cell paper in 2019, where you have a small molecule that targets the cargo receptor called TMED9. Anna Greka (06:34): Correct. Expanding the Mission Eric Topol (06:34): And what's amazing about this, of course, is the potential to reverse this toxic protein disease. And as you say, it may have applicability well beyond this MUC1 kidney story, but rather eye disease with retinitis pigmentosa and the familial Alzheimer's and who knows what else. And what's also fascinating about this is how, as you said, there were these limited number of families with the kidney disease and then you found another one, uromodulin. So there's now, as you say, thousands of families, and that gets me to part of your sleuth work is not just hardcore science. You started an entity called the Ladders to Cures (L2C) Scientific Accelerator. Eric Topol (07:27): Maybe you can tell us about that because this is really pulling together all the forces, which includes the patient advocacy groups, and how are we going to move forward like this? Anna Greka (07:39): Absolutely. I think the goal of the Ladders to Cures Accelerator, which is a new initiative that we started at the Broad, but it really encompasses many colleagues across Boston. And now increasingly it's becoming sort of a national, we even have some international collaborations, and it's only two years that it's been in existence, so we're certainly in a growth mode. But the inspiration was really some of this molecular sleuthing work where I basically thought, well, for starters, it cannot be that there's only one molecular node, these TMED cargo receptors that we discovered there's got to be more, right? And so, there's a need to systematically go and find more nodes because obviously as anyone who works in rare genetic diseases will tell you, the problem for all of us is that we do what I call hand to hand combat. We start with the disease with one mutation, and we try to uncover the mechanism and then try to develop therapies, and that's wonderful. Anna Greka (08:33): But of course, it's slow, right? And if we consider the fact that there are 30 million patients in the United States in every state, everywhere in the country who suffer from a rare genetic disease, most of them, more than half of them are children, then we can appreciate the magnitude of the problem. Out of more than 8,000 genes that are involved in rare genetic diseases, we barely have something that looks like a therapy for maybe 500 of them. So there's a huge mismatch in the unmet need and magnitude of the problem. So the Ladders to Cures Accelerator is here to address this and to do this with the most modern tools available. And to your point, Eric, to bring patients along, not just as the recipients of whatever we discover, but also as partners in the research enterprise because it's really important to bring their perspectives and of course their partnerships in things like developing appropriate biomarkers, for example, for what we do down the road. Anna Greka (09:35): But from a fundamental scientific perspective, this is basically a project that aims to identify every opportunity for nodes, underlying all rare genetic diseases as quickly as possible. And this was one of the reasons I was there at the AI for Science Forum, because of course when one undertakes a project in which you're basically, this is

Before getting into this new podcast, have you checked out the recent newsletter editions and podcasts of Ground Truths? —the first diagnostic immunome —a Covid nasal vaccine update —medical storytelling and uncertainty —why did doctors with A.I. get outperformed by A.I. alone? The audio is available on iTunes and Spotify. The full video is embedded here, at the top, and also can be found on YouTube. Transcript with links to Audio and External Links Eric Topol (00:07): Well, hello. It's Eric Topol with Ground Truths, and I am just thrilled today to welcome Carl Zimmer, who is one of the great science journalists of our times. He's written 14 books. He writes for the New York Times and many other venues of great science, journalism, and he has a new book, which I absolutely love called Air-Borne. And you can see I have all these rabbit pages tagged and there's lots to talk about here because this book is the book of air. I mean, we're talking about everything that you ever wanted to know about air and where we need to go, how we missed the boat, and Covid and everything else. So welcome, Carl. Carl Zimmer (00:51): Thanks so much. Great to be here. A Book Inspired by the Pandemic Eric Topol (00:54): Well, the book starts off with the Skagit Valley Chorale that you and your wife Grace attended a few years later, I guess, in Washington, which is really interesting. And I guess my first question is, it had the look that this whole book was inspired by the pandemic, is that right? Carl Zimmer (01:18): Certainly, the seed was planted in the pandemic. I was working as a journalist at the New York Times with a bunch of other reporters at the Times. There were lots of other science writers also just trying to make sense of this totally new disease. And we were talking with scientists who were also trying to make sense of the disease. And so, there was a lot of uncertainty, ambiguity, and things started to come into focus. And I was really puzzled by how hard it was for consensus to emerge about how Covid spread. And I did some reporting along with other people on this conflict about was this something that was spreading on surfaces or was it the word people were using was airborne? And the World Health Organization said, no, it's not airborne, it's not airborne until they said it was airborne. And that just seemed like not quantum physics, you know what I'm saying? In the sense that it seemed like that would be the kind of thing that would get sorted out pretty quickly. And I think that actually more spoke to my own unfamiliarity with the depth of this field. And so, I would talk to experts like say, Donald Milton at the University of Maryland. I'd be like, so help me understand this. How did this happen? And he would say, well, you need to get to know some people like William Wells. And I said, who? Eric Topol (02:50): Yeah, yeah, that's what I thought. Carl Zimmer (02:53): Yeah, there were just a whole bunch of people from a century ago or more that have been forgotten. They've been lost in history, and yet they were real visionaries, but they were also incredibly embattled. And the question of how we messed up understanding why Covid was airborne turned out to have an answer that took me back thousands of years and really plunged me into this whole science that's known as aerobiology. Eric Topol (03:26): Yeah, no, it's striking. And we're going to get, of course, into the Covid story and how it got completely botched as to how it was being transmitted. But of course, as you go through history, you see a lot of the same themes of confusion and naysayers and just extraordinary denialism. But as you said, this goes back thousands of years and perhaps the miasma, the moral stain in the air that was start, this is of course long before there was thing called germ theory. Is that really where the air thing got going? A Long History of Looking Into Bad Air Carl Zimmer (04:12): Well, certainly some of the earliest evidence we have that people were looking at the air and thinking about the air and thinking there's something about the air that matters to us. Aristotle thought, well, there's clearly something important about the air. Life just seems to be revolve around breathing and he didn't know why. And Hippocrates felt that there could be this stain on the air, this corruption of the air, and this could explain why a lot of people in a particular area, young and old, might suddenly all get sick at the same time. And so, he put forward this miasma theory, and there were also people who were looking at farm fields and asking, well, why are all my crops dead suddenly? What happened? And there were explanations that God sends something down to punish us because we've been bad, or even that the air itself had a kind of miasma that affected plants as well as animals. So these ideas were certainly there, well over 2,000 years ago. Eric Topol (05:22): Now, as we go fast forward, we're going to get to, of course into the critical work of William and Mildred Wells, who I'd never heard of before until I read your book, I have to say, talk about seven, eight decades filed into oblivion. But before we get to them, because their work was seminal, you really get into the contributions of Louis Pasteur. Maybe you could give us a skinny on what his contributions were because I was unaware of his work and the glaciers, Mer de Glace and figuring out what was going on in the air. So what did he really do to help this field? Carl Zimmer (06:05): Yeah, and this is another example of how we can kind of twist and deform history. Louis Pasteur is a household name. People know who Louis Pasteur is. People know about pasteurization of milk. Pasteur is associated with vaccines. Pasteur did other things as well. And he was also perhaps the first aerobiologist because he got interested in the fact that say, in a factory where beet juice was being fermented to make alcohol, sometimes it would spoil. And he was able to determine that there were some, what we know now are bacteria that were getting into the beet juice. And so, it was interrupting the usual fermentation from the yeast. That in itself was a huge discovery. But he was saying, well, wait, so why are there these, what we call bacteria in the spoiled juice? And he thought, well, maybe they just float in the air. Carl Zimmer (07:08): And this was really a controversial idea in say, 1860, because even then, there were many people who were persuaded that when you found microorganisms in something, they were the result of spontaneous generation. In other words, the beet juice spontaneously produced this life. This was standard view of how life worked and Pasteur was like, I'm not sure I buy this. And this basically led to him into an incredible series of studies around Paris. He would have a flask, and he'd have a long neck on it, and the flask was full of sterile broth, and he would just take it places and he would just hold it there for a while, and eventually bacteria would fall down that long neck and they would settle in the broth, and they would multiply in there. It would turn cloudy so he could prove that there was life in the air. Carl Zimmer (08:13): And they went to different places. He went to farm fields, he went to mountains. And the most amazing trip he took, it was actually to the top of a glacier, which was very difficult, especially for someone like Pasteur, who you get the impression he just hated leaving the lab. This was not a rugged outdoorsman at all. But there he is, climbing around on the ice with this flask raising it over his head, and he caught bacteria there as well. And that actually was pivotal to destroying spontaneous generation as a theory. So aerobiology among many, many other things, destroyed this idea that life could spontaneously burst into existence. Eric Topol (08:53): Yeah, no. He says ‘these gentlemen, are the germs of microscopic beings’ shown in the existence of microorganisms in the air. So yeah, amazing contribution. And of course, I wasn't familiar with his work in the air like this, and it was extensive. Another notable figure in the world of germ theory that you bring up in the book with another surprise for me was the great Robert Koch of the Koch postulates. So is it true he never did the third postulate about he never fulfilled his own three postulates? Carl Zimmer (09:26): Not quite. Yeah, so he had these ideas about what it would take to actually show that some particular pathogen, a germ, actually caused a disease, and that involved isolating it from patients, culturing it outside of them. And then actually experimentally infecting an animal and showing the symptoms again. And he did that with things like anthrax and tuberculosis. He nailed that. But then when it came to cholera, there was this huge outbreak in Egypt, and people were still battling over what caused cholera. Was it miasma? Was it corruption in the air, or was it as Koch and others believe some type of bacteria? And he found a particular kind of bacteria in the stool of people who were dying or dead of cholera, and he could culture it, and he consistently found it. And when he injected animals with it, it just didn't quite work. Eric Topol (10:31): Okay. Yeah, so at least for cholera, the Koch’s third postulate of injecting in animals, reproducing the disease, maybe not was fulfilled. Okay, that's good. Eric Topol (10:42): Now, there's a lot of other players here. I mean, with Fred Meier and Charles Lindbergh getting samples in the air from the planes and Carl Flügge. And before we get to the Wells, I just want to mention these naysayers like Charles Chapin, Alex Langmuir, the fact that they said, well, people that were sensitive to pollen, it was just neurosis. It wasn't the pollen. I mean, just amazing stuff. But anyway, the principles of what I got from the book was the Wells, the husband and wife, very interesting characters who eventually even split

Before getting into this new podcast, have you checked out the recent newsletter editions of Ground Truths? —how are gut microbiome drives sugar cravings —the influence of sleep on brain waste clearance and aging —the new findings of microplastics in the brain —the surprise finding about doctors and A.I. In this podcast with Dr. Emily Silverman, an internist and founder of The Nocturnists, an award winning podcast and live show, we discuss what inspired her in medicine, what led to her disillusionment, the essentiality of storytelling, of recognizing uncertainty, the limits of A.I., and promoting humanity in medicine. The audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube. “Storytelling is medicine's currency. Storytelling is not just an act of self-healing; it may actually create better physicians.”—Emily Silverman Transcript with links to audio and relevant publications, websites Eric Topol (00:07): Well, hello. This is Eric Topol with Ground Truths, and with me, I am delighted to welcome Dr. Emily Silverman, who is Assistant Volunteer Professor of Medicine at UCSF, an old training grounds for me. And we're going to talk about some of the experience she's had there and she is the Founder of the remarkably recognized podcast, The Nocturnists. It's more than a podcast folks. We'll talk about that too. So Emily, welcome. Emily Silverman (00:40): Thank you for having me. Inspiration by Kate McKinnon Eric Topol (00:42): Yeah. Well, I thought I would go back to perhaps when we first synapsed, and it goes back to a piece you wrote in JAMA about going to the Saturday Night Live (SNL) with Kate McKinnon. And it was one of my favorite columns, of course, it brought us together kind of simpatico because you were telling a story that was very personal, and a surprise factor added to it. We'll link to it. But it said, ‘Sometime in 2016, I fell in love with SNL comedian Kate McKinnon.’ You wrote, ‘It was something about her slow-mo swagger; her unilateral dimple, flickering in and out of existence; the way she drinks up her characters and sweats them from her pores.’ I mean, you're an incredible writer, no less podcast interviewer, organizer, doctor. And you talked about my sterile clinical life, which was kind of maybe a warning of things to come and about the fact that there's two very different career paths, comedy and medicine. One could argue they are in essence the same. So maybe you could tell us about that experience and about Kate McKinnon who, I mean, she's amazing. Emily Silverman (02:09): You're making me blush. Thank you for the kind words about the piece and about the writing, and I'm happy to give you a bit of background on that piece and where it came from. So I was in my internal medicine residency at UCSF and about halfway through residency really found myself hitting a wall. And that is actually what gave birth to The Nocturnists, which is the medical storytelling program that I run. But I think another symptom of my hitting that wall, so to speak, and we can talk more about what exactly that is and what that means, was me really looking outside of medicine and also outside of my typical day-to-day routine to try to find things that were a part of me that I had lost or I had lost touch with those aspects of myself. And one aspect of myself that I felt like I had lost touch to was my humorous side, my sense of humor, my silly side even you could say. Emily Silverman (03:17): And throughout my life I have this pattern where when I'm trying to get back in touch with a side of myself, I usually find somebody who represents that and sort of study it, I guess you could say. So in this case, for whatever reason that landed on Kate McKinnon, I just loved the surrealism of her comedy. I loved how absurd she is and loved her personality and so many things. Everything that you just read and really found her and her comedy as an escape, as a way to escape the seriousness of what I was doing on a day-to-day basis in the hospital and reconnect with those humorous sides of myself. So that's the understory. And then the story of the article is, I happened to be traveling to New York for a different reason and found myself standing in line outside of 30 Rock, hoping to get into Saturday Night Live. And there was basically a zero chance that we were going to get in. And part of the reason why is the musical guest that week was a K-pop band called BTS, which is one of the most famous bands in the world. And there were BTS fans like camped out in three circles around 30 Rock. So that week in particular, it was especially difficult to get in. There was just too many people in line. And we were at the very end of the line. Eric Topol (04:43): And it was in the pouring rain, too. Emily Silverman (04:45): And it was pouring rain. And my husband, God bless him, was there with me and he was like, what are we doing? And I was like, I don't know. I just have a feeling that we should stay in line, just go with it. So we did stay in line and then in the morning we got a number, and the way it works is you get your number and then that evening you show up with your number and our number was some crazy number that we weren't going to get in. But then that evening when we went back with our number to wait in line again to get in, what ended up happening is a young woman in the NBC gift shop, she passed out in the middle of the gift shop and I was right there. And so, I went over to her and was asking her questions and trying to help her out. Emily Silverman (05:27): And fortunately, she was fine. I think she just was dehydrated or something, and the security guards were so appreciative. And the next thing I knew, they were sweeping me backstage and up a staircase and in an elevator and they said, thank you so much for your service, welcome to Saturday Night Live. So it became this interesting moment where the very thing that I had been escaping from like medicine and serving and helping people ended up being the thing that gave me access, back to that side of myself, the humorous side. So it was just felt kind of cosmic, one of those moments, like those butterfly wing flapping moments that I decided to write about it and JAMA was kindly willing to publish it. Eric Topol (06:15): Well, it drew me to you and recognize you as quite an extraordinary talent. I don't know if you get recognized enough for the writing because it's quite extraordinary, as we'll talk about in some of your other pieces in the New York Times and in other JAMA journals and on and on. But one thing I just would note is that I resort to comedy a lot to deal with hard times, like the dark times we're in right now, so instead of watching the news, I watch Jimmy Kimmel's monologue or Colbert's monologue or the Comedy Show, anything to relieve some of the darkness that we're dealing with right at the moment. And we're going to get back to comedy because now I want to go back, that was in 2019 when you wrote that, but it was in 2016 when you formed The Nocturnists. Now, before you get to that critical path in your career of this new podcast and how it blossomed, how it grew is just beyond belief. But maybe you could tell us about your residency, what was going on while you were a medical resident at UCSF, because I can identify with that. Well, like any medical residency, it's pretty grueling experience and what that was like for you. Medical Residency Emily Silverman (07:45): There were so many wonderful positive aspects of residency and there were so many challenges and difficult aspects of residency. It's all mixed up into this sticky, complicated web of what residency was. On the positive side, some of the most amazing clinicians I've ever met are at UCSF and whether that was seasoned attendings or chief residents who they just seemed to have so many skills, the clinical, the research, the teaching, just amazing, amazing high caliber people to learn from. And of course, the patient population. And at UCSF, we rotate at three different hospitals, the UCSF hospital, the SF General Hospital, which is the public county hospital and the VA hospital. So having the opportunity to see these different patient populations was just such a rich clinical and storytelling opportunity. So there was a lot there that was good, but I really struggled with a few things. Emily Silverman (08:48): So one was the fact that I spent so much of my sitting in front of a computer, and that was not something that I expected when I went into medicine when I was young. And I started to learn more about that and how that happened and when that changed. And then it wasn't just the computer, it was the computer and other types of paperwork or bureaucratic hurdles or administrative creep and just all the different ways that the day-to-day work of physicians was being overtaken by nonclinical work. And that doesn't just mean thinking about our patients, but that also means going to the bedside, sitting with our patients, getting to know them, getting to know their families. And so, I started to think a lot about clinical medicine and what it really means to practice and how that's different from how it was 10, 20, 40 years ago. Emily Silverman (09:43): And then the other part of it that I was really struggling with was aspects of medical culture. The fact that we were working 80 hour weeks, I was working 28 hour shifts every fourth night, every other month. And the toll that took on my body, and I developed some health issues as a result of that and just felt in a way, here I am a doctor in the business of protecting and preserving health and my own health is kind of being run into the ground. And that didn't make sense to me. And so, I started asking questions about that. So there was a lot there. And at first I thought, maybe this is a me thing or maybe this is a California thing. And eventually I realized

The Chief Scientific Advisor at Novo Nordisk, Lotte Bjerre Knudsen, was the key force who pushed hard to develop GLP-1 drugs for treating obesity and subsequently for Alzheimer’s. She was recently recognized by the 2024 Lasker Medical Research Award, and the 2024 AAAS Bhaumik Breakthrough of the Year Award. That recognition is richly deserved, since it is unclear if the GLP-1 drug path to obesity treatment, and all of the associated benefits, would have been seen at this time without her influence. That’s especially true given the mystery for why people with Type 2 diabetes (for which these drugs were used for many years) did not exhibit much in the way of weight loss. We discussed that and the future of these drugs, including their potential to prevent neurodegenerative diseases. And about dressing up in pink! The Ground Truths podcasts are also available on Apple and Spotify. Our entire conversation can also be seen by video at YouTube along with all of the Ground Truths podcasts. If you like the video format, please subscribe to this channel. Even if you prefer video, please take a look at the transcript with graphics and useful links to citations. A Video Clip below on the barriers of a woman scientist to push Novo Nordisk to develop GLP-1 for obesity. “I was always just been a nerdy little scientist who kind of found home here in this company for 35 years.”—Lotte Bjerre Knudsen, 60 Minutes Transcript with Links to audio and external references Eric Topol (00:06): Well, hello, it's Eric Topol with Ground Truths, and I have with me a special guest. She's the Chief Science Officer of Novo Nordisk and it's Lotte Bjerre Knudsen, and we're delighted to have her. She's a recent recipient of the Lasker Award, which I think is considered like the pre-Nobel Award here in the United States. And I was involved with her in terms of researching who was the principal person who brought the GLP-1 drugs to the forefront for obesity, and it turned out to be Lotte. So welcome, Lotte. Lotte Bjerre Knudsen (00:48): Thank you very much. And also very, very happy to be here. I'm not the Chief Science Officer for Novo Nordisk, I'm the Chief Scientific Advisor of working for the Chief Science Officer of Novo Nordisk, but maybe too many people, not so different, right? From Laundry Detergents to GLP-1 Drugs Eric Topol (01:06): Yes. Thank you, I actually meant to say advisor, but yes, I'm glad you cleared that up. I know from speaking to some of your colleagues, I actually spoke to Robin yesterday that you are looked to very highly, the most highly regarded person in science there, so not surprisingly. What I want to do is first talk about the glucagon-like peptide-1 (GLP-1) that got its legs back in, I guess 1984. So we're going way back. And what's also interesting is that you go way back at Novo Nordisk to 35 years in 1989. And so, there had been this work with this extraordinary hormone and neurotransmitter with a very short half-life that you knew about. But when you first started in Novo Nordisk, you weren't working on this. As I understand it, you're working on laundry detergent enzymes. How did you make this pivot from the laundry enzymes to getting into the GLP-1 world? Lotte Bjerre Knudsen (02:16): Yeah, thank you for that question. I'm from the technical University of Denmark, so I'm trained in biotechnology, and we're a small country, so not that many companies to work for. And I always had my mind set on, I wanted to work for Novo as it was called back then, and it just happened to be in the industrial enzyme part that I got my foot in first. And then I had a very interesting boss at the time. Unfortunately, he's not alive anymore, but he was both a medical doctor as well as a chemist. So he was actually put in charge of actually, let's see if we can do something new in diabetes. And then since he hired me and I had not been there that long, I simply tagged along as the youngest scientist on the team, and then suddenly I became a diabetes researcher. Around the same time, I think you remember that all of pharma was interested in obesity in the early 90s, everyone wanted to do diabetes as well as obesity, but they were separate teams and they all wanted to do small molecules, but it just happens to be so that the best idea we could find at that time was actually GLP-1, because we actually had clinical data relatively early that GLP-1 was a really good candidate as a treatment for diabetes because of the glucose sensitivity of the actions. (03:43): So you'd have efficient lowering of glucose through a dual mechanism with increasing insulin, lowering glucagon, and then it was safe because there wasn't this hypoglycemia you get from insulin. But then I had other colleagues who were working on obesity, and I was just kind of listening, right, what's going on there? And then also a colleague that I had, we had, I don’t know if you remember the old Hagedorn Research Institute, but Novo actually had kind of like an academic research institute that was affiliated with us. And there was this group that were working on this glucagon tumor model that produced high levels of glucagon, GLP-1 and PYY. And these rats, they starved themselves to death. And I knew about that from 1994. So that actually inspired my thinking. So when Stephen Bloom's paper came out in January of 1996, and he was the first one to call GLP-1 a neurotransmitter, I think, but I was already way into actually screening these kind of molecules that later then became liraglutide. No One Else Thought About This [Obesity] (04:54): And then I thought, why on earth should we not actually do both things at the same time? If we have an idea that can both work in diabetes in a much safer way than in insulin, and then also at the same time work in obesity. But the reality is that no one else thought about this, or if they thought about it, they didn't really think that it would a good idea. But I think I had the luxury of being in a biotech company, so everyone was working with peptides and proteins. So I don't think I got the same challenge that the other people in the other pharma’s got when they all wanted small molecules. Eric Topol (05:36): Well, also just to set the foundation here, which you alluded to, there had been so many attempts to come up with a drug that would work, not just of course in diabetes where there are many classes of drugs, but moreover, to treat the condition of obesity. Actually, I was involved with one of them, Rimonabant and did the large trial, which as you know, led to having to stop the drug, discontinue it because it was associated with suicidal ideation and actual some suicide. So there had been such a long history of checkered inability to come up with a drug. But what was striking is the challenge, and this is one of the first important questions about, when you had the extended half-life of the first GLP-1 drug, that instead of having to take multiple times a day, you could actually, with liraglutide get to a point where you were starting to get to an extended half-life. This is now going back to 1997 with approval in 2010, still 14 years ago. But when you came up with this drug, because this was certainly one of your great contributions, this drug was just a step along the way in this kind of iterative process, wouldn't you say? It wasn't the long half-life and the potency that eventually got us to where we are today. Is that true? Lotte Bjerre Knudsen (07:15): Yeah, it was a stepwise process. And what's super interesting about this class of medicines is that they're actually so different. If you talk about a class of medicine where small molecules, they can be different, but they're usually more alike than they're different. And when it comes to this class with these medium-sized peptides, people tried a whole bunch of different things. So they're actually really, really different. Some are simple peptides. So the idea that I came up with was to use this fatty acid isolation principle, and that's then a subclass in the class. And then the first, once weekly, for example, was an antibody-based molecule liraglutide. So they're much, much, much larger molecule compared to the small peptides. So they're very different. And neither the simple peptides nor the really big antibody derived molecules, they don't give a lot of weight loss. So we actually get more weight loss with these kinds of molecules, which is also why you can now see that it has actually kind of inspired a whole industry to kind of try and go and make similar kinds of molecules. Eric Topol (08:27): Well, inspired a whole industry is an understatement. It’s become the most extraordinary class of drugs, I think in medical history, having been a student of various, I mean obviously statins have been a major contribution, but this seems to have transcended that already. We're going to talk about more about where things are headed, but this fatty acid acetylation was a major step forward in extending the half-life of the drug, whereby today you can give semaglutide once a week. And this, I think, of course, there are many ways that you might've been able to extend the half-life, but you were starting with a hormone, a natural hormone neurotransmitter that had such an exquisitely short half-life of basically second or minutes rather than that you could give for a week. So I know there were many different ways you could have protected or extended the half-life one way or another, but this seemed to be a breakthrough of many along the chain of breakthroughs. But the question I have is when you were giving this to the diabetics, which was the precedent, that was really what these drugs were first intended, they didn't lose that much weight, and they never, still today when it's looked at for obese non-diabetics versus diabetics, there's a gap in weight loss. Why is that at the exact same dose, with the exact same peptide that the weight loss differs for pe

Piezo touch and pressure-sensing ion channels are showing up everywhere as the explanation for physiologic phenomena, both at the macro and micro levels. Ardem Patapoutian, my friend and colleague at Scripps Research, discovered these receptors back in 2010 and was awarded the Nobel Prize in 2021 for his work. As you’ll see/hear from our conversation, the field has exploded. And you’ll get to know Ardem, who is such a fun, charismatic, and down-to-earth person. He also recently got a unique tattoo (videos below) and I wonder (unlikely) if any other Nobel laureates have one related to their discovery?! Below is a video clip from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify. Transcript with links to audio Eric Topol (00:07): Well, hello. It's Eric Topol with Ground Truths, and I've really got a special guest today. The first time for the podcast, I've been able to interview a colleague and faculty at Scripps Research, Ardem Patapoutian, who just by the way happens to be the 2021 Nobel Laureate in Physiology or Medicine. So welcome, Ardem. It's so wonderful to have you. Ardem Patapoutian (00:30): Thanks so much, Eric. Looking forward to chatting with you. Eric Topol (00:34): Well, this has been interesting because although I've known you for several years, I didn't research you. I mean, I had to learn about more than I even do. And of course, one of the great sources of that is on the Nobel Prize website where you tell your whole story. It is quite a story and not to review all of it, but I wanted to go back just before you made the call to move to Los Angeles from Beirut, Lebanon and with the scare that you went through at that time, it seemed like that was just extraordinary that you had to live through that. Ardem Patapoutian (01:11): Yeah, so I am of Armenian origin, but I was born in Lebanon and born in 1967, so I was eight years old when the civil war started. So it's a kind of bizarre childhood in the sense that with all the bombs and fighting in Lebanon. So it was tough childhood to have, but it was never personal. It was bombs and such. And so, the event you're talking about is, I happened to be kidnapped while crossing East to West Beirut. They only held me for four or five hours at first asking me questions to see who I am, but I think they pretty soon figured out that I was not a dangerous guy and they ended up letting me go. But before that, that incident really had a huge impact on me so that by the time I got home, I literally said, I'm out of here. I'm going to find a way to leave the country. And so, that's what, very quickly within a few months I packed and came to United States. Eric Topol (02:19): And how did you pick LA to be your destination? Ardem Patapoutian (02:22): Being from the Armenian community, there's a lot of Armenians in Los Angeles. My cousins already had moved there. They also grew up in Lebanon. And my brother, who's a few years older than me, got admitted to USC graduate school in engineering. So he was going to be there. So it made a lot of sense. Eric Topol (02:44): Oh yeah. Ardem Patapoutian (02:45): Unlike him, I came with no school or job prospects because it happened so fast that I kind of just left. One year I was at American University of Beirut for one year, but then just left and came here. So worked for a year in various jobs and then started going back to school to UCLA. Eric Topol (03:07): Yeah, I saw how there was about a year where you were delivering pizzas and before you got into UCLA, and that must have been an interesting off year, if you will. Well, the story of course, just to fast forward, you did your baccalaureate at UCLA, your PhD at Caltech, postdoc at UCSF, and then you came to Scripps Research 24 years ago along with Pete Schultz, and it's been quite an amazing run that you've had. Now, before we get into PIEZO receptors, the background, maybe you could help me understand, the precursor work seems to be all related to the transient receptor potential (TRP) series, also ion channels. They were of course related to whether it was heat and temperature or somatosensory. How do these channels compare to the ones that you discovered years later? Background on these Ion Channels Ardem Patapoutian (04:09): Yeah, so the somatosensory neurons that innervate your fingertips and everywhere else in your body, their main job is to sense temperature and pressure. And this is very different than any other neuron or any other cell. So when you touch a hot stove that’s burning hot, you need to know about that immediately within milliseconds or something cold. So the opposite side of it is pressure sensing, and it also comes in light touch, which is pleasant or a hammer hitting your finger, which is unpleasant. But all of these have the same characteristic anyway, that is your body has learned at the molecular level to translate a physical stimulus such as temperature and pressure into an electrical signal that neurons use to communicate with each other. But this idea of how you translate physical stimuli into chemical or electrical signal has been a long open question because as you know, most of our cells communicate by chemicals, whether that's hormones or small molecules, we know how that works, receptor bind to ligand, confirmational change and you get a kinase activation and that's enough. But here, how do you sense pressure? How do you sense temperature? It was just, there wasn't much known about that. And that's why our earlier work on TRP channels, which were temperature sensors came before the pressure. And so, they're very related in that sense. Eric Topol (05:52): The structure of these, if you were to look at them, do they look pretty similar? What the TRP as you say, and what you did back in the 2010 Science paper, which we'll link to, of course the classic paper where you describe PIEZO1 and PIEZO2, but if you were to look at this structures, would they look pretty similar? Ardem Patapoutian (06:14): No, that's a good question. And they absolutely don't. That's why finding these receptors were so hard. So if you go back to other sensory receptors, vision rhodopsin G-protein coupled receptor (GPCRs), larger G-protein coupled receptor look the same. So for example, when it was identified by chemically, that smell also works through G-protein coupled receptor. Richard Axel and Linda Buck, who also won the Nobel Prize, found those receptors by homology to visual GPCRs. The ion channels other than the fact that they crossed the membrane a few times or more, they have nothing else in common. If you looked at their structure, you can't even immediately tell they’re ion channels. So you couldn't find these by structural homology or sequence homology. So you had to do something else. And usually that means functional screens and et cetera. Eric Topol (07:09): Well, yeah, and I'm in touch with the screening. We'll get to that and how you dig these up and find them. But the somatosensory ones are really interesting because I don't think a lot of people realize that when you have wasabi or you have Listerine mouthwash and feel the burn and that these are all mediated through these channels, right? Ardem Patapoutian (07:35): Yeah. So there's this whole field of chemesthesis, which means senses in your mouth, for example, that are not explained by taste transduction and olfactory. And these are actually by the same somatosensory neurons that help you sense temperature and pressure. And some of these receptors are the same. Their evolution has taken over and used them for many different things. The prime example of this is the capsaicin receptor that David Julius my co-laureate identified, which is also heat receptors. So all languages describe chili peppers as hot, and that's not a coincidence. It actually activates heat activated channel, and that's why we think of it as hot. And so, the same goes to another one of these TRP channels that you mentioned, which is TRPA1, and this one is also activated, but a lot of spicy foods other than the chili pepper active ingredient includes what's in garlic and onions and everything that has this burning sensation and chemicals of this and wasabi and chemicals of this are used in over the counter products like Listerine that cause that burning sensation. Eric Topol (08:54): So when you're chopping onions and it makes you cry, is that all part of it as well? Ardem Patapoutian (08:59): That’s all TRPA1, yeah. The Discovery, A Test of Perseverance Eric Topol (09:01): It's wild. Now, this was the groundwork. There were these heat temperature and somatic sensory, and then you were starting to wonder what about touch, what about out pressure and proprioception. And so, you went on a hunt, and it's actually kind of an incredible story about how you were able to find out of these cells that you had, screening hundreds or I guess you got to 72 different small interfering RNA blocking that you finally found the one. Is that right? Ardem Patapoutian (09:37): That's right. So in retrospect, looking back at it, I think there's such an interesting scientific message there. And so, many of us were looking for this touch pressure sensors and we were all looking in the DRG sensory neurons that are complicated heterogeneous, they don't divide. It's not easy to do a screen on them. And ultimately after a lot of failures, what worked for us is to take a step back and ask a much more simpler question. And that was, can we find one of these cell lines that you could easily homogeneously grow in a culture dish, if they respond to mechanical force, can we find our channel there? And then go back and look if it's relevant in vivo for what process. So I think the message is ask the simplest question to answer the question you're after. And finding what that is, is

American healthcare is well known for its extreme cost and worst outcomes among industrialized (such as the 38 OECD member) countries, and beyond that to be remarkably opaque. The high cost of prescription drugs contributes, and little has been done to change that except for the government passing the Affordable Insulin Now Act at the end of 2022, enacted in 2023. But in January 2022 Mark Cuban launched Cost Plus Drugs that has transformed how many Americans can get their prescriptions filled at a fraction of the prevailing prices, bypassing pharmacy benefit managers (PBMs) that control 80% of US prescriptions. That was just the beginning of a path of creative destruction (disruptive innovation, after Schumpeter) of many key components American healthcare that Cuban is leading, with Cost Plus Marketplace, Cost Plus Wellness and much more to come. He certainly qualifies as a master disrupter: “someone who is a leader in innovation and is not afraid to challenge the status quo.” Below is a video clip from our conversation dealing with insurance companies. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify. Transcript with External links to Audio (00:07): Hello, it's Eric Topol with Ground Truths, and I have our special phenomenal guest today, Mark Cuban, who I think you know him from his tech world contributions and Dallas Mavericks, and the last few years he's been shaking up healthcare with Cost Plus Drugs. So Mark, welcome. Mark Cuban (00:25): Thanks for having me, Eric. Eric Topol (00:27): Yeah, I mean, what you're doing, you’ve become a hero to millions of Americans getting them their medications at a fraction of the cost they're used to. And you are really challenging the PBM industry, which I've delved into more than ever, just in prep for our conversation. It's just amazing what this group of companies, namely the three big three CVS Caremark, Optum of UnitedHealth and Express Scripts of Cigna with a market of almost $600 billion this year, what they're doing, how can they get away with all this stuff? Inner Workings of Pharmacy Benefit Managers Mark Cuban (01:03): I mean, they're just doing business. I really don't blame them. I blame the people who contract with them. All the companies, particularly the bigger companies, the self-insured companies, where the CEO really doesn't have an understanding of their healthcare or pharmacy benefits. And so, the big PBMs paid them rebates, which they think is great if you're a CEO, when in reality it's really just a loan against the money spent by your sickest employees, and they just don't understand that. So a big part of my time these days is going to CEOs and sitting with them and explaining to them that you're getting ripped off on both your pharmacy and your healthcare side. Eric Topol (01:47): Yeah, it's amazing to me the many ways that they get away with this. I mean, they make companies sign NDAs. They're addicted to rebates. They have all sorts of ways a channel of funds to themselves. I mean, all the things you could think of whereby they even have these GPOs. Each of these companies has a group purchasing organization (I summarized in the Table below). Mark Cuban (02:12): Yeah, which gives them, it's crazy because with those GPOs. The GPO does the deal with the pharmacy manufacturer. Then the GPO also does the deal with the PBM, and then the PBM goes to the self-insured employer in particular and says, hey, we're going to pass through all the rebates. But what they don't say is they've already skimmed off 5%, 10%, 20% or more off the top through their GPO. But that's not even the worst of it. That's just money, right? I mean, that's important, but I mean, even the biggest companies rarely own their own claims data. Mark Cuban (02:45): Now think about what that means. It means you can't get smarter about the wellness of your employees and their families. You want to figure out the best way to do GLP-1s and figure out how to reduce diabetes, whatever it may be. You don't have that claims data. And then they don't allow the companies to control their own formularies. So we've seen Humira biosimilars come out and the big PBMs have done their own version of the biosimilar where we have a product called Yusimry, which is only $594 a month, which is cheaper than the cheapest biosimilar that the big three are selling. And so, you would think in a normal relationship, they would want to bring on this new product to help the employer. No, they won't do it. If the employer asks, can I just add Cost Plus Drugs to my network? They'll say no, every single time. Mark Cuban (03:45): Their job is not to save the employer money, particularly after they've given a rebate. Because once they give that loan, that rebate to the employer, they need to get that money back. It's not a gift. It's a loan and they need to have the rebates, and we don't do rebates with them at all. And I can go down the list. They don't control the formula. They don't control, you mentioned the NDAs. They can't talk to manufacturers, so they can't go to Novo or to Lilly and say, let's put together a GLP-1 wellness program. All these different things that just are common sense. It's not happening. And so, the good news is when I walk into these companies that self-insured and talk to the CEO or CFO, I'm not asking them to do something that's not in their best interest or not in the best interest of the lives they cover. I'm saying, we can save you money and you can improve the wellness of your employees and their families. Where's the downside? Eric Topol (04:40): Oh, yeah. Yeah. And the reason they can't see the claims is because of the privacy issues? Mark Cuban (04:46): No, no. That's just a business decision in the contract that the PBMs have made. You can go and ask. I mean, you have every right to your own claims. You don't need to have it personally identified. You want to find out how many people have GLP-1s or what are the trends, or God forbid there's another Purdue Pharma thing going on, and someone prescribing lots of opioids. You want to be able to see those things, but they won't do it. And that's only on the sponsor side. It's almost as bad if not worse on the manufacturer side. Eric Topol (05:20): Oh, yeah. Well, some of the work of PBMs that you've been talking about were well chronicled in the New York Times, a couple of major articles by Reed Abelson and Rebecca Robbins: The Opaque Industry Secretly Inflating Prices for Prescription Drugs and The Powerful Companies Driving Local Drugstores Out of Business. We'll link those because I think some people are not aware of all the things that are going on in the background. Mark Cuban (05:39): You see in their study and what they reported on the big PBMs, it's crazy the way it works. And literally if there was transparency, like Cost Plus offers, the cost of medications across the country could come down 20%, 30% or more. Cost Plus Drugs Eric Topol (05:55): Oh, I mean, it is amazing, really. And now let's get into Cost Plus. I know that a radiologist, Alex Oshmyansky contacted you with a cold email a little over three years ago, and you formed Cost Plus Drugs on the basis of that, right? Mark Cuban (06:12): Yep, that's exactly what happened. Eric Topol (06:15): I give you credit for responding to cold emails and coming up with a brilliant idea with this and getting behind it and putting your name behind it. And what you've done, so you started out with something like 110 generics and now you're up well over 1,200 or 2,500 or something like that? Mark Cuban (06:30): And adding brands. And so, started with 111. Now we're around 2,500 and trying to grow it every single day. And not only that, just to give people an overview. When you go to www.costplusdrugs.com and you put in the name of your medication, let's just say it's tadalafil, and if it comes up. In this case, it will. It'll show you our actual cost, and then we just mark it up 15%. It's the same markup for everybody, and if you want it, we'll have a pharmacist check it. And so, that's a $5 fee. And then if you want ship to mail order, it's $5 for shipping. And if you want to use our pharmacy network, then we can connect you there and you can just pick it up at a local pharmacy. Eric Topol (07:10): Yeah, no, it's transparency. We don't have a lot of that in healthcare in America, right? Mark Cuban (07:15): No. And literally, Eric, the smartest thing that we did, and we didn't expect this, it's always the law of unintended consequences. The smartest thing we did was publish our entire price list because that allowed any company, any sponsor, CMS, researchers to compare our prices to what others were already paying. And we've seen studies come out saying, for this X number of urology drugs, CMS would save $3.6 billion a year. For this number of heart drugs at this amount per year, for chemotherapy drugs or MS drugs this amount. And so, it's really brought attention to the fact that for what PBMs call specialty drugs, whether there's nothing special about them, we can save people a lot of money. Eric Topol (08:01): It's phenomenal. As a cardiologist, I looked up a couple of the drugs that I'm most frequently prescribed, just like Rosuvastatin what went down from $134 to $5.67 cents or Valsartan it went down from $69 to $7.40 cents. But of course, there's some that are much more dramatic, like as you mentioned, whether it's drugs for multiple sclerosis, the prostate cancer. I mean, some of these are just thousands and thousands of dollars per month that are saved, brought down to levels that you wouldn't think would even be conceivable. And this has been zero marketing, right? Mark Cuban (08:42): Yeah, none. It's all been word of mouth and my big mouth, of course. Going out there and doing interviews like this and going to

A leader for conducting rigorous randomized trials of humans along with animal models for understanding nutrition and metabolism, Dr. Kevin Hall is a Senior Investigator at the National Institutes of Health, and Section Chief of the Integrative Physiology Section, NIDDK. In this podcast, we reviewed his prolific body of research a recent publications. The timing of optimizing our diet and nutrition seems apropos, now that we’re in in the midst of the holiday season! Below is a video snippet of our conversation on his ultra-processed food randomized trial. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify. Note: I’ll be doing a Ground Truths Live Chat on December 11th at 12 N EST, 9 AM PST, so please mark your calendar and join! Transcript with links to publications and audio Eric Topol (00:05): Well, hello. This is Eric Topol with Ground Truths, and I'm really delighted to have with me today, Dr. Kevin Hall from the NIH. I think everybody knows that nutrition is so important and Kevin is a leader in doing rigorous randomized trials, which is not like what we usually see with large epidemiologic studies of nutrition that rely on food diaries and the memory of participants. So Kevin, it's really terrific to have you here. Kevin Hall (00:34): Thanks so much for the invitation. Ultra-Processed Foods Eric Topol (00:36): Yeah. Well, you've been prolific and certainly one of the leaders in nutrition science who I look to. And what I thought we could do is go through some of your seminal papers. There are many, but I picked a few and I thought we'd first go back to the one that you published in Cell Metabolism. This is ultra-processed diets cause excessive caloric intake and weight gain. (Main results in graph below.) So maybe you can take us through the principle findings from that trial. Kevin Hall (01:10): Yeah, sure. So that was a really interesting study because it's the first randomized control trial that's investigated the role of ultra-processed foods in potentially causing obesity. So we've got, as you mentioned, lots and lots of epidemiological data that have made these associations between people who consume diets that are very high in ultra-processed foods as having greater risk for obesity. But those trials are not demonstrating causation. I mean, they suggest a strong link. And in fact, the idea of ultra-processed foods is kind of a new idea. It's really sort of appeared on the nutrition science stage probably most prominently in the past 10 years or so. And I first learned about this idea of ultra-processed foods, which is really kind of antithetical to the way most nutrition scientists think about foods. We often think about foods as nutrient delivery vehicles, and we kind of view foods as being the fraction of carbohydrates versus fats in them or how much sodium or fiber is in the foods. Kevin Hall (02:17): And along came this group in Brazil who introduced this new way of classifying foods that completely ignores the nutrient composition and says what we should be doing is classifying foods based on the extent and purpose of processing of foods. And so, they categorize these four different categories. And in the fourth category of this so-called NOVA classification scheme (see graphic below) , they identified something called ultra-processed foods. There's a long formal definition and it's evolved a little bit over the years and continues to evolve. But the basic ideas that these are foods that are manufactured by industries that contain a lot of purified ingredients made from relatively cheap agricultural commodity products that basically undergo a variety of processes and include additives and ingredients that are not typically found in home kitchens, but are typically exclusively in manufactured products to create the wide variety of mostly packaged goods that we see in our supermarkets. Kevin Hall (03:22): And so, I was really skeptical that there was much more about the effects of these foods. Other than that they typically have high amounts of sugar and saturated fat and salt, and they're pretty low in fiber. And so, the purpose of this study was to say, okay, well if there's something more about the foods themselves that is causing people to overconsume calories and gain weight and eventually get obesity, then we should do a study that's trying to test for two diets that are matched for these various nutrients of concern. So they should be matched for the macronutrients, they should be matched for the sugar content, the fat, the sodium, the fiber, and people should just be allowed to eat whatever they want and they shouldn't be trying to change their weight in any way. And so, the way that we did this was, as you mentioned, we can't just ask people to report what they're eating. Kevin Hall (04:19): So what we did was we admitted these folks to the NIH Clinical Center and to our metabolic ward, and it's a very artificial environment, but it's an environment that we can control very carefully. And so, what we basically did is take control over their food environment and we gave them three meals a day and snacks, and basically for a two-week period, they had access to meals that were more than 80% of calories coming from ultra-processed foods. And then in random order, they either received that diet first and give them simple instructions, eat as much as little as you want. We're going to measure lots of stuff. You shouldn't be trying to change your weight or weight that gave them a diet that had no calories from ultra-processed foods. In fact, 80% from minimally processed foods. But again, both of these two sort of food environments were matched for these nutrients that we typically think of as playing a major role in how many calories people choose to eat. Kevin Hall (05:13): And so, the basic idea was, okay, well let's measure what these folks eat. We gave them more than double the calories that they would require to maintain their weight, and what they didn't know was that in the basement of the clinical center where the metabolic kitchen is, we had all of our really talented nutrition staff measuring the leftovers to see what it was that they didn't eat. So we knew exactly what we provided to them and all the foods had to be in our nutrition database and when we compute what they actually ate by difference, so we have a very precise estimate about not only what foods they chose to ate, but also how many calories they chose to eat, as well as the nutrient composition. And the main upshot of all that was that when these folks were exposed to this highly ultra-processed food environment, they spontaneously chose to eat about 500 calories per day more over the two-week period they were in that environment then when the same folks were in the environment that had no ultra-processed foods, but just minimally processed foods. They not surprisingly gained weight during the ultra-processed food environment and lost weight and lost body fat during the minimally processed food environment. And because those diets were overall matched for these different nutrients, it didn't seem to be that those were the things that were driving this big effect. So I think there's a couple of big take homes here. One is that the food environment really does have a profound effect on just the biology of how our food intake is controlled at least over relatively short periods of time, like the two-week periods that we were looking at. And secondly, that there's something about ultra-processed foods that seem to be driving this excess calorie intake that we now know has been linked with increased risk of obesity, and now we're starting to put some of the causal pieces together that really there might be something in this ultra-processed food environment that's driving the increased rates of obesity that we've seen over the past many decades. Eric Topol (07:18): Yeah, I mean I think the epidemiologic studies that make the link between ultra-processed foods and higher risk of cancer, cardiovascular disease, type 2 diabetes, neurodegenerative disease. They're pretty darn strong and they're backed up by this very rigorous study. Now you mentioned it short term, do you have any reason to think that adding 500 calories a day by eating these bad foods, which by the way in the American diet is about 60% or more of the average American diet, do you have any inkling that it would change after a few weeks? Kevin Hall (07:54): Well, I don't know about after a few weeks, but I think that one of the things that we do know about body weight regulation and how it changes in body weight impact both metabolism, how many calories were burning as well as our appetite. We would expect some degree of moderation of that effect eventually settling in at a new steady state, that's probably going to take months and years to achieve. And so the question is, I certainly don't believe that it would be a 500 calorie a day difference indefinitely. The question is when would that difference converge and how much weight would've been gained or lost when people eventually reached that new plateau? And so, that's I think a really interesting question. Some folks have suggested that maybe if you extrapolated the lines a little bit, you could predict when those two curves might eventually converge. That's an interesting thought experiment, but I think we do need some longer studies to investigate how persistent are these effects. Can that fully explain the rise in average body weight and obesity rates that have occurred over the past several decades? Those are open questions. Eric Topol (09:03): Yeah. Well, I mean, I had the chance to interview Chris van Tulleken who wrote the book, Ultra-Processed People and I think you might remember in the book he talked about how he went on an ultra-processed diet

Below is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! This one has embedded one of my favorite TikTok’s from Will. There are several links to others in the transcript. The audios are also available on Apple and Spotify. Transcript with links to both audio and videos, commencement addresses, NEJM article coverage Eric Topol (00:06): Hi, it's Eric Topol from Ground Truths, and I've got an amazing couple with me today. It's Will Flanary and Kristin Flanary, the Glaucomfleckens. I've had the chance to get to know them a bit through Knock Knock, Hi! which is their podcast. And of course, everyone knows Dr. Glaucomflecken from his TikTok world and his other about 4 million followers on Instagram and Twitter and all these other social media, and YouTube. So welcome. Will Flanary (00:43): Thanks for having us. Kristin Flanary (00:44): Thank you. Happy to be here. By Way of Background Eric Topol (00:45): Yeah. Well, this is going to be fun because I'm going to go a quick background so we can go fast forward because we did an interview back in early 2022. Kristin Flanary (00:56): Yes. Eric Topol (00:57): And what you've been doing since then is rocking it. You're like a meteoric, right. And it was predictable, like rarefied talent and who couldn't love humor, medical humor, but by way of background, just for those who are not up to speed. I guess you got your start, Will, as a class clown when your mother was a teacher in the sixth grade. Will Flanary (01:22): Yep, yep. I misbehaved a little bit. It helped that I still made good grades, but I cut up a bit in class. Eric Topol (01:32): And then you were already in the comedy club circuits doing standup in Houston as an 18-year-old. Will Flanary (01:40): It was all amateur stuff, nothing, just dabble in it and trying to get better. I was always kind of naturally funny just with my friend group and everything. I loved making people laugh, but doing standups is a whole different ball game. And so, I started doing that around Houston as a high school senior and kept that going through college and a little bit into med school. Kristin Flanary (02:02): Houston was a good training ground, right? That where Harris Wittels was also coming up. Will Flanary (02:07): Yeah. A lot of famous comedians have come through Houston. Even going back to Bill Hicks back in the, was that the 80s, I think? Or 90s? Eric Topol (02:17): Well, and then of course, it was I think in 2020 when you launched Dr. Glaucomflecken, I think. Is that right? Will Flanary (02:28): That's when it really started to take off. I was on Twitter telling jokes back in 2016. Kristin Flanary (02:39): GomerBlog before that, that's actually where it was born. Will Flanary (02:41): I was doing satire writing. I basically do what I'm doing now, but in article form, trying to be The Onion of medicine. And then the pandemic hit, started doing video content and that's really with lockdown. That's when, because everybody was on social media, nobody had anything else to do. So it was right place, right time for me and branching out into video content. On to Medical School Commencement Addresses Eric Topol (03:11): Alright, so that's the background of some incredible foundation for humor. But since we last got together, I'll link the Medicine and the Machine interview we did back then. What has been happening with you two is nothing short of incredible. I saw your graduation speeches, Will. Yale in 2022, I watched the UCSF in 2023 and then the University of Michigan in 2024. Maybe there's other ones I don't even know. Kristin Flanary (03:45): There’s a few others. Will Flanary (03:45): There's a few. But I feel like you've done, I'm sure your fair share of commencement addresses as well. It's kind of hard to come up with different ways to be inspirational to the next generation. So fortunately, we have together, we have some life experiences and learned a thing or two by doing all of this social media stuff and just the things we've been through that I guess I have enough things to say to entertain an interest. Eric Topol (04:18): Well, you're being humble as usual, but having watched those commencement addresses, they were the best medical commencement addresses I've ever seen. And even though you might have told us some of the same jokes, they were so great that it was all right. Yeah, and you know what is great about it is you've got these, not the students, they all love you of course, because they're probably addicted to when's your next video going to get posted. (04:44): But even the old professors, all the family members, it's great. But one of the things I wanted to get at. Well, I'll start with the graduation speeches, because you were such an inspiration, not just with humor, but your message. And this gets back to you as a couple and the tragedies you've been through. So you really, I think, got into this co-survivor story and maybe Kristin, since you are the co-survivor of two bouts of Will’s testicular cancer, and then the sudden cardiac death. I mean, people don't talk about this much, so maybe you could help enlighten us. Tragedies and Being a Co-Survivor Kristin Flanary (05:26): Yeah, it’s funny because the experience of being a co-survivor is nothing new. It’s as long as we've had human beings, we've had co-survivors. But the concept around it and giving it a name and a label, a framework to be able to think about it, that is what I think is new and what people haven't talked about before. So co-survivor is just this idea that when a medical trauma happens to a patient, the patient has their experience and if they survive it, they are a survivor and they have a survivor experience. And also, most people are closely attached to at least one other person, if not many. And those people are co-surviving the medical event along with the survivor. That event is happening in their lives as was happening to them too. If someone comes in with a patient to the hospital, that person, you can just assume by default that their lives are pretty intimately or profoundly intertwined or else why would that person be there? And so, thinking of it as there's the patient and then there's also a co-patient, that family members in the past have only been thought of as caregivers if they've been thought of at all. And that is certainly one aspect of the role, but it's important to remember that whatever it is that's happening to the patient is also affecting the family members' lives in a really deep and profound way. Eric Topol (07:04): That's really helpful. Now, the fact that you recognize that in your graduation speech, Will, I think is somewhat unique. And of course, some of the other things that you touched on like playing to your creativity and the human factors, I mean, these are so important messages. Will Flanary (07:23): Well, in the discussion about co-survivorship and because I talk about that whenever I do my keynotes and when I do the commencement addresses, but all credit goes to Kristin for really being the driving force of this idea for me and for many others because as a physician, we take care of patients. Our focus is always on the patient. And it really wasn't until this happened to me and my family and Kristin in particular that I started to understand exactly what she's talking about and this idea. And so, Kristin gets a lot of credit for just really bringing that term and that idea to the forefront. Eric Topol (08:09): Yeah, well, you saved his life. It's just not many have that bond. And then the other thing I just want to mention now, you've been recognized by the American Heart Association and a whole bunch of other organizations awarded because of your advocacy for CPR. And you even mentioned that I think in one of your commencement addresses. Will Flanary (08:31): Yeah, I tried to get the crowd to do CPR. Like team up, partner up, and it kind of fell flat. It wasn't quite the right time, I think, to try to do a mass class on CPR. So maybe next time. Eric Topol (08:47): Right. Well, so you had this foundation with the Glaucomflecken General Hospital and taking on 37 specialties and all these incredible people that became part of the family, if you will, of spoof on medicine and your alter ego and these videos that you would do. And sometimes you have three or four different alter egos in there playing out, but now you've branched into new things. So one which is an outgrowth of what we were just talking about. You've been on this country tour, Wife & Death. “Wife and Death,” A Nationwide Tour Kristin Flanary (09:28): Yes. Eric Topol (09:29): Wife and death. I mean, yeah, I guess we can make the connect of how you named it that, but what is it you've been selling out in cities all over the country, and by the way, I'm really upset you haven't come to San Diego, but tell us about wife and death. Will Flanary (09:44): Yeah. Well, we have this amazing story and all these medical challenges we've been through, and then developing the Glaucomflecken brand and universe, and we've done keynotes together for years, and then we thought, let's have more fun with it. Let's do keynotes. They're great. We can get our message out, but sometimes they're just a bit stuffy. It's an academic environment. Kristin Flanary (10:15): They're usually at seven in the morning also, so that's the downside. Will Flanary (10:21): So we thought, let's just put together our own live show. Let's put together something that we could just creatively, we can do whatever we want with it. I could dress up as characters, Kristin, who has these beautiful writing and monologues that she's put together around her experience and just to create something that people can come into a theater and just experience this wide range of emotions from just laughter to tears of all kinds, and just h

Above is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to audio and external citations Eric Topol (00:06): Well, hello it's Eric Topol with Ground Truths, and I'm really delighted to welcome Dr. Rachael Bedard, who is a physician geriatrician in New York City, and is actually much more multidimensional, if you will. She's a writer. We're going to go over some of her recent writings. She's actually quite prolific. She writes in the New Yorker, New York Magazine, New York Times, New York Review of Books. If it has New York in front of it, she's probably writing there. She's a teacher. She works on human rights, civil rights, criminal justice in the prison system. She's just done so much that makes her truly unique. That's why I really wanted a chance to meet her and talk with her today. So welcome, Rachael. Rachael Bedard (00:52): Thank you, Dr. Topol. It's an honor to be here. Eric Topol (00:55): Well, please call me Eric and it's such a joy to have a chance to get acquainted with you as a person who is into so many different things and doing all of them so well. So maybe we'd start off with, because you're the first geriatrician we've had on this podcast. Practicing Geriatrics and Internal Medicine Eric Topol (01:16): And it’s especially apropos now. I wanted maybe to talk about your practice, how you got into geriatrics, and then we'll talk about the piece you had earlier this summer on aging. Rachael Bedard (01:32): Sure. I went into medicine to do social justice work and I was always on a funny interdisciplinary track. I got into the Mount Sinai School of Medicine through what was then called the Humanities and Medicine program, which was an early acceptance program for people who were humanities focused undergrads, but wanted to go into medicine. So I always was doing a mix of politics and activist focused work, humanities and writing, that was always interested in being a doctor. And then I did my residency at the Cambridge Health Alliance, which is a social medicine program in Cambridge, Massachusetts, and my chief residency there. (02:23): I loved being an internist, but I especially loved taking care of complex illness and I especially loved taking care of complex illness in situations where the decision making, there was no sort of algorithmic decision-making, where you were doing incredibly sort of complex patient-centered shared decision making around how to come up with treatment plans, what the goals of care were. I liked taking care of patients where the whole family system was sort of part of the care team and part of the patient constellation. I loved running family meetings. I was incredibly lucky when I was senior resident and chief resident. I was very close with Andy Billings, who was one of the founders of palliative care and in the field, but also very much started a program at MGH and he had come to work at Cambridge Hospital in his sort of semi-retirement and we got close and he was a very influential figure for me. So all of those things conspired to make me want to go back to New York to go to the Sinai has an integrated geriatrics and palliative care fellowship where you do both fellowships simultaneously. So I came to do that and just really loved that work and loved that medicine so much. There was a second part to your question. Eric Topol (03:52): Is that where you practice geriatrics now? Rachael Bedard (03:55): No, now I have ever since finishing fellowship had very unusual practice settings for a geriatrician. So right out of fellowship, I went to work on Rikers Island and then New York City jail system, and I was the first jail based geriatrician in the country, which is a sort of uncomfortable distinction because people don't really like to think about there being a substantial geriatric population in jails. But there is, and I was incredibly lucky when I was finishing fellowship, there was a lot of energy around jail healthcare in New York City and I wrote the guy who was then the CMO and said, do you think you have an aging problem? And he said, I'm not sure, but if you want to come find out, we'll make you a job to come find out. And so, that was an incredible opportunity for someone right out of fellowship. (04:55): It meant stepping off the sort of academic track. But I went and worked in jail for six years and took care of older folks and people with serious illness in jail and then left Rikers in 2022. And now I work in a safety net clinic in Brooklyn that takes care of homeless people or people who have serious sort of housing instability. And that is attached to Woodhull Hospital, which is one of the public hospitals in New York City. And there I do a mix of regular internal medicine primary care, but I preferentially see the older folks who come through, which is a really interesting, painful, complicated patient population because I see a fair amount of cognitive impairment in folks who are living in the shelter system. And that's a really hard problem to address. Frailty, The Aged, and Longevity Eric Topol (05:54): Well, there's a theme across your medical efforts. It seems to me that you look after the neglected folks, the prisoners, the old folks, the homeless people. I mean that's kind of you. It's pretty impressive. And there's not enough of people like you in the medical field. Now, no less do you do that, but of course you are a very impressive author, writer, and of many topics I want to get into with you, these are some recent essays you've written. The one that piqued my interest to start to understand who you were and kind of discover this body of work was the one that you wrote related to aging and President Biden. And that was in New York Times. And I do want to put in a quote because as you know very well, there's so much interest in longevity now. Eric Topol (06:51): Interrupting the aging process, and this one really stuck with me from that op-ed, “Time marches forward, bodies decline, and the growing expectation that we might all live in perfect health until our 100th birthdays reflects a culture that overprizes longevity to the point of delusion.” So maybe if you could tell us, that was a rich piece, you got into frailty, you related it to the issues that were surrounding President Biden who at that time had not withdrawn from the race. But what were you thinking and what are your thoughts about the ability to change the aging process? Rachael Bedard (07:36): I am very interested in, I mean, I'm incredibly interested in the science of it. And so, I guess I think that there are a few things. (07:49): One thing is that the framework that, the part that gives me pause the most is this framework that anything less than perfect health is not a life worth living. So if you're going to have a long life, life should not just be long and sort of healthy in relative terms to your age cohort, but healthy that when you're 80 you should feel like you have the health of a 45-year-old is my understanding of the culture of longevity science. And while I understand why that's aspirational and everybody worry about my body's decline, I think it's a really problematic thing to say that sick bodies are bodies that have disability or people who have cognitive difference are somehow leading lesser lives or lives that are not meaningful or not worth living. I think it's a very, very slippery slope. It puts you in a place where it sort of comes up against another trend or another emerging cultural trend, which is really thinking a lot about physician-assisted suicide and end of life choices. (09:04): And that in some ways that conversation can also be very focused on this idea that there's just no way that it's worth living if you're sick. And that's just not true, I think, and that's not been true for many, many, many of my patients, some of whom have lived with enormous disability and incredible burden of illness, people who are chronically seriously ill and are still leading lives that for them and for the people who love them are filled with meaning. So that's my concern about the longevity stuff. I'm interested in the science around the longevity stuff for sure. I'm interested in, I think we're living in this really interesting moment where there's so much happening across so many of the chronic disease fields where the things that I think have been leading to body decay over the last several decades for the majority of the population, we're sort of seeing a lot of breakthroughs in multiple fronts all at once. And that's really exciting. I mean, that's really exciting. And so, certainly if it's possible to make it to 100 in wonderful health, that's what I'd wish for all of us. But to hold it up as the standard that we have to achieve, I think is both unrealistic and a little myopic. Eric Topol (10:28): Yeah. Well, I certainly agreed with that and I think that that particular essay resonated so well and you really got into frailty and the idea about how it can be potentially prevented or markedly delayed. And I think before we move on to one of those breakthroughs that you were alluding to, any comments about the inevitability of frailty in people who are older, who at some point start to get the dwindles, if you will, what do you have to say about that? Rachael Bedard (11:11): Well, from a clinical standpoint, I guess the caveat versus that not everybody becomes frail and dwindles exactly. Some people are in really strong health up until sort of their final years of life or year of life and then something happens, they dwindle quickly and that's how they die. Or some people die of acute events, but the vast majority of us are going to become more frail in our final decades than we are in our middle decades. And that is the normal sort of pattern of wear and tear on the body. And it is an extraordinary framework

Superimposed on an impressive body of work on the blood-brain-barrier and immune system, Prof Akassoglou and her collaborators just published an elegant study in Nature that centered on the direct binding os the SARS-CoV-2 spike protein to fibrin with marked downstream pro-inflammatory effects. The findings and potential treatments have implications beyond Covid, Long Covid to other neurologic diseases. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to audio and to relevant papers, graphics Eric Topol (00:07): Well, hello this is Eric Topol with Ground Truths, and with me today is Katerina Akassoglou. She is at the Gladstone Institute and she is a remarkable neuroimmunologist who has been doing extraordinary work for three decades to unravel the interactions between the brain, blood vessels and the role of inflammation. So Katerina, there's a lot to discuss, so welcome. Katerina Akassoglou (00:40): Thank you. Thank you so much. It's a great pleasure to join. By Way of Background Eric Topol (00:43): It's really interesting going back in your career. First of all, we're thankful that you immigrated here from Greece, and you have become one of the leading scientists in this discipline of important discipline of neuroimmunology, which is not just about Covid that we're going to talk about, but Alzheimer's and neurodegenerative diseases. This is a really big hot area and you're definitely one of the leaders. And what I was impressed is that all these years that you've been working on the integrity of the blood-brain barrier, the importance of fibrinogen and fibrin, and then comes along the Covid story. So maybe what we can do is start with that, which is you've made your mark in understanding this whole interaction between what can get into the brain, through the blood-brain barrier and incite inflammation. So this has been something that you've really taken to the extreme knowledge base. So maybe we can start with your work there before we get into the important seminal Nature paper that you recently published. Katerina Akassoglou (01:57): Yes, of course. So since very early on, I was still a graduate student when we made the first discovery and at the time was like mid-90s, so it was really ahead of its time. That dysregulation of cytokine expression in the brain of mice was sufficient to induce the whole cascade of events, triggering neurodegeneration, demyelination in pathological alterations, very reminiscent of multiple sclerosis pathology. And it was really hard to publish that study at the time because it was not yet accepted that this regulation of the immune system modeling the brain can be linked to neurodegeneration. So that was 1995 when we made that discovery, and I became really interested, what are the pathogenic triggers that actually polarized the immune cells in the brain? So with this, of course, this transgenic animal was expressing TNF, it was an artificially made animal that we made, but naturally what were the triggers that would polarize the innate immune cells? So I looked really early on in this mice and what I found was that the very first event was leaks of blood-brain barrier. It was opening of the blood-brain barrier in this mouse before inflammation, before demyelination, before neuronal loss. And this is really what shaped the question that, is it possible that these blood leaks that happened very early in the pathology, could this be the instigators of pathogenic inflammation in the brain? Eric Topol (03:34): Yeah. So in a way, you got at this question because of the chicken-and-egg and what happens first, and you got to the temporal saying, which happened first as you said, the leak before you could see evidence of inflammation and being able to study this of course in the experimental model, which you couldn't really do in people. And what I love about the description of your career, which has been quite extraordinary contributions is connecting the dots between the blood, the inflammatory response and the brain. Perhaps no one has done that like you have. And before we get into the recent paper, a lot of people are not aware that a year ago, a group in the UK known as PHOSP-COVID, they published a really important paper in Nature Medicine of over 1,800 people who were hospitalized with Covid and they found that fibrinogen was the best marker for cognitive deficits at 6 and 12 months (Figure below) (04:40): So that's just one of many papers, but it's a particularly well done study that already before you got into this work that recently published had emphasized fibrinogen. And by the way, again, having spent a lot of years in clots in the arteries, for me, we have to just get it down to fibrinogen plus thrombin gets you to fibrin. Okay, so fibrin is a major player here when fibrinogen is cleaved. So here we have the basis that you established, which is the fibrinogen leakage into the brain, activating inflammation, activating microglia, which like the macrophages of the brain and inciting the whole process. And before we close, I want to not just talk about Covid, but Alzheimer's too. But now let's get into the study that you did, [Fibrin drives thromboinflammation and neuropathology in COVID-19] which is striking, I mean really striking. And can you kind of take us through, because you not only demonstrated the importance of fibrin in inciting neuroinflammation in this model, but also how you could reverse it or prevent it. So this, and you looked at it in many different ways, this was a systematic approach. Maybe you can take us through how you were able to make such compelling evidence. The Multimodal Evidence Katerina Akassoglou (06:09): Yes, thank you. First of all, thank you for bringing up the human relevance because this was also our inspiration for the work that we did in the Covid study. So as you mentioned in Covid patients, fibrinogen unbiased mass spec analysis was identified as the predictive biomarker for cognitive impairment in Long Covid patients. And this was in addition to also neuropathology data about the abundance of fibrin deposition in the brain. And these were studies that were done by NIH that have found deposition of fibrin in the brain and the reports for the abnormal and puzzling coagulation in Covid that is not setting other infections and also in many cases not always relating with the severity of symptoms. So even mild cases of Covid also had increased coagulation. I was really intrigued by this human, all this evidence in human data, and I thought that maybe the way that we're thinking about this, that it's systemic inflammation that drives the clotting. (07:24): Maybe there's another aspect to this. Maybe there is a direct effect of the virus with the coagulation cascade, and in this way maybe this can be an instigator of inflammation. So this was the original idea to be able to reconcile this data from the clinic about why do we have this prevalence of coagulopathy in Covid. And of course, the second question is, could this also be a driver of the disease? And of course, we're in a unique position because we have been studying this pathway now for over 20 years to have all the toolbox, the genetic toolbox, the pharmacologic toolbox to be able to actually really address these questions with genetic loss of function studies, with a blood innate immunity multiomics pipeline that we have set up in the lab. And of course, with preclinical pharmacology in our ABSL3 facility. So we had the infrastructure in place and the source in place to actually really dissect this question with both genetic tools as well as also technology platforms. Eric Topol (08:29): And you had in vivo imaging, you're the director of in vivo imaging for Gladstone and UCSF. So you do have the tools to do this. Katerina Akassoglou (08:38): Yes. The imaging that you mentioned is really important because this is, we employed that very early in our studies over now 15 years ago. And the reason was sometimes from snapshots of histopathology, you cannot really understand the sequence of events. So by being able to image these processes, both neuronal activity, microglia activation, infiltration of peripheral cells in the brain, this is how we could see the steps that what happens early on and to be able to answer these chicken-and-egg questions that you mentioned. So these were very, they're very important experiments, especially at the beginning because they were hypothesis driving and we were able to ask the right questions to drive our research program. Eric Topol (09:26): Now was the binding of the spike protein to one key site in fibrinogen, was that known before? [See outstanding Figure below from Trends in Immunology] Katerina Akassoglou (09:36): No, this was not known. So there was evidence that there are abnormal clots in Covid, but it was not known whether the spike protein would directly bind to protein to the coagulation cascade. So one of the key discoveries in our study was to use peptide array mapping and be able to identify not only the binding, but exactly the domains on fibrin that spike binds too. And what we found was two key domains, one the inflammatory domain and the other the plasmin binding site, which is important for fibrin degradation. So this suggested a potential dual deleterious role for this interaction, both by maybe affecting inflammation, but also delaying fibrinolysis, which is the degradation of this toxic protein from the brain. And indeed, we found that this interaction was responsible for all these two aspects, including decreased degradation, more inflammation, but also at the same time increased, increased coagulation. So it was a really pathogenic interaction. Eric Topol (10:47): Yeah, actually it's pretty striking. You have these two sites, the plasmin cleavage site of fibrinogen, which as you say, we knew there was a problem with clots.

When I think of digital biology, I think of Patrick Hsu—he’s the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley. Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Here’s the transcript with links to the audio and external links to relevant papers and things we discussed. Eric Topol (00:06): Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick. Patrick Hsu (00:29): Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric. The Arc Institute Eric Topol (00:33): Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going. Patrick Hsu (01:05): Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives. (02:05): One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs. Patrick, Patrick and Silvana Eric Topol (02:52): So the two Patrick’s. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing? Patrick Hsu (03:08): Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That’s a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused. Eric Topol (04:58): Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right? Patrick Hsu (05:14): I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today. Eric Topol (06:07): Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow? Patrick Hsu (06:24): So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin. New Models of Funding Life Science Eric Topol (06:42): That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they’re both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference. Patrick Hsu (07:26): Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century. (08:38): And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together. (09:42): You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what

Arvind Narayanan and Sayash Kapoor are well regarded computer scientists at Princeton University and have just published a book with a provocative title, AI Snake Oil. Here I’ve interviewed Sayash and challenged him on this dismal title, for which he provides solid examples of predictive AI’s failures. Then we get into the promise of generative AI. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to audio and external links to key publications Eric Topol (00:06): Hello, it's Eric Topol with Ground Truths, and I'm delighted to welcome the co-author of a new book AI SNAKE OIL and it's Sayash Kapoor who has written this book with Arvind Narayanan of Princeton. And so welcome, Sayash. It's wonderful to have you on Ground Truths. Sayash Kapoor (00:28): Thank you so much. It's a pleasure to be here. Eric Topol (00:31): Well, congratulations on this book. What's interesting is how much you've achieved at such a young age. Here you are named in TIME100 AI’s inaugural edition as one of those eminent contributors to the field. And you're currently a PhD candidate at Princeton, is that right? Sayash Kapoor (00:54): That's correct, yes. I work at the Center for Information Technology Policy, which is a joint program between the computer science department and the school of public and international affairs. Eric Topol (01:05): So before you started working on your PhD in computer science, you already were doing this stuff, I guess, right? Sayash Kapoor (01:14): That's right. So before I started my PhD, I used to work at Facebook as a machine learning engineer. Eric Topol (01:20): Yeah, well you're taking it to a more formal level here. Before I get into the book itself, what was the background? I mean you did describe it in the book why you decided to write a book, especially one that was entitled AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference. Background to Writing the Book Sayash Kapoor (01:44): Yeah, absolutely. So I think for the longest time both Arvind and I had been sort of looking at how AI works and how it doesn't work, what are cases where people are somewhat fooled by the potential for this technology and fail to apply it in meaningful ways in their life. As an engineer at Facebook, I had seen how easy it is to slip up or make mistakes when deploying machine learning and AI tools in the real world. And had also seen that, especially when it comes to research, it's really easy to make mistakes even unknowingly that inflate the accuracy of a machine learning model. So as an example, one of the first research projects I did when I started my PhD was to look at the field of political science in the subfield of civil war prediction. This is a field which tries to predict where the next civil war will happen and in order to better be prepared for civil conflict. (02:39): And what we found was that there were a number of papers that claimed almost perfect accuracy at predicting when a civil war will take place. At first this seemed sort of astounding. If AI can really help us predict when a civil war will start like years in advance sometimes, it could be game changing, but when we dug in, it turned out that every single one of these claims where people claim that AI was better than two decades old logistic regression models, every single one of these claims was not reproducible. And so, that sort of set the alarm bells ringing for the both of us and we sort of dug in a little bit deeper and we found that this is pervasive. So this was a pervasive issue across fields that were quickly adopting AI and machine learning. We found, I think over 300 papers and the last time I compiled this list, I think it was over 600 papers that suffer from data leakage. That is when you can sort of train on the sets that you're evaluating your models on. It's sort of like teaching to the test. And so, machine learning model seems like it does much better when you evaluate it on your data compared to how it would really work out in the real world. Eric Topol (03:48): Right. You say in the book, “the goal of this book is to identify AI snake oil - and to distinguish it from AI that can work well if used in the right ways.” Now I have to tell you, it's kind of a downer book if you're an AI enthusiast because there's not a whole lot of positive here. We'll get to that in a minute. But you break down the types of AI, which I'm going to challenge a bit into three discrete areas, the predictive AI, which you take a really harsh stance on, say it will never work. Then there's generative AI, obviously the large language models that took the world by storm, although they were incubating for several years when ChatGPT came along and then content moderation AI. So maybe you could tell us about your breakdown to these three different domains of AI. Three Types of AI: Predictive, Generative, Content Moderation Sayash Kapoor (04:49): Absolutely. I think one of our main messages across the book is that when we are talking about AI, often what we are really interested in are deeper questions about society. And so, our breakdown of predictive, generative, and content moderation AI sort of reflects how these tools are being used in the real world today. So for predictive AI, one of the motivations for including this in the book as a separate category was that we found that it often has nothing to do with modern machine learning methods. In some cases it can be as simple as decades old linear regression tools or logistic regression tools. And yet these tools are sold under the package of AI. Advances that are being made in generative AI are sold as if they apply to predictive AI as well. Perhaps as a result, what we are seeing is across dozens of different domains, including insurance, healthcare, education, criminal justice, you name it, companies have been selling predictive AI with the promise that we can use it to replace human decision making. (05:51): And I think that last part is where a lot of our issues really come down to because these tools are being sold as far more than they're actually capable of. These tools are being sold as if they can enable better decision making for criminal justice. And at the same time, when people have tried to interrogate these tools, what we found is these tools essentially often work no better than random, especially when it comes to some consequential decisions such as job automation. So basically deciding who gets to be called on the next level of like a job interview or who is rejected, right as soon as they submit the CV. And so, these are very, very consequential decisions and we felt like there is a lot of snake oil in part because people don't distinguish between applications that have worked really well or where we have seen tremendous advances such as generative AI and applications where essentially we've stalled for a number of decades and these tools don't really work as claimed by the developers. Eric Topol (06:55): I mean the way you partition that, the snake oil, which is a tough metaphor, and you even show the ad from 1905 of snake oil in the book. You're really getting at predictive AI and how it is using old tools and selling itself as some kind of breakthrough. Before I challenge that, are we going to be able to predict things? By the way, using generative AI, not as you described, but I would like to go through a few examples of how bad this has been and since a lot of our listeners and readers are in the medical world or biomedical world, I'll try to get to those. So one of the first ones you mentioned, which I completely agree, is how prediction of Covid from the chest x-ray and there were thousands of these studies that came throughout the pandemic. Maybe you could comment about that one. Some Flagrant Examples Sayash Kapoor (08:04): Absolutely. Yeah, so this is one of my favorite examples as well. So essentially Michael Roberts and his team at the University of Cambridge a year or so after the pandemic looked back at what had happened. I think at the time there were around 500 studies that they included in the sample. And they looked back to see how many of these would be useful in a clinical setting beyond just the scope of writing a research paper. And they started out by using a simple checklist to see, okay, are these tools well validated? Does the training and the testing data, is it separate? And so on. So they ran through the simple checklist and that excluded all but 60 of these studies from consideration. So apart from 60 studies, none of these other studies even passed a very, very basic criteria for being included in the analysis. Now for these 60, it turns out that if you take a guess about how many were useful, I'm pretty confident most cases would be wrong. (09:03): There were exactly zero studies that were useful in a clinically relevant setting. And the reasons for this, I mean in some cases the reasons were as bizarre as training a machine learning model to predict Covid where all of the positive samples of people who had Covid were from adults. But all of the negative samples of people who didn't have Covid were from children. And so, essentially claiming that the resulting classifier can predict who has Covid is bizarre because all the classifier is doing is looking at the checks history and basically predicting which x-ray belongs to a child versus an adult. And so, this is the sort of error in some cases we saw duplicates in the training and test set. So you have the same person that is being used for training the model and that it is also used for evaluating the model. So simply memorizing a given sample of x-rays would be enough to achieve a very high performance. And so, for issues like these, I think all 60 of these studies prove to be not useful in a clinically relevant setting. And I think this is sort of

Francis Collins is a veritable national treasure. He directed the National Institutes of Health from 2009 to 2021. Prior to that he led the National Human Genetics Research Institute (NHGRI) from 1997-2009, during which the human genome was first sequenced. As a physician-scientist, he has made multiple seminal discoveries on the genetic underpinnings of cystic fibrosis, Huntington’s disease, neurofibromatosis, progeria, and others. This brief summary is barely scratching the surface oh his vast contributions to life science and medicine. A video clip from our conversation on hepatitis C. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with external inks and links to audio Eric Topol (00:06): Well, I am really delighted to be able to have our conversation with Francis Collins. This is Eric Topol with Ground Truths and I had the chance to first meet Francis when he was on the faculty at the University of Michigan when I was a junior faculty. And he gave, still today, years later, we're talking about 40 years later, the most dazzling Grand Rounds during his discovery of cystic fibrosis. And Francis, welcome, you inspired me and so many others throughout your career. Francis Collins (00:40): Well, Eric, thank you and you've inspired me and a lot of other people as well, so it's nice to have this conversation with you in the Ground Truths format. Eric Topol (00:49): Well, thank you. We're at the occasion of an extraordinary book you put together. It's the fifth book, but it stands out quite different from the prior books as far as I can tell. It's called The Road to Wisdom: On Truth, Science, Faith and Trust, these four essential goods that build upon each other. And it's quite a book, Francis, I have to say, because you have these deep insights about these four critical domains and so we'll get into them. But I guess the first thing I thought I'd do is just say, how at some point along the way you said, “the goal of this book is to turn the focus away from hyperpartisan politics and bring it back to the most important sources of wisdom: truth, science, faith and trust, resting upon a foundation of humility, knowledge, morality, and good judgment.” So there's a lot there. Maybe you want to start off with what was in the background when you were putting this together? What were you really aiming at getting across? Reflections on Covid Francis Collins (02:06): I'm glad to, and it's really a pleasure to have a chance to chat with you about this. I guess before Covid came along, I was probably a bit of a naive person when it came to how we make decisions. Yeah, I knew there were kind of wacky things that had gone out there from time to time, but I had a sort of Cartesian attitude that we were mostly rational actors and when presented with evidence that's been well defended and validated that most people will say, okay, I know what to do. Things really ran off the rails in the course of Covid. It was this remarkable paradox where, I don't know what you would say, but I would say the development of the vaccines that were safe and highly effective in 11 months using the mRNA platform was one of the most stunning achievements of science in all of history up until now. Francis Collins (03:02): And yet 50 million Americans decided they didn't want any part of it because of information that came to them that suggested this was not safe or there was conspiracies behind it, or maybe the syringes had chips that Bill Gates had put in there or all manner of other things that were being claimed. And good honorable people were distracted by that, lost their trust in other institutions like the CDC, maybe like the government in general like me, because I was out there a lot trying to explain what we knew and what we didn't know about Covid. And as a consequence of that, according to Kaiser Family Foundation, more than 230,000 people died between June of 2021 and April of 2022 because of a decision to reject the opportunity for vaccines that were at that time free and widely available. That is just an incredibly terribly tragic thing to say. Francis Collins (04:03): More than four times the number of people who died, Americans who died in the Vietnam War are in graveyards unnecessarily because we lost our anchor to truth, or at least the ability to discern it or we couldn't figure out who to trust while we decided science was maybe not that reliable. And people of faith for reasons that are equally tragic were among those most vulnerable to the misinformation and the least likely therefore, to take advantage of some of these lifesaving opportunities. It just completely stunned me, Eric, that this kind of thing could happen and that what should have been a shared sense of working against the real enemy, which was the SARS-CoV-2 virus became instead a polarized, divisive, vitriolic separation of people into separate camps that were many times driven more by politics than by any other real evidence. It made me begin to despair for where we're headed as a country if we can't figure out how to turn this around. Francis Collins (05:11): And I hadn't really considered it until Covid how serious this was and then I couldn't look away. And so, I felt if I have a little bit of credibility after having stepped down after 12 years as the NIH Director and maybe a chance to influence a few people. I just have to try to do something to point out the dangers here and then to offer some suggestions about what individuals can do to try to get us back on track. And that's what this book is all about. And yeah, it's called The Road to Wisdom because that's really how I want to think of all this in terms of truth and science and faith and trust. They all kind of give you the opportunities to acquire wisdom. Wisdom is of course knowledge, but it's not just knowledge, it's also understanding it has a moral character to it. It involves sophisticated judgment about difficult situations where there isn't an obvious answer. We need a lot more of that, it seems we’re at short supply. Deconvoluting Truth Eric Topol (06:13): Well, what I really loved about the book among many things was how you broke things down in just a remarkably thoughtful way. So truth, you have this great diagram like a target with the four different components. in the middle, necessary truth. And then as you go further out, firmly established facts, then uncertainty and then opinion, and truth is not a dichotomous by any means. And you really got that down and you explained each of these different facets of truth with great examples. And so, this among many other things that you broke down, it wasn't just something that you read somewhere, you really had to think this through and perhaps this experience that we all went through, but especially you. But because you bring so much of the book back to the pandemic at times with each of the four domains, so that and the spider web. The spider web of where your core beliefs are and then the ones further out on the web and you might be able to work on somebody out further periphery, but it's pretty hard if you're going to get to them in the middle where their main thing is science is untrustworthy or something like that. Eric Topol (07:36): So how did you synthesize these because the graphics are quite extraordinary? Francis Collins (07:44): Well, I will say the artist for the graphics is a remarkable graphic design student at the University of Michigan who happens to be my granddaughter. So it was nice having that ability to have my scratches turned into something actually looks like artwork. The concepts I got to say, Eric, I was feeling pretty unsure of myself. I never took a course in philosophy. I know there are people who've spent their entire careers going all the way back to Socrates and on up until now about what does truth mean and here's this scientist guy who's trying to say, well, let me tell you what I think about it. I'm glad to hear that you found these circles useful. They have been very useful for me and I hadn't thought about it much until I tried to put it in some sort of framework and a lot of the problems we have right now where somebody says, well, that might be true for you, but it's not true for me, that's fine if you're talking about an opinion, like whether that movie was really good or not. Francis Collins (08:43): But it's not fine if it's about an established fact, like the fact that climate change is real and that human activity is the main contributor to the fact that we've warmed up dramatically since 1950. I'm sorry, that's just true. It doesn't care how you feel about it, it's just true. So that zone of established facts is where I think we have to re-anchor ourselves again when something's in that place. I'm sorry, you can't just decide you don't like it, but in our current climate and maybe postmodernism has crept in all kinds of ways we're not aware of, the idea that there is such a thing as objective truth even seems to be questioned in some people's minds. And that is the path towards a terrible future if we can't actually decide that we have, as Jonathan Rauch calls it, a constitution of knowledge that we can depend on, then where are we? Eric Topol (09:37): Well, and I never heard of the term old facts until the pandemic began and you really dissect that issue and like you, I never had anticipated there would be, I knew there was an anti-science, anti-vaccine sector out there, but the fact that it would become so strong, organized, supported, funded, and vociferous, it's just looking back just amazing. I do agree with the statement you made earlier as we were talking and in the book, “the development of mRNA vaccines for Covid in record time as one of the greatest medical achievements in human history.” And you mentioned besides the Kaiser Family Foundation, but the Commonwealth Fund, a bipartisan e

Professor Joseph Allen directs the Healthy Buildings Program at Harvard Chan School of Public Health. His expertise extends far beyond what makes buildings healthy. He has been a leading voice and advocate during the Covid pandemic for air quality and ventilation. He coined the term “Forever Chemicals” and has written extensively on this vital topic, no less other important exposures, which we covered In our wide-ranging conversation. You will see how remarkably articulate and passionate Prof Allen is about these issues, along with his optimism for solutions. A video snippet of our conversation: buildings as the 1st line of defense vs respiratory pathogens. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with External Links and Links to Audio Eric Topol (00:00:06): Well, hello. It's Eric Topol from Ground Truths and I am just delighted to have with me, Joseph Allen from the Harvard School of Public Health, where he directs the Healthy Buildings Program that he founded and does a whole lot more that we're going to get into. So welcome, Joe. Joseph Allen (00:00:24): Thanks. It's great to be here. I appreciate the invitation. Joe Allen’s Background As A Detective Eric Topol (00:00:28): Well, you have been, as I've learned, rocking it for many years long before the pandemic. There's quite a background about you having been a son of a homicide detective, private eye agency, and then you were going to become an FBI agent. And the quote from that in the article that's the Air Investigator is truly a classic. Yeah, you have in there, “I guarantee I'm the only public health student ever to fail an FBI lie detector polygraph in the morning and start graduate school a few hours later.” That's amazing. That's amazing. Joseph Allen (00:01:29): All right. Well, you've done your deep research apparently. That's good. Yeah, my dad was a homicide detective and I was a private investigator. That's no longer my secret. It's out in the world. And I switched careers and it happened to be the day I took the polygraph at the FBI headquarters in Boston, was the same day I started graduate studies in public health. Sick vs Healthy Buildings (Pre-Covid) Eric Topol (00:01:53): Well, you're still a detective and now you're a detective of everything that can hurt us or help us environmentally and my goodness, how grateful we are that you change your career path. I don't know anyone who's had more impact on buildings, on air, and we're going to get into chemicals as well. So if we go back a bit here, you wrote a book before the pandemic, talk about being prescient. It’s called Healthy Buildings: How Indoor Spaces Can Make You Sick - or Keep You Well with John Macomber, your co-author. What was it that gave you the insight to write a book before there was this thing called Covid? Joseph Allen (00:02:41): Yeah, well, thanks for making the connection too, my past career to current career. For many years, I thought there wasn't a connection, but I agree. There's actually a lot of similarities and I also am really appreciative. I am lucky I found the field of Public Health, it's clearly where I belong. I feel like I belong here. It's a place to make an impact that I want to make in my career. So yeah, the Healthy Buildings book, we started writing years before the pandemic and was largely motivated by, I think what you and others and other people in my field have known, is that buildings have an outsized impact on our health. Yet it's not something that comes to the forefront when you ask people about what matters for their health. Right, I often start presentations by asking people that, what constitutes healthy living? They'll say, I can't smoke, I have to eat well. (00:03:30): I have to exercise. Maybe they'll say, outdoor pollution’s bad for you. Very few people, if any, will say, well, the air I breathe inside my building matters a lot. And over the years I had started my public health career doing forensic investigations of sick buildings. People really can get sick in buildings. It can be anything from headaches and not being able to concentrate all the way to cancer clusters and people dying because of the building. And I've seen this in my career, and it was quite frustrating because I knew, we all knew how to design and operate buildings in a way that can actually keep people healthy. But I was frustrated like many in my field that it wasn't advancing. In other words, the science was there, but the practice wasn't changing. We were still doing things the wrong way around ventilation, materials we put in our building, and I would lecture over and over and give presentations and I decided I want to try something new. (00:04:22): I do peer-reviewed science. That's great. I write pieces like you for the public, and I thought we'd try a longer form piece in a book, and it's published by Harvard Press. John Macomber for those who know is a professor at Harvard Business School who's an expert in real estate finance. So he'd been talking about the economic benefits of healthier buildings and some hand waving as he describes around public health. I've been talking about the public health benefits and trying to wave an economic argument. We teamed up to kind of use both of our strengths to, I hope make a compelling case that buildings are good for health and they're also just good business. In other words, try to break down as many barriers as we can to adoption. And then the book was published right as Covid hit. Indoor Air Quality and Cognition Eric Topol (00:05:05): Yeah. I mean, it's amazing. I know that typically you have to have a book almost a year ahead to have it in print. So you were way, way ahead of this virus. Now, I'm going to come back to it later, but there were two things beyond the book that are pretty striking about your work. One is that you did all these studies to show with people wearing sensors to show that when the levels of CO2 were high by sensors that their cognition indoors was suffering. Maybe you could just tell us a little bit about these sensors and why aren't we all wearing sensors so that we don't lose whatever cognitive power that we have? Joseph Allen (00:05:56): Well, yeah. First I think we will start having these air quality sensors. As you know, they're starting to become a lot more popular. But yeah, when I first joined the faculty full-time at Harvard, one of the first studies I conducted with my team was to look at how indoor air quality influences cognitive function. And we performed a double-blind study where we took people, office workers and put them in a typical office setting. And unbeknownst to them, we started changing the air they were breathing in really subtle ways during the day, so they didn't know what we were doing. At the end of the day, we administered an hour and a half long cognitive function battery, and like all studies, we control for things like caffeine intake, baseline cognitive performance, all the other factors we want to account for. And after controlling for those factors in a double-blind study, we see that indoor air quality, minor improvements to indoor air quality led to dramatic increases in cognitive function test scores across domains that people recognize as important for everyday life. (00:06:59): How do you seek out and utilize information? How do you make strategic decisions? How do you handle yourself during a crisis and importantly recover after that crisis? I don't mean the world's ending crisis. I mean something happens at work that's stressful. How do you handle that and how do you respond? Well, it turns out that amongst all the factors that influence how we respond there, indoor air quality matters a lot. We call that study the COGfx Study for cognitive function. We replicated it across the US, we replicated it across the world with office workers around the world, and again, always showing these links, the subtle impact of indoor air quality on cognitive function performance. Now, that also then starts to be the basis for some of the economic analysis we perform with my colleague at Harvard Business School. We say, well, look, if you perform this much better related to air quality, what would happen if we implemented this at scale in a business? (00:07:51): And we estimate that there are just massive economic gains to be had. On a per person basis, we found and published on this, that's about $6,000 to $7,000 per person per year benefit across a company. It could lead to 10% gains to the bottom line performance of the company. And again, I'm a public health professor. My goal is to improve people's health, but we add a lens, mental health, brain health is part of health, and we add the economic lens to say, look, this is good for a worker of productivity and the costs are downright trivial when you compare it against the benefits, even just including the cognitive function benefits, not even including the respiratory health benefit. Eric Topol (00:08:33): And I mean, it's so striking that you did these studies in a time before sensors were, and they still are not widely accepted, and it really helped prove, and when we start to fall asleep in a group session indoors, it may not just be because we didn't have enough sleep the night before, right. Joseph Allen (00:08:56): It's funny you say that. I talk about that too. It's like, do we actually need the study to tell us to quantify what we've all experienced these bad conference rooms, you get tired, you can't concentrate, you get sleepy while you're driving your car. Yeah, a whole bunch of other factors. Maybe the speaker's boring, but a key factor is clearly indoor air quality and things like good ventilation, the chemical load in the space are all contributing. Eric Topol (00:09:20): Yeah. No, it's pretty darn striking. Now we're going to get into the pandemic, and this of course is when your work fi

Steve Horvath made the seminal discovery of the—Horvath Clock— an epigenetic clock based on DNA methylation, which is now being used extensively in medical research and offered commercially for individuals (←we talk about that!). He was on the faculty at UCLA from 2000-2022 as a Professor of Human Genetics and Biostatistics, and now works on anti-aging research at Altos Labs. A perspective on the importance of epigenetic clocks this week’s Nature”This insight is crucial for deriving reliable biological markers of ageing in tissues or blood. Such a feat has been accomplished through the ingenious identification of epigenetic clocks in our genome. But these insights are even more important for revealing targets that enable intervention in the ageing process.” A video snippet on vegetable intake and epigenetic clocks. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to Audio and External Links Eric Topol (00:06): Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath. He's a geneticist, a statistician, a mathematician. He's got a lot of background that has led to what is a landmark finding in biomedicine, the Horvath clock. So Steve, welcome. Steve Horvath (00:30): Thank you for having me. Eric Topol (00:33): Well, it's really fascinating. I followed your work for well over a decade since you introduced the pan-tissue clock in 2013, and it's fascinating to go back a bit on that finding, which initially, I guess was in saliva a couple of years prior, and then you found it everywhere you looked, wherever cells had a nucleus and tissues. And what gave you the sense that these markers of methylation on the DNA would give us some clues about the aging process? How did you even come about to make this discovery? Serendipity Steve Horvath (01:17): It was an accidental discovery because before the methylation clock, I had worked very hard on a gene expression clock, a transcriptomic biomarker. I mean, I was at the height of my energy levels. I worked really on weekends, really eight hour days during the week. But all the weekends I had collected a large set of gene expression data and I dredged the data. And for two years and I couldn't get anywhere, there was nothing I could do. But nowadays, of course, you see various publications where people built transcriptomic clocks. But back in the day when we had these arrays, I just couldn't see a signal. And then at some point I got roped into a study of homosexuality where my collaborator at UCLA wanted to see whether there's an epigenetic correlate of sexual orientation in saliva. And so yeah, being a biostatistician, I said, sure, I analyzed the data and I couldn't find any signal for homosexuality. (02:48): But then I just looked for an aging signal in the same, and really within an hour of analyzing the data, I knew that I have to completely drop gene expression. I need to go after methylation. And the signal is so profound, and as you said initially we looked at saliva samples and we thought, isn't it curious? You spit in a cup and you can measure someone's age. And we were of course, hoping that this could become a valuable readout of biologic age, but it took, of course, many years to realize that potential. Nowadays, there's several companies that offer a saliva based methylation clock test. But yeah, many years passed, and it was important to fill in the details and to build the case that methylation clocks are predictive of things we care about time to death or time to various forms of morbidity. So it took many, many years to analyze large cohort studies and to accumulate the evidence that it actually works. Eric Topol (04:16): Yeah, I mean, it was pretty amazing back almost a decade ago when I would see, we would take tissue or blood sample and look at your clock and it would say, age of the person is 75 years. And then we look at the actual age of the person who is 75 years to say, wait a minute, how can this be? So I mean, the plausibility of this discovery, if you look back, I mean you say, well, this is just kind of the rust of the pipes, or how do you process that the methylation is such a marker potentially of a person's biologic age? Of course, we're going to get into how it could be a way to intervene to change the aging process. But would it be fair to say that its epigenetic clocks are not the same as biologic aging or how do you put all that together? Epigenetic Age vs Biologic Age Steve Horvath (05:21): Yes, for sure. An epigenetic age estimate is certainly not the same as a biologic age estimate. And the reason why I say it is because biologic age is really determined by so many things and by so many organs. And as I mentioned initially, we had a clock for saliva later for blood and so on. And so, if you only have an epigenetic readout of a certain cell type, it's really too limited to assess the whole organismal state. And arguably you would want to measure also proteomics, readouts and many other data modalities. So I typically avoid the terminology biologic age, because to begin with, we don't have a definition of it. Decades of discussions, nobody really has a precise definition of it. Second Generation Epigenetic Clocks Eric Topol (06:35): Well, from the first generation Horvath clock then became this newer second generation, GrimAge, PhenoAge, the DunedinPACE of aging. How has that helped to advance the field? Because as you touched on, they're measuring different things and what is it meant by kind of a second generation clock? Steve Horvath (07:03): Yeah, so a second generation clock truly aims to predict mortality or morbidity risk. As opposed to simply chronologic age or what is known as calendar age. And fortunately, there's no doubt that the second generation clocks can do that. I often finish a talk on GrimAge by telling the audience that I give them a money back guarantee, that it will be predictive of mortality in their cohort study. I'm 100% certain that it works if you analyze a hundred people or so. The question is more whether an individual could benefit from such a test. And there are now many providers of various epigenetic clock tests. These biomarkers have different names, but they're quite pricey. A couple of hundred dollars are needed to get such a measurement. And the question is, is it helpful for the individual should you get such a test? And I would say we are not quite there yet for a variety of reasons. The main reason being we don't have good interventions against accelerated epigenetic age. So because when you think about it, why does a doctor order a test for you? For example, cholesterol levels. Well, because they have a drug against elevated cholesterol levels, the statin. And at the moment, we don't have validated interventions against accelerated epigenetic age. So that's kind of missing. Eric Topol (09:13): Yeah, we're going to get to that because obviously a lot of things are in the pipeline there, but are you saying then that these people that are getting these consumer tests, that they're getting a test that really wasn't validated at an individual level, so it predicts their mortality that it may be good at a cohort or population level, but maybe it's not so helpful, accurate, or would you say it is accurate? I mean, GrimAge is a good name because since it says when you're going to die. How do you make the differentiation between the individual level or beyond? Steve Horvath (09:59): Yeah, I think it's good to compare to other biomarkers. So take glucose levels, hemoglobin A1C, nobody doubts that these levels predict mortality risk when you study couples a hundred people. But how accurate is such a test for an individual? Clearly there is substantial noise associated with a prediction. Two people could have exactly the same hemoglobin A1C levels, but live very different lifespans. And the same holds for epigenetic clocks. They do predict how long you live. In theory, one could arrive at an estimate of age and death. There's a complicated mathematical formula that allows you to do that, but there would be a substantial error bar associated with it, an order of magnitude plus minus five years. And so, for the individual, such an estimate is not that important because the error bar is substantial. But I want to add that these second generation clocks, they do predict mortality risk. There's no question. Maximal Lifespan Eric Topol (11:35): Well, as you know, the longevity space is now very crowded with all sorts of clubs, and it's like a circus out there. And some of these things are being promoted that really don't have the basis or have a false sense to consumers who want to live forever and be healthy forever. But maybe these markers are not really helping guide them so much. Now, you recently published you and your group a fascinating paper, so getting away from the individual for a second, but now at the species level and in Science Advances, and we'll put this diagram with the podcast, but you looked at 348 mammal species for the maximal lifespan with DNA methylation. And it was amazing to see the display from the desert hamster all the way to the humpback whale with somewhere along the way, the humans. So you could predict maximal lifespan pretty well, right? Steve Horvath (12:43): Yes. So I collected this very large dataset over seven years, and one of the reasons was to understand the mystery of maximum lifespan. The bowhead whale can live over 211 years, whereas certain mice only three or four years. And my question was, can methylation teach us something about maximum lifespan? And the answer is a resounding, yes. The methylation profiles very much predict the maximum lifespan of a species. And maybe to use a metaphor to explain the patterns. So one can visualize methylation around the DNA molecule, like a landscape. You want that certain regi

Pradeep is a brilliant geneticist and Director of Preventive Cardiology, holds the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine at Mass General Hospital and on faculty at Harvard Medical School and the Broad Institute. His prolific research has been illuminating for the field of improving our approach to reduce the risk of heart disease. That’s especially important because heart disease is the global (and US) #1 killer and is on the increase. We didn’t get into lifestyle factors here since there was so much ground to cover on new tests. drugs, and strategies. A video snippet of our conversation on ApoB. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to key publications and audio Eric Topol (00:06): Well, welcome to Ground Truths. I'm Eric Topol and with me is Pradeep Natarajan from Harvard. He's Director of Preventative Cardiology at the Mass General Brigham Health System and he has been lighting it up on the field of cardiovascular. We're going to get to lots of different parts of that story and so, Pradeep welcome. Pradeep Natarajan (00:31): Thanks Eric, really delighted and honored to be with you and have this discussion. Eric Topol (00:36): Well, for years I've been admiring your work and it's just accelerating and so there's so many things to get to. I thought maybe what we'd start off with is you recently wrote a New England Journal piece about two trials, two different drugs that could change the landscape of cardiovascular prevention in the future. I mean, that's one of the themes we're going to get to today is all these different markers and drugs that will change cardiology as we know it now. So maybe you could just give us a skinny on that New England Journal piece. Two New Lipid Targets With RNA Drugs Pradeep Natarajan (01:16): Yeah, yeah, so these two agents, the trials were published at the same time. These phase two clinical trials for plozasiran, which is an siRNA against APOC3 and zodasiran, which is an siRNA against ANGPTL3. The reason why we have medicines against those targets are based on human genetics observations, that individuals with loss of function mutations and either of those genes have reduced lipids. For APOC3, it's reduced triglycerides for ANGPTL3 reduced LDL cholesterol and reduced triglycerides and also individuals that have those loss of function mutations also have lower risk for coronary artery disease. Now that's a very similar parallel to PCSK9. We have successful medicines that treat that target because people have found that carriers of loss of function mutations in PCSK9 lead to lower LDL cholesterol and lower coronary artery disease. (02:11): Now that suggests that therapeutic manipulation without significant side effects from the agents themselves for APOC3 and ANGPTL3 would be anticipated to also lower coronary artery disease risk potentially in complementary pathways to PCSK9. The interesting thing with those observations is that they all came from rare loss of function mutations that are enriched in populations of individuals. However, at least for PCSK9, has been demonstrated to have efficacy in large groups of individuals across different communities. So the theme of that piece was really just the need to study diverse populations because those insights are not always predictable about which communities are going to have those loss of function mutations and when you find them, they often have profound insights across much larger groups of individuals. Eric Topol (03:02): Well, there's a lot there that we can unpack a bit of it. One of them is the use of small interfering RNAs (siRNA) as drugs. We saw in the field of PCSK9, as you mentioned. First there were monoclonal antibodies directed against this target and then more recently, there’s inclisiran which isn't an RNA play if you will, where you only have to take it twice a year and supposedly it's less expensive and I’m still having trouble in my practice getting patients covered on their insurance even though it's cheaper and much more convenient. But nonetheless, now we're seeing these RNA drugs and maybe you could comment about that part and then also the surprise that perhaps is unexplained is the glucose elevation. Pradeep Natarajan (03:53): Yeah, so for medicines and targets that have been discovered through human genetics, those I think are attractive for genetic-based therapies and longer interval dosing for the therapies, which is what siRNAs allow you to do because the individuals that have these perturbations, basically the naturally occurring loss of function mutations, they have these lifelong, so basically have had a one-time therapy and have lived, and so far, at least for these targets, have not had untoward side effects or untoward phenotypic consequences and only reduce lipids and reduce coronary artery disease. And so, instead of taking a pill daily, if we have conviction that that long amount of suppression may be beneficial, then longer interval dosing and not worrying about the pill burden is very attractive specifically for those specific therapeutics. And as you know, people continue to innovate on further prolonging as it relates to PCSK9. (04:57): Separately, some folks are also developing pills because many people do feel that there's still a market and comfort for daily pills. Now interestingly for the siRNA for zodasiran at the highest dose, actually for both of them at the highest doses, but particularly for zodasiran, there was an increase in insulin resistance parameters actually as it relates to hyperglycemia and less so as it relates to insulin resistance, that is not predicted based on the human genetics. Individuals with loss of function mutations do not have increased risks in hyperglycemia or type 2 diabetes, so that isolates it related to that specific platform or that specific technology. Now inclisiran, as you'd mentioned, Eric is out there. That's an siRNA against PCSK9 that's made by a different manufacturer. So far, the clinical trials have not shown hyperglycemia or type 2 diabetes as it relates inclisiran, so it may be related to the specific siRNAs that are used for those targets. That does merit further consideration. Now, the doses that the manufacturers do plan to use in the phase three clinical trials are at lower doses where there was not an increase in hyperglycemia, but that does merit further investigation to really understand why that's the case. Is that an expected generalized effect for siRNAs? Is it related to siRNAs for this specific target or is it just related to the platform used for these two agents which are made by the same manufacturer? Eric Topol (06:27): Right, and I think the fact that it's a mystery is intriguing at the least, and it may not come up at the doses that are used in the trials, but the fact that it did crop up at high doses is unexpected. Now that is part of a much bigger story is that up until now our armamentarium has been statins and ezetimibe to treat lipids, but it's rapidly expanding Lp(a), which for decades as a cardiologist we had nothing to offer. There may even be drugs to be able to lower people who are at high risk with high Lp(a). Maybe you could discuss that. What About Lp(a)? Pradeep Natarajan (07:13): Yeah, I mean, Eric, as you know, Lp(a) has been described as a cardiovascular disease risk factors for quite so many years and there are assays to detect lipoprotein(a) elevation and have been in widespread clinical practice increasing widespread clinical practice, but we don't yet have approved therapies. However, there is an abundance of literature preclinical data that suggests that it likely is a causal factor, meaning that if you lower lipoprotein(a) when elevated, you would reduce the risk related to lipoprotein(a). And a lot of this comes from similar human genetic studies. The major challenge of just relating a biomarker to an outcome is there are many different reasons why a biomarker might be elevated, and so if you detect a signal that correlates a biomarker, a concentration to a clinical outcome, it could be related to that biomarker, but it could be to the other reasons that the biomarker is elevated and sometimes it relates to the outcome itself. (08:10): Now human genetics is very attractive because if you find alleles that strongly relate to that exposure, you can test those alleles themselves with the clinical outcome. Now the allele assignment is established at birth. No other factor is going to change that assignment after conception, and so that provides a robust, strong causal test for that potential exposure in clinical outcome. Now, lipoprotein(a) is unique in that it is highly heritable and so there are lots of different alleles that relate to lipoprotein(a) and so in a well powered analysis can actually test the lipoprotein(a) SNPs with the clinical outcomes and similar to how there is a biomarker association with incident myocardial infarction and incident stroke, the SNPs related to lipoprotein(a) show the same. That is among the evidence that strongly supports that this might be causal. Now, fast forward to many years later, we have at least three phase three randomized clinical trials testing agents that have been shown to be very potent at lowering lipoprotein(a) that in the coming years we will know if that hypothesis is true. Importantly, we will have to understand what are the potential side effects of these medicines. There are antisense oligonucleotides and siRNAs that are primarily in investigation. Again, this is an example where there's a strong genetic observation, and so these genetic based longer interval dosing therapies may be attractive, but side effects will be a key thing as well too. Those things hard to anticipate really can anticipate based on the human genetics for off target effects, for example. (09:52): It's clearly a risk signal and hop

A video snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Shane Crotty: A Landmark Study on Upper Airway Mucosal Immunity Transcript This is the first time a Ground Truths podcast is being posted simultaneous with a new publication, this one in Nature, by Professor Shane Crotty and his colleagues at La Jolla Institute for Immunology. Shane is one of the leading immunologists and virologists in the country; he and his group published in 2020 the first detailed analysis for how our immune system responds to SARS-CoV-2. Shane also, among many other notable contributions during COVID, illuminated the role of hybrid immunity vs COVID, the differences between and additivity of vaccination and infection. Today’s paper in Nature is indeed a landmark contribution doing something that hasn’t been done before—to understand the underpinnings of mucosal immunity of the upper airway. 100 participants had monthly nasal and nasopharyngeal swabs throughout the pandemic. With a median of >100,000 cells per swab recovered, they undertook single-cell sequencing and full characterization of the cells (tissue-resident memory B cells, CD4+ and CD8+ T cells, germinal center follicular helper T cells and B cells, etc.) to determine optimal immune protection of the upper airway, the effect of infections by different variants, breakthrough infections, vaccination, and age. Here is the transcript of our conversation about the new report with links to the audio: Eric Topol (00:06): Hello, it's Eric Topol with Ground Truths, and with me today is Professor Shane Crotty from the La Jolla Institute of Immunology (LJI), not too far away from where I work at Scripps. And Shane has been a go-to immunologist colleague here in the Mesa, and he and his colleagues were the ones that really first published the response to SARS-CoV-2 as far as the immunologic response. And today we're doing something very unique. We're going to go over for the first time in the two year plus history of Ground Truths, going to have a publication with at least simultaneous or near simultaneous podcast. Shane, welcome and congratulations on this really important paper in Nature. Shane Crotty (00:57): Thanks, Eric. Thanks for having me. Yeah, somebody asked if I was going to go over to Scripps for the podcast and I was like, yeah, we could. Eric Topol (01:06): You could. You could. But no, it's good. And it's nice having the logo of this great institute you work at right in the right corner. And you've done so many contributions with your colleagues at La Jolla Institute. It's really a privilege to have a chance to learn from you and particularly about what we're going to talk about today, which is mucosal immunity to upper airway infections, which is especially germane to COVID. And we're actually in the middle of a significant wave of COVID right now. And I guess it would maybe be fair to say, Shane, that we've never truly understood the underpinnings, the real details of upper airway mucosal immunity. Is that a fair statement? Shane Crotty (01:53): Yeah, it is a fair statement. Eric Topol (01:56): Okay. So today we're going to crack the case. This paper from you and your colleagues, of course, you're the senior author and first author, Sydney Ramirez did a remarkable study. I mean, just extraordinary. This is why we're doing a special podcast about it. Maybe you could just kind of give us the overview of the design because you were doing things that haven't been done before. Shane Crotty (02:24): Sure. And, I would say the genesis even of it goes back to what you were introducing. I mean, during the pandemic, we like a lot of scientists spent a lot of time and energy trying to help understanding immune responses to this virus, and immune memory to this virus, and what was involved in protective immunity. And we're certainly proud of the work that we did. And it was hard work. And after a while we were exhausted and we stopped. Shane Crotty (02:59): And then we came back to it after a while and said, well, the virus is still here. And so many people have contributed so much to better understanding the virus and creating vaccines. But there are clearly still things we don't understand. What are those biggest knowledge gaps and where might we be able to contribute? And really to me the biggest one was location, location, location. This is a virus that infects your nose, infects your upper airway—your nose, and throat, and oral cavity. And then obviously if you get severe disease, the severe disease and death are from the lungs. And it's just been a big knowledge gap in terms of understanding what actually occurs in those tissues immunologically and what is associated with protective immunity or what could be associated with protective immunity. And sort of looking forward what might be helpful for mucosal vaccine development from things that we could learn. Shane Crotty (04:12): So we started from what we would call the basics, and what does immune memory look like in the upper airways in normal people? And that hasn't been available really even in, and we started this two years ago, even in the biggest atlases published of the human body. There was no upper airway tissue representation at all. And that's because technically it's just tough to access and difficult to reproducibly get at. And so, we recruited people to a group of 20 to 30 people to come to LJI once a month, and just started testing out, published and unpublished sampling techniques to see were there ways where we could reproducibly sample immune cells in the upper airways from people. And once we got things, so the keys for us were you got to have enough cells that you can collect to learn something from. And luckily with modern techniques of flow cytometry and single cell sequencing, you don't need that many cells. And so, we could get a hundred thousand cells on a swab and that's enough to do a lot with. And second, how reproducible was it? So we showed, we had people come in every month for a year and we could reproducibly find the same things in their swab; same cell types in their swabs. And the third thing was that people would come back. Shane Crotty (06:05): We found that if you have good nurses doing the techniques, we could find ways that this would be a sampling approach that was tolerable and people would come back for repeat measures, which is really valuable to see what's happening in people over time. So that was what we started from in the study and built from. Eric Topol (06:27): And if I am correct, you sampled two places with the swabs, one in the nose and one of the throat. Or, I think one which you have in the paper as the MT for something about the median nasal turbinate and the other adenoid in the back of the throat. Is that right? Shane Crotty (06:50): So all the sampling is a swab into your nose. And when we were doing that, we were really excited to see the diversity of immune cells, particularly T cells and B cells, memory T cells and B cells that we isolated. They're like, wow, there's actually a lot of interesting immune memory up in there. And the lab said, oh, by the way, we're seeing T follicular helper cells (TFH). Now that happens to be my favorite cell type. Eric Topol (07:22): Why is that, Shane? Of all the cells, why do you say that's your favorite? I know you publish a lot on it. Shane Crotty (07:31): Because those are the T cells that are required for basically all neutralizing antibody responses. All high-quality antibody responses depend on—almost all high-quality antibody responses depend on—T cell help. That T cell help comes from T follicular helper cells. Antibody evolution is certainly one of the coolest processes of the immune system. And all of that depends on T follicular helper cells. So the fact that for example, you could get Omicron neutralizing antibodies even after only being vaccinated with ancestral vaccine, that's the immune system making guesses of what variants would look like. And those guesses come about through this antibody evolution that's driven by T follicular helper cells. So, it's really one of the most brilliant things the immune system does, and that's a cell type that's really key, but those processes happen in lymphoid tissue. That's what happens in lymph nodes and spleen. And here we were sampling epithelium, your nasal epithelium, so the cells didn't really belong there. Shane Crotty (08:37): And so, that's what turned the study in another direction. And we said, okay, let's figure out why is it that these cells are present in these swabs? And we had a couple of possibilities. One possibility was that the swab was going all the way back to the posterior wall of your nasopharynx, your top of your throat and sampling adenoid tissue. So adenoid tonsils and adenoids are a true lymphoid tissue and they're a mucosal lymphoid tissue. And so, we came up with multiple ways to validate that that's what we were testing. And in fact, it was the Sydney Ramirez, a clinician, and the ENTs involved who said, well, let's just look. And so, they actually did endoscopies with the swab to actually see where the swab went. We've got videos of the swabs going into the adenoid crypt in the back, and then we've got measurements of here are the cells that you find on those swabs. Shane Crotty (09:58): And what's cool about it is that, yes, so we did studies with two sets. We then shifted to doing studies with two sets of swabs. One where we essentially went “halfway back” where we were detecting that epithelium of your nasal passages and then one where it was all the way back and detecting the adenoid lymphoid tissue. So here we've got two different sites in your upper airways that are about an inch apart, and we're detecting essentially completely different cells of the immune system at those two places. And we tend to t

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Thank you for reading Ground Truths. This post is public so feel free to share it. Transcript with audio and external links Eric Topol (00:05): Hello, it's Eric Topol with Ground Truths, and I am really thrilled to have with me Professor Faisal Mahmood, who is lighting it up in the field of pathology with AI. He is on the faculty at Harvard Medical School, also a pathologist at Mass General Brigham and with the Broad Institute, and he has been publishing at a pace that I just can't believe we're going to review that in chronological order. So welcome, Faisal. Faisal Mahmood (00:37): Thanks so much for having me, Eric. I do want to mention I'm not a pathologist. My background is in biomedical imaging and computer science. But yeah, I work very closely with pathologists, both at Mass General and at the Brigham. Eric Topol (00:51): Okay. Well, you know so much about pathology. I just assume that you were actually, but you are taking computational biology to new levels and you're in the pathology department at Harvard, I take it, right? Faisal Mahmood (01:08): Yeah, I'm at the pathology department at Mass General Brigham. So the two hospitals are now integrated, so I'm at the joint department. Eric Topol (01:19): Good. Okay. Well, I'm glad to clarify that because as far as I knew you were hardcore pathologist, so you're changing the field in a way that is quite unique, I should say, because a number of years ago, deep learning was starting to get applied to pathology just like it was and radiology and ophthalmology. And we saw some early studies with deep learning whereby you could find so much more on a slide that otherwise would be not even looked at or considered or even that humans wouldn't be able to see. So maybe you could just take us back first to the deep learning phase before these foundation models that you've been building, just to give us a flavor for what was the warmup in this field? Faisal Mahmood (02:13): Yeah, so I think around 2016 and 2017, it was very clear to the computer vision community that deep learning was really the state of the art where you could have abstract feature representations that were rich enough to solve some of these fundamental classification problems in conventional vision. And that's around the time when deep learning started to be applied to everything in medicine, including pathology. So we saw some earlier cities in 2016 and 2017, mostly in machine learning conferences, applying this to very basic patch level pathology dataset. So then in 2018 and 2019, there were some studies in major journals including in Nature Medicine, showing that you could take large amounts of pathology data and classify what's known to us and including predicting what's now commonly referred to as non-human identifiable features where you could take a label and this could come from molecular data, other kinds of data like treatment response and so forth, and use that label to classify these images as responders versus non-responders or having a certain kind of mutation or not. (03:34): And what that does is that if there is a morphologic signal within the image, it would pick up on that morphologic signal even though humans may not have picked up on it. So it was a very exciting time of developing all of these supervised, supervised foundation models. And then I started working in this area around 2019, and one of the first studies we did was to try to see if we can make this a little bit more data efficient. And that's the CLAM method that we published in 2021. And then we took that method and applied it to the problem of cancers of unknown primary, that was also in 2021. Eric Topol (04:17): So just to review, in the phase of deep learning, which was largely we're talking about supervised with ground truth images, there already was a sign that you could pick up things like the driver mutation, the prognosis of the patient from the slide, you could structural variations, the origin of the tumor, things that would never have been conceived as a pathologist. Now with that, I guess the question is, was all this confined to whole slide imaging or could you somehow take an H&E slide conventional slide and be able to do these things without having to have a whole slide image? Faisal Mahmood (05:05): So at the time, most of the work was done on slides that were fully digital. So taking a slide and then digitizing the image and creating a whole slide image. But we did show in 2021 that you could put the slide under a microscope and then just capture it with a camera or just with a cell phone coupled to a camera, and then still make those predictions. So these models were quite robust to that kind of domain adaptation. And still I think that even today the slide digitization rate in the US remains at around 4%, and the standard of care is just looking at a glass light under a microscope. So it's very important to see how we can further democratize these models by just using the microscope, because most microscopes that pathologists use do have a camera attached to them. So can we somehow leverage that camera to just use a model that might be trained on a whole slide image, still work with the slide under a microscope? Eric Topol (06:12): Well, what you just said is actually a profound point that is only 4% of the slides are being reviewed digitally, and that means that we're still an old pathology era without the enlightenment of machine eyes. I mean these digital eyes that can be trained even without supervised learning as we'll get to see things that we'll never see. And to make, and I know we'll be recalling back in 2022, you and I wrote a Lancet piece about the work that you had done, which is very exciting with cardiac biopsies to detect whether a heart transplant was a rejection. This is a matter of life or death because you have to give more immunosuppression drugs if it's a rejection. But if you do that and it's not a rejection or you miss it, and there's lots of disagreement among pathologists, cardiac pathologists, regarding whether there's a transplant. So you had done some early work back then, and because much of what we're going to talk about, I think relates more to cancer, but it's across the board in pathology. Can you talk about the inner observer variability of pathologists when they look at regular slides? Faisal Mahmood (07:36): Yeah. So when I first started working in this field, my kind of thinking was that the slide digitization rate is very low. So how do we get people to embrace and adapt digital pathology and machine learning models that are trained on digital data if the data is not routinely digitized? So one of my kind of line of thinking was that if we focus on problems that are inherently so difficult that there isn't a good solution for them currently, and machine learning provides, or deep learning provides a tangible solution, people will be kind of forced to use these models. So along those lines, we started focusing on the cancers of unknown primary problem and the myocardial biopsy problem. So we know that the Cohen’s kappa or the intra-observer variability that also takes into account agreement by chance is around 0.22. So it's very, very low for endomyocardial biopsies. So that just means that there are a large number of patients who have a diagnosis that other pathologists might not agree with, and the downstream treatment regimen that's given is entirely based on that diagnosis. The same patient being diagnosed by a different cardiac pathologist could be receiving a very different regimen and could have a very, very different outcome. (09:14): So the goal for that study is published in Nature of Medicine in 2022, was to see if we could use deep learning to standardize that and have it act as an assistive tool for cardiac pathologists and whether they give more standardized responses when they're given a machine learning based response. So that's what we showed, and it was a pleasure to write that corresponding piece with you in the Lancet. Eric Topol (09:43): Yeah, no, I mean I think that was two years ago and so much has happened since then. So now I want to get into this. You've been on a tear every month publishing major papers and leading journals, and I want to just go back to March and we'll talk about April, May, and June. So back in March, you published two foundation models, UNI and CONCH, I believe, both of these and back-to-back papers in Nature Medicine. And so, maybe first if you could explain the foundation model, the principle, how that's different than the deep learning network in terms of transformers and also what these two different, these were mega models that you built, how they contributed to help advance the field. Faisal Mahmood (10:37): So a lot of the early work that we did relied on extracting features from a resonant trained on real world images. So by having these features extracted, we didn't need to train these models end to end and allowed us to train a lot of models and investigate a lot of different aspects. But those features that we used were still based on real world images. What foundation models led us do is they leveraged self supervised learning and large amounts of data that would be essentially unlabeled to extract rich feature representations from pathology images that can then be used for a variety of different downstream tasks. So we basically collected as much data as we could from the Brigham and MGH and some public sources while trying to keep it as diverse as possible. So the goal was to include infectious, inflammatory, neoplastic all everything across the pathology department while still being as diverse as possible, including normal tissue, everything. (11:52): And the hypothesis there, and that's been just recently confirmed that the hypothesis was that diversity would matter

Recently, a series of papers were published in Nature and Nature journals illuminating the physiologic effects of exercise from an NIH initiative called MoTrPAC. To understand the wealth of new findings, I spoke with Professor Euan Ashley, who, along with Matt Wheeler, heads up the bioinformatics center. Earlier this week, Stanford announced Euan Ashley will be the new Chair of the Department of Medicine. He has done groundbreaking work in human genomics, including rapid whole genome sequencing for critically ill patients and applying the technology for people with unknown diseases. A few years ago he published The Genome Odyssey book. As you’ll see from our conversation, he has also done extensive work on the science of exercise. Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with audio and external links Eric Topol (00:06): Well, hello, it's Eric Topol with Ground Truths, and I'm really delighted today to welcome my friend, Euan Ashley. He is the Roger and Joelle Burnell Chair of Genomics and Precision Health at Stanford. He's done pioneering work in genomics, but today we're going to talk about something very different, which he also is working in exercise. Exercise the cover of a Nature paper in May regarding this MoTrPAC, which we're going to talk about this big initiative to understand the benefits of exercise. But before I hand it over to Euan, and I just want to mention his description of the paper that he posted to summarize started with, “Exercise may be the single most potent medical intervention ever known.” So Euan welcome. Euan Ashley (01:01): Yeah, well, great. It's wonderful to be here, Eric, and so nice to see you. Eric Topol (01:06): Yeah. Well, we have a lot to talk about because exercise is a fascinating topic. And I guess maybe we'd start with the MoTrPAC, which is an interesting acronym that you all came up with. Maybe tell us a bit about that with the 800 rats and the 2,400 people and the 17,000 molecules, there’s a lot there. Euan Ashley (01:24): Right, right. Yeah. Well, first of all, of course, before you do any scientific study, especially with a large number of people in a consortium, you need a good acronym. So that was where we started with the idea was to focus on the molecular transducers of physical activity. As you pointed out there at the beginning, we really don’t have a more potent medical intervention, especially for prevention of disease. I mean, it’s just such a powerful thing that we have, and yet we don’t really understand how it works. And so, the MoTrPAC Consortium was designed to really work together, bring groups of people across the US together who all have some interest in exercise and some ability to measure molecules and really put together the world's largest study of exercise to try and start answering some of the questions about where the potency of this intervention come from. Eric Topol (02:20): So the first crop of papers, and there were several of them that came out all on the same day in Nature publications, was about the rats. The people part is incubating, but can you give us a skinny on, there was a lot there, but maybe you could just summarize what you thought were the main findings. Key MoTrPAC Findings Euan Ashley (02:43): Yeah, of course, of course. And the MoTrPAC Consortium, I'll say first of all, yeah, large group is probably I think 36 principal investigators funded by the Common Fund. And so, it brings together large numbers of people, some of whom who spend most of their time thinking about let’s say animal exercise. Some have spent a lot of time thinking about humans in exercise and many of whom think about measuring technologies. And as you say, these first group of papers were focused on the rat study, but actually the study goes much more broadly than that. But of course, there are some advantages to the animal protocols. We can look at tissue and we'll talk about that in a moment. But the humans, of course, are where we're most interested in the end. And we do have tissues coming from humans blood and adipose tissue and skeletal muscle, but those are obviously the only organs we can really access. (03:31): So there's a rat study, which is this one we'll talk about, and that's aerobic exercise and training. There's human studies that include aerobic exercise, strengths studies as well. There's a study in kids, pediatric study and then also a study of people who are very fit because here we're focusing on the change from sedentary to fit. And so that gives us the key exercise signal. So this first crop of papers was really our first look, cross-tissue, cross multi-omics, so multiple different modalities of measurement. And I think, yeah, we were like about nine and a half thousand assays, 19 tissues, 25 different measurement platforms, and then four training points for these rats. So let's talk about the rats for a minute. What do they do? So they normally live at night. They're active at night. In this study, we reverse that so that we can actually do the studies during the day. (04:25): So we reverse their at night cycle and they do their treadmill exercise over the course of several weeks. They start with about 20 minutes, and they do more every day. There's a control group of rats that just get placed on the treadmill and then don't do any exercise. And so, this is a controlled study as well. And over the course of time, we work more, it's about eight weeks in total and then two days after each of those bouts of exercise. So it's not an acute study, we measure to see where we are. So we also have this time trajectory of exercise. So what did we find? I mean, I think the first thing I would say, we talked about just how potent exercise is. It's very, very clear from looking at all these tissues that when you exercise regularly, you are just a different person, or in this case a different rat. (05:15): Like literally every tissue is changed dramatically and some in quite surprising ways. So I give you a couple of the things that surprised me or that I thought were most interesting. The first thing was this question of how does exercise actually work? Because exercise is a stress. You go out and you pound the pavement or you're on the bike or whatever, and then your body recovers. And so, there's been this idea, it's referred to as hormesis, this idea that some of the benefit of exercise might come from this recurrent stress. So your body learns how to deal with stress. And so given that we were very interested that this heat shock response was so prominent across multiple tissues. So heat shock proteins are molecular chaperones and they take care of protein folding to make sure it's appropriately done and they prevent protein aggregation. And when proteins need degraded because they're damaged, the heat shock system jumps in. (06:10): So perhaps not surprising, but pretty interesting that the heat shock proteins were very prominent part of the stress response to exercise. And remember, this is not acute exercise, so these are benefits that are built up over time, so that was one. A surprising one to me, the adrenal gland. So we're used to thinking of adrenaline as an epinephrine, as a stress hormone, but actually we saw dramatic changes in the adrenal gland and we don't necessarily think too much. You think about the exercising muscles, you think about the heart, we think about the lungs, when we think about exercise, you don't necessarily think that you're changing your adrenal gland, but it was one of the most changed tissues. The immune system was a common upregulated system. We saw that. And in fact, some of the tissues in which the immune genes were most changed were somewhat surprising. (07:02): So the small intestine, for example, was a place where there was a highest enrichment of immune mediated pathways. And then some tissues changed pretty early, like the small intestine changed after just one or two weeks of training other tissues like the brown adipose tissue. It was more like seven or eight weeks of training before we saw the real changes in there. So just one or two little things that struck out, but I think this really the first molecular map of exercise. So we're looking across the whole system across multiple modalities of measurement across multiple tissues. Simulating Stress Eric Topol (07:34): So as far as understanding the benefits of exercise, does this tell us that it really does simulate stress that it's conditioning the body to deal with stress as reflected by the various points you just summarized? Euan Ashley (07:51): Yeah, I think that is exactly right. I mean, part of what we were trying to understand was in what way are you changed after you do exercise regularly? And I think if we think about things that are positive, then the ability to deal with stress at a cellular level, quite literally repair mechanisms seems to be a big part of it. The other aspect that was interesting is that when you're measuring this many analytes, you can also compare that with disease. And so, we understand that exercises is preventive benefit against disease. So in some cases, and this was work highlighted by my colleague Maléne Lindholm in the mitochondrial paper that came along with the main paper and she looked with a team across all mitochondrial changes across all of the tissues of the cell. So these are the workhorses of the individual cells that like the batteries inside the cells of the mitochondria. (08:54): And we saw big changes across, it's not surprisingly, but it's the energy source for cells, big changes across many tissues. But interestingly for two specific really important diseases, a liver disease in one case and type 2 diabetes on the other, it was very clear that the training upregulated a network that was exactly the opposite of that of the disease. And so, it

A book that reads like a novel; it’s humorous, it’s a love story. Dr. Christopher Labos, an imaginative cardiologist and epidemiologist at McGill University, takes us through multiple longstanding misconceptions about different foods and drinks, and along the way provides outstanding educational value. Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with external links and links to the audio recording Eric Topol (00:07): Hello, it's Eric Topol with Ground Truths, and with me today is a cardiologist, Chris Labos from Montreal, who has written an extraordinary book. I just read it on my Kindle, “Does Coffee Cause Cancer? And 8 More Myths about the Food We Eat. Chris teaches at McGill University. He is a prolific writer at the Montreal Gazette and Canadian broadcast system, CBC, CJAD radio, CTV News. And he also has a podcast on the Body of Evidence and he probably has other stuff, but welcome Chris. Christopher Labos (00:49): Hello. Hello. Hello. Thank you for having me. It is a great honor to be on your podcast. I am in awe of the work that you've been doing, I mean, for all your career, but especially during Covid. So it's a big thrill for me to be on the podcast. Eric Topol (01:03): Well, for me, I have to say I learned about a person who is not only remarkably imaginative but also humorous. And so, have you ever done standup comedy? Christopher Labos (01:16): I have not. Although I was asked to chair the research awards that we did here at McGill one year because I've been doing local media stuff and they said, can you come and be like the MC? And I said, sure. And I said, do you want me to be funny? And they were like, well, if you can. And I went up there and people were laughing and laughing and laughing and then people, like some of my former attendings had come up to me and they're like, Chris, I don't remember you being this funny as a resident. And I was like, well, I guess you come into your own when you start your own career. But I think people were very, it's tough MCing a research awards because you're essentially, it's kind of like a high school graduation where you don't read the names in alphabetical order, right? It's like one name after the other. And I went up there and I tried to throw in a little bit of humor and people seem to like it. So I think that was the first, that was when I started to realize, oh, if you inject a little bit of levity into what you're doing, it tends to resonate a little bit more with people. Eric Topol (02:13): Well, no question about that. And what I love about this book is that it wasn't anything like I thought it was going to be. Eric Topol (02:21): Amazing. It was a surprise. So basically you took these nine myths, which we'll talk to, hopefully we'll get to several of them, but you didn't just get into that myth. You get into teaching medical statistics, how to read papers, all the myths. I mean, you are the master debunker with entertainment, with funny stuff. It's really great. So this is great, before we get into some of these myths and for you to amplify, but this is a gift of communication, science communication that is you get people to learn about things like p-hacking and you throw in love stories and all kinds of stuff. I mean, I don't know how you can dream this stuff up. I really don't. Christopher Labos (03:10): I sort of look back at the inception of this. This book did have sort of a few iterations. And I think the first time I was thinking about it, I mean I wrote it during Covid and so I was really thinking about this type of stuff. It's like how do we educate the public to become better consumers of scientific information? Because there was a lot of nonsense during Covid. So teaching them about confounding, which I think through a lot of people when we started talking about low vitamin D levels and Covid and outcomes and all that. And so, I started like, how do I write this type of book? And I thought, yeah, this should probably be a serious science book. And the first version of it was a very serious science book. And then the idea came and try to make it a conversation. And I think I sort of wrote it. (04:02): There's a book that may not be that popular in the US but it was kind of popular here in Canada. It was called The Wealthy Barber. And it was all about personal finance. And the idea of the book was these people would go into a barbershop and the barber would talk to them about how to save money and how to invest in all that. And it was fairly popular and people liked that back and forth. And I said, oh, maybe I could do something like that. And then I wrote the first chapter of the doctor who goes in to talk to the barista and I showed it to a friend of mine. I said, what do you think? Do you think this would work? And her response to me by email was two lines. It was pretty good period. But I kept expecting him to ask her out at the end. And the minute she said that I thought, oh my God, this is a love story. And so, I reshaped everything to make this a love story. And I don't think the publishers were expecting that either because they were like, the first comment from the editor was, most science books don't have a narrative arc to them in character, but this one does. So there you go. Eric Topol (05:00): This is a unique book. I hope that people who listen or read the transcript will realize that this is a gift. It's a model of communication and it just is teaching things almost like you don't realize it. You're just learning all this stuff. So let's get into some of these because they're just masterful. I guess I should start ask you, you have nine of them. You could have picked 20 more, but which one is your favorite? Or do you have one? Christopher Labos (05:31): I think the one, it's hard to say. I think the first one in the book is the vitamin C one. And I think it's the most interesting one to explain to people, not just because vitamin C to fight the common cold is so pervasive as a product and a thing that people believe. But it also, I think has the greatest opportunity to teach people about what is one of the most important ones, which is subgroup analysis and p-hacking. And it's so easy to bring that back into a comedic level with some of the graphs that I put in there. I think a close second would probably be the coffee one where I was talking about selection bias, because those examples of online dating and then all the jokes that came from it. And it's hard to say how much of it was the subject and how much of it was the character. (06:21): Because I'd always heard stories of authors when they say like, oh, the characters will tell me what to say. And I always thought that sounds like bollocks. How could that be possible? You're the author, you write what's on the page. But then the minute I started actually writing it and started envisaging these characters, all of a sudden the characters took on a life of their own and they were dictating how the story ended up. So the coffee one I think is also good too. And I guess it became the title of the book. So I guess that's a good indication that was popular. But when you can really spin it out and make it obvious to people using common examples, I think those are interesting ones. So the vitamin C and the coffee ones, I think were probably the most interesting. Eric Topol (07:02): Let's take those first because you've mentioned them and then hopefully we'll get into some others. Now in the vitamin C, you're going on a plane and you hook up with this guy, Jim, on the plane. I know none of this stuff really happened, and you're explaining to him the famous ISIS-2 trial about the Gemini and Libra subgroup. So for those of people who are listening, can you review that? Because that of course is just one of so many things you get into. Christopher Labos (07:33): I know it's almost amazing how short a memory we have in medicine, right? And again, this is sort of surprising me. I sort of knew the study and then I went back, and I looked at it and I thought ISIS-2 was in 1988. That's not that long ago. The fact that we didn't give aspirin. So for people who don't know, I mean, we did not give aspirin to people with cardiac disease for a very long time. And it was really from 1988 afterwards. So relatively recently, I mean I realized it's been a couple of decades, but still. So ISIS-2 was really the first trial to show that if you give aspirin to somebodywhen they're having a heart attack, you see a benefit. But what was fascinating in the study was this one subgroup analysis of people in whom it did not work. (08:19): And when I give public lectures, I often use this example because it's such a beautiful teaching case, and I go ask people, what do you think it was? And people are like, oh, hemophiliacs, smokers, people who drink alcohol. And then you find out, no, the subgroup in whom aspirin does not work is Geminis and Libras. And everybody sort of laughs and they think it's funny. And it's a beautiful example because a lot of people think it's like, oh, it was a joke or it was sort of silly science. But no, it was actually done purposefully. And the authors put that in there because they wanted to make the point that subgroup analysis are potentially misleading. And I sort of am a little bit in awe of, I mean the power or the intelligence to actually make it a point with the editors like, no, we're going to put this in here essentially as a teaching tool. (09:09): And it's amazing to me that we're still using it as a teaching tool decades after the fact. But it was just to show that when you have these tables where you have umpteen subgroup analysis, just by random chance, you will get some spurious results. And though our brain understands that Zodiac signs have nothing to do with the effectiveness of aspirin, you do the same subgroup anal

The most enthralling conversation I’ve ever had with anyone on cancer. It’s with Charlie Swanton who is a senior group leader at the Francis Crick Institute, the Royal Society Napier Professor in Cancer and medical oncologist at University College London, co-director of Cancer Research UK. Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with audio links and many external links Eric Topol (00:07): Well, hello, this is Eric Topol with Ground Truths, and I am really fortunate today to connect us with Charlie Swanton, who is if not the most prolific researcher in the space of oncology and medicine, and he's right up there. Charlie is a physician scientist who is an oncologist at Francis Crick and he heads up the lung cancer area there. So Charlie, welcome. Charles Swanton (00:40): Thank you, Eric. Nice to meet you. Learning from a Failure Eric Topol (00:43): Well, it really is a treat because I've been reading your papers and they're diverse. They're not just on cancer. Could be connecting things like air pollution, it could be Covid, it could be AI, all sorts of things. And it's really quite extraordinary. So I thought I'd start out with a really interesting short paper you wrote towards the end of last year to give a sense about you. It was called Turning a failing PhD around. And that's good because it's kind of historical anchoring. Before we get into some of your latest contributions, maybe can you tell us about that story about what you went through with your PhD? Charles Swanton (01:26): Yeah, well thank you, Eric. I got into research quite early. I did what you in the US would call the MD PhD program. So in my twenties I started a PhD in a molecular biology lab at what was then called the Imperial Cancer Research Fund, which was the sort of the mecca for DNA tumor viruses, if you like. It was really the place to go if you wanted to study how DNA tumor viruses worked, and many of the components of the cell cycle were discovered there in the 80s and 90s. Of course, Paul Nurse was the director of the institute at the time who discovered cdc2, the archetypal regulator of the cell cycle that led to his Nobel Prize. So it was a very exciting place to work, but my PhD wasn't going terribly well. And sort of 18, 19 months into my PhD, I was summoned for my midterm reports and it was not materializing rapidly enough. (02:25): And I sat down with my graduate student supervisors who were very kind, very generous, but basically said, Charlie, this isn't going well, is it? You've got two choices. You can either go back to medical school or change PhD projects. What do you want to do? And I said, well, I can't go back to medical school because I’m now two years behind. So instead I think what I'll do is I'll change PhD projects. And they asked me what I'd like to do. And back then we didn't know how p21, the CDK inhibitor bound to cyclin D, and I said, that's what I want to understand how these proteins interact biochemically. And they said, how are you going to do that? And I said, I'm not too sure, but maybe we'll try yeast two-hybrid screen and a mutagenesis screen. And that didn't work either. And in the end, something remarkable happened. (03:14): My PhD boss, Nic Jones, who's a great guy, still is, retired though now, but a phenomenal scientist. He put me in touch with a colleague who actually works next door to me now at the Francis Crick Institute called Neil McDonald, a structural biologist. And they had just solved, well, the community had just solved the structure. Pavletich just solved the structure of cyclin A CDK2. And so, Neil could show me this beautiful image of the crystal structure in 3D of cyclin A, and we could mirror cyclin D onto it and find the surface residue. So I spent the whole of my summer holiday mutating every surface exposed acid on cyclin D to an alanine until I found one that failed to interact with p21, but could still bind the CDK. And that little breakthrough, very little breakthrough led to this discovery that I had where the viral cyclins encoded by Kaposi sarcoma herpes virus, very similar to cyclin D, except in this one region that I had found interactive with a CDK inhibitor protein p21. (04:17): And so, I asked my boss, what do you think about the possibility this cyclin could have evolved from cyclin D but now mutated its surface residues in a specific area so that it can't be inhibited by any of the control proteins in the mammalian cell cycle? He said, it's a great idea, Charlie, give it a shot. And it worked. And then six months later, we got a Nature paper. And that for me was like, I cannot tell you how exciting, not the Nature paper so much as the discovery that you were the first person in the world to ever see this beautiful aspect of evolutionary biology at play and how this cyclin had adapted to just drive the cell cycle without being inhibited. For me, just, I mean, it was like a dream come true, and I never experienced anything like it before, and I guess it's sizes the equivalent to me of a class A drug. You get such a buzz out of it and over the years you sort of long for that to happen again. And occasionally it does, and it's just a wonderful profession. Eric Topol (05:20): Well, I thought that it was such a great story because here you were about to fail. I mean literally fail, and you really were able to turn it around and it should give hope to everybody working in science out there that they could just be right around the corner from a significant discovery. Charles Swanton (05:36): I think what doesn't break you makes you stronger. You just got to plow on if you love it enough, you'll find a way forward eventually, I hope. Tracing the Evolution of Cancer (TRACERx) Eric Topol (05:44): Yeah, no question about that. Now, some of your recent contributions, I mean, it's just amazing to me. I just try to keep up with the literature just keeping up with you. Charles Swanton (05:58): Eric, it's sweet of you. The first thing to say is it's not just me. This is a big community of lung cancer researchers we have thanks to Cancer Research UK funded around TRACERx and the lung cancer center. Every one of my papers has three corresponding authors, multiple co-first authors that all contribute in this multidisciplinary team to the sort of series of small incremental discoveries. And it's absolutely not just me. I've got an amazing team of scientists who I work with and learn from, so it's sweet to give me the credit. Eric Topol (06:30): I think what you're saying is really important. It is a team, but I think what I see through it all is that you're an inspiration to the team. You pull people together from all over the world on these projects and it's pretty extraordinary, so that's what I would say. Charles Swanton (06:49): The lung community, Eric, the lung cancer community is just unbelievably conducive to collaboration and advancing understanding of the disease together. It's just such a privilege to be working in this field. I know that sounds terribly corny, but it is true. I don't think I recall a single email to anybody where I've asked if we can collaborate where they've said, no, everybody wants to help. Everybody wants to work together on this challenge. It's just such an amazing field to be working in. Eric Topol (07:19): Yeah. Well I was going to ask you about that. And of course you could have restricted your efforts or focused on different cancers. What made you land in lung cancer? Not that that's only part of what you're working on, but that being the main thing, what drew you to that area? Charles Swanton (07:39): So I think the answer to your question is back in 2008 when I was looking for a niche, back then it was lung cancer was just on the brink of becoming an exciting place to work, but back then nobody wanted to work in that field. So there was a chair position in thoracic oncology and precision medicine open at University College London Hospital that had been open, as I understand it for two years. And I don't think anybody had applied. So I applied and because I was the only one, I got it and the rest is history. (08:16): And of course that was right at the time when the IPASS draft from Tony Mok was published and was just a bit after when the poster child of EGFR TKIs and EGFR mutant lung cancer had finally proven that if you segregate that population of patients with EGFR activating mutation, they do incredibly well on an EGFR inhibitor. And that was sort of the solid tumor poster child along with Herceptin of precision medicine, I think. And you saw the data at ASCO this week of Lorlatinib in re-arranged lung cancer. Patients are living way beyond five years now, and people are actually talking about this disease being more like CML. I mean, it's extraordinary the progress that's been made in the last two decades in my short career. Eric Topol (09:02): Actually, I do want to have you put that in perspective because it's really important what you just mentioned. I was going to ask you about this ASCO study with the AKT subgroup. So the cancer landscape of the lung has changed so much from what used to be a disease of cigarette smoking to now one of, I guess adenocarcinoma, non-small cell carcinoma, not related to cigarettes. We're going to talk about air pollution in a minute. This group that had, as you say, 60 month, five year plus survival versus what the standard therapy was a year plus is so extraordinary. But is that just a small subgroup within small cell lung cancer? Charles Swanton (09:48): Yes, it is, unfortunately. It’s just a small subgroup. In our practice, probably less than 1% of all presentations often in never smokers, often in female, never smokers. So it is still in the UK at least a minority subset of adenocarcinomas, but it's still, as you rightly say, a minority of patients that we c

In this podcast, Thomas Czech, Distinguished Professor at the University of Colorado, Boulder, with a lineage of remarkable contributions on RNA, ribozyme, and telomeres, discuss why RNA is so incredibly versatile. Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to the audio and external links Eric Topol (00:07): Well, hello, this is Eric Topol from Ground Truths, and it's really a delight for me to welcome Tom Cech who just wrote a book, the Catalyst, and who is a Nobel laureate for his work in RNA. And is at the University of Colorado Boulder as an extraordinary chemist and welcome Tom. Tom Cech (00:32): Eric, I'm really pleased to be here. The RNA Guy Eric Topol (00:35): Well, I just thoroughly enjoyed your book, and I wanted to start out, if I could, with a quote, which gets us right off the story here, and let me just get to it here. You say, “the DNA guy would need to become an RNA guy. Though I didn’t realize it at the time, jumping ship would turn out to be the most momentous decision in my life.” Can you elaborate a bit on that? Tom Cech (01:09): As a graduate student at Berkeley, I was studying DNA and chromosomes. I thought that DNA was king and really somewhat belittled the people in the lab next door who were working on RNA, I thought it was real sort of second fiddle material. Of course, when RNA is acting just as a message, which is an important function, a critical function in all life on earth, but still, it's a function that's subservient to DNA. It's just copying the message that's already written in the playbook of DNA. But little did I know that the wonders of RNA were going to excite me and really the whole world in unimaginable ways. Eric Topol (02:00): Well, they sure have, and you've lit up the world well before you had your Nobel Prize in 1989 was Sid Altman with ribozyme. And I think one of the things that struck me, which are so compelling in the book as I think people might know, it's divided in two sections. The first is much more on the biology, and the second is much more on the applications and how it's changing the world. We'll get into it particularly in medicine, but the interesting differentiation from DNA, which is the one trick pony, as you said, all it does is store stuff. And then the incredible versatility of RNA as you discovered as a catalyst, that challenging dogma, that proteins are supposed to be the only enzymes. And here you found RNA was one, but also so much more with respect to genome editing and what we're going to get into here. So I thought what we might get into is the fact that you kind of went into the scum of the pond with this organism, which by the way, you make a great case for the importance of basic science towards the end of the book. But can you tell us about how you, and then of course, many others got into the Tetrahymena thermophila, which I don't know that much about that organism. Tom Cech (03:34): Yeah, it's related to Tetrahymena is related to paramecium, which is probably more commonly known because it's an even larger single celled animal. And therefore, in an inexpensive grade school microscope, kids can look through and see these ciliated protozoa swimming around on a glass slide. But I first learned about them when I was a postdoc at MIT and I would drive down to Joe Gall's lab at Yale University where Liz Blackburn was a postdoc at the time, and they were all studying Tetrahymena. It has the remarkable feature that it has 10,000 identical copies of a particular gene and for a higher organism, one that has its DNA in the nucleus and does its protein synthesis in the cytoplasm. Typically, each gene's present in two copies, one from mom, one from dad. And if you're a biochemist, which I am having lots of stuff is a real advantage. So 10,000 copies of a particular gene pumping out RNA copies all the time was a huge experimental advantage. And that's what I started working on when I started my own lab at Boulder. Eric Topol (04:59): Well, and that's where, I guess the title of the book, the Catalyst ultimately, that grew into your discovery, right? Tom Cech (05:08): Well, at one level, yes, but I also think that the catalyst in a more general conversational sense means just facilitating life in this case. So RNA does much more than just serve as a biocatalyst or a message, and we'll get into that with genome editing and with telomerase as well. The Big Bang and 11 Nobel Prizes on RNA since 2000 Eric Topol (05:32): Yes, and I should note that as you did early in the book, that there's been an 11 Nobel prize awardees since 2000 for RNA work. And in fact, we just had Venki who I know you know very well as our last podcast. And prior to that, Kati Karikó, Jennifer Doudna who worked in your lab, and the long list of people working RNA in the younger crowd like David Liu and Fyodor Urnov and just so many others, we need to have an RNA series because it's just exploding. And that one makes me take you back for a moment to 2007. And when I was reading the book, it came back to me about the Economist cover. You may recall almost exactly 17 years ago. It was called the Biology’s Big Bang – Unravelling the secrets of RNA. And in that, there was a notable quote from that article. Let me just get to that. And it says, “it is probably no exaggeration to say that biology is now undergoing its neutron moment.” (06:52): This is 17 years ago. “For more than half a century the fundamental story of living things has been a tale of the interplay between genes, in the form of DNA, and proteins, which is genes encode and which do the donkey work of keeping living organisms living. The past couple of years, 17 years ago, however, has seen the rise and rise of a third type of molecule, called RNA.” Okay, so that was 2007. It's pretty extraordinary. And now of course we're talking about the century of biology. So can you kind of put these last 17 years in perspective and where we're headed? Tom Cech (07:34): Well, Eric, of course, this didn't all happen in one moment. It wasn't just one big bang. And the scientific community has been really entranced with the wonders of RNA since the 1960s when everyone was trying to figure out how messenger RNA stored the genetic code. But the general public has been really kept in the dark about this, I think. And as scientists, were partially to blame for not reaching out and sharing what we have found with them in a way that's more understandable. The DNA, the general public's very comfortable with, it's the stuff of our heredity. We know about genetic diseases, about tracing our ancestry, about solving crimes with DNA evidence. We even say things like it's in my DNA to mean that it's really fundamental to us. But I think that RNA has been sort of kept in the closet, and now with the mRNA vaccines against Covid-19, at least everyone's heard of RNA. And I think that that sort of allowed me to put my foot in the door and say, hey, if you were curious about the mRNA vaccines, I have some more stories for you that you might be really interested in. RNA vs RNA Eric Topol (09:02): Yeah, well, we'll get to that. Maybe we should get to that now because it is so striking the RNA versus RNA chapter in your book, and basically the story of how this RNA virus SARS-CoV-2 led to a pandemic and it was fought largely through the first at scale mRNA nanoparticle vaccine package. Now, that takes us back to some seminal work of being able to find, giving an mRNA to a person without inciting massive amount of inflammation and the substitution of pseudouridine or uridine in order to do that. Does that really get rid of all the inflammation? Because obviously, as you know, there's been some negativism about mRNA vaccines for that and also for the potential of not having as much immune cell long term activation. Maybe you could speak to that. Tom Cech (10:03): Sure. So the discovery by Kati Karikó and Drew Weissman of the pseudouridine substitution certainly went a long way towards damping down the immune response, the inflammatory response that one naturally gets with an RNA injection. And the reason for that is that our bodies are tuned to be on the lookout for foreign RNA because so many viruses don't even mess with DNA at all. They just have a genome made of RNA. And so, RNA replicating itself is a danger sign. It means that our immune system should be on the lookout for this. And so, in the case of the vaccination, it's really very useful to dampen this down. A lot of people thought that this might make the mRNA vaccines strange or foreign or sort of a drug rather than a natural substance. But in fact, modified nucleotides, nucleotides being the building blocks of RNA, so these modified building blocks such as pseudoU, are in fact found in natural RNAs more in some than in others. And there are about 200 modified versions of the RNA building blocks found in cells. So it's really not an unusual modification or something that's all that foreign, but it was very useful for the vaccines. Now your other question Eric had to do with the, what was your other question, Eric? Eric Topol (11:51): No, when you use mRNA, which is such an extraordinary way to get the spike protein in a controlled way, exposed without the virus to people, and it saved millions of lives throughout the pandemic. But the other question is compared to other vaccine constructs, there's a question of does it give us long term protective immunity, particularly with T cells, both CD8 cytotoxic, maybe also CD4, as I know immunology is not your main area of interest, but that's been a rub that's been put out there, that it isn't just a weaning of immunity from the virus, but also perhaps that the vaccines themselves are not as good for that purpose. Any thoughts on that? Tom Cech (12:43): Well, so my main thought on that is t

Professor Venki Ramakrishnan, a Nobel laureate for his work on unraveling the structure of function of the ribosome, has written a new book WHY WE DIE which is outstanding. Among many posts and recognitions for his extraordinary work in molecular biology, Venki has been President of the Royal Society, knighted in 2012, and was made a Member of the Order of Merit in 2022. He is a group leader at the MRC Laboratory of Molecular Biology research institute in Cambridge, UK. A brief video snippet of our conversation below. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are available on Apple and Spotify. Transcript with links to audio and external links Eric Topol (00:06): Hello, this is Eric Topol with Ground Truths, and I have a really special guest today, Professor Venki Ramakrishnan from Cambridge who heads up the MRC Laboratory of Molecular Biology, and I think as you know a Nobel laureate for his seminal work on ribosomes. So thank you, welcome. Venki Ramakrishnan (00:29): Thank you. I just want to say that I'm not the head of the lab. I'm simply a staff member here. Eric Topol (00:38): Right. No, I don't want to give you more authority than you have, so that was certainly not implied. But today we're here to talk about this amazing book, Why We Die, which is a very provocative title and it mainly gets into the biology of aging, which Venki is especially well suited to be giving us a guided tour and his interpretations and views. And I read this book with fascination, Venki. I have three pages of typed notes from your book. The Compression of Morbidity Eric Topol (01:13): And we could talk obviously for hours, but this is fascinating delving into this hot area, as you know, very hot area of aging. So I thought I'd start off more towards the end of the book where you kind of get philosophical into the ethics. And there this famous concept by James Fries of compression of morbidity that's been circulating for well over two decades. That's really the big question about all this aging effort. So maybe you could give us, do you think there is evidence for compression of morbidity so that you can just extend healthy aging and then you just fall off the cliff? Venki Ramakrishnan (02:00): I think that's the goal of most of the sort of what I call the saner end of the aging research community is to improve our health span. That is the number of years we have healthy lives, not so much to extend lifespan, which is how long we live. And the idea is that you take those years that we now spend in poor health or decrepitude and compress them down to just very short time, so you're healthy almost your entire life, and then suddenly go into a rapid decline and die. Now Fries who actually coined that term compression or morbidity compares this to the One-Hoss Shay after poem by Oliver Wendell Holmes from the 19th century, which is about this horse carriage that was designed so perfectly that all its parts wore out equally. And so, a farmer was riding along in this carriage one minute, and the next minute he found himself on the ground surrounded by a heap of dust, which was the entire carriage that had disintegrated. Venki Ramakrishnan (03:09): So the question I would ask is, if you are healthy and everything about you is healthy, why would you suddenly go into decline? And it's a fair question. And every advance we've made that has kept us healthier in one respect or another. For example, tackling diabetes or tackling heart disease has also extended our lifespan. So people are not living a bigger fraction of their lives healthily now, even though we're living longer. So the result is we're spending the same or even more number of years with one or more health problems in our old age. And you can see that in the explosion of nursing homes and care homes in almost all western countries. And as you know, they were big factors in Covid deaths. So I'm not sure it can be accomplished. I think that if we push forward with health, we're also going to extend our lifespan. Venki Ramakrishnan (04:17): Now the argument against that comes from studies of these, so-called super centenarians and semi super centenarians. These are people who live to be over 105 or 110. And Tom Perls who runs the New England study of centenarians has published findings which show that these supercentenarians live extraordinarily healthy lives for most of their life and undergo rapid decline and then die. So that's almost exactly what we would want. So they have somehow accomplished compression of morbidity. Now, I would say there are two problems with that. One is, I don't know about the data sample size. The number of people who live over 110 is very, very small. The other is they may be benefiting from their own unique genetics. So they may have a particular combination of genetics against a broad genetic background that's unique to each person. So I'm not sure it's a generally translatable thing, and it also may have to do with their particular life history and lifestyle. So I don't know how much of what we learned from these centenarians is going to be applicable to the population as a whole. And otherwise, I don't even know how this would be accomplished. Although some people feel there's a natural limit to our biology, which restricts our lifespan to about 115 or 120 years. Nobody has lived more than 122. And so, as we improve our health, we may come up against that natural limit. And so, you might get a compression of morbidity. I'm skeptical. I think it's an unsolved problem. Eric Topol (06:14): I think I'm with you about this, but there's a lot of conflation of the two concepts. One is to suppress age related diseases, and the other is to actually somehow modulate control the biologic aging process. And we lump it all together as you're getting at, which is one of the things I loved about your book is you really give a balanced view. You present the contrarians and the different perspectives, the perspective about people having age limits potentially much greater than 120, even though as you say, we haven't seen anyone live past 122 since 1997, so it's quite a long time. So this, I think, conflation of what we do today as far as things that will reduce heart disease or diabetes, that’s age related diseases, that's very different than controlling the biologic aging process. Now getting into that, one of the things that's particularly alluring right now, my friend here in San Diego, Juan Carlos Belmonte, who went over from Salk, which surprised me to the Altos Labs, as you pointed on in the book. Venki Ramakrishnan (07:38): I'm not surprised. I mean, you have a huge salary and all the resources you want to carry out the same kind of research. I wouldn't blame any of these guys. Rejuvenating Animals With Yamanaka Factors Eric Topol (07:50): No, I understand. I understand. It's kind of like the LIV Golf tournament versus the PGA. It's pretty wild. At any rate, he's a good friend of mine, and I visited with him recently, and as you mentioned, he has over a hundred people working on this partial epigenetic reprogramming. And just so reviewing this for the uninitiated is giving the four Yamanaka transcription factors here to the whole animal or the mouse and rejuvenating old mice, essentially at least those with progeria. And then others have, as you point out in the book, done this with just old mice. So one of the things that strikes me about this, and in talking with him recently is it's going to be pretty hard to give these Yamanaka factors to a person, an intravenous infusion. So what are your thoughts about this rejuvenation of a whole person? What do you think? Venki Ramakrishnan (08:52): If I hadn't seen some of these papers would've been even more skeptical. But the data from, well, Belmonte's work was done initially on progeria mice. These are mice that age prematurely. And then people thought, well, they may not represent natural aging, and what you're doing is simply helping with some abnormal form of aging. But he and other groups have now done it with normal mice and observed similar effects. Now, I would say reprogramming is one way. It's a very exciting and powerful way to almost try to reverse aging because you're trying to take cells back developmentally. You're taking possibly fully differentiated cells back to stem cells and then helping regenerate tissue, which one of the problems as we age is we start losing stem cells. So we have stem cell depletion, so we can no longer replace our tissues as we do when we're younger. And I think anyone who knows who's had a scrape or been hurt in a fall or something knows this because if I fall and scrape my elbow and get a big bruise and my grandson falls, we repair our tissues at very, very different rates. It takes me days or weeks to recover, and my grandson's fine in two or three days. You can hardly see he had a scrape at all. So I think that's the thing that these guys want to do. Venki Ramakrishnan (10:48): And the problem is Yamanaka factors are cancer. Two of them are oncogenic factors, right? If you give Yamanaka factors to cells, you can take them all the way back to what are called pluripotent cells, which are the cells that are capable of forming any tissue in the body. So for example, a fertilized egg or an early embryo cells from the early embryo are pluripotent. They could form anything in the body. Now, if you do that to cells with Yamanaka factors, they often form teratomas, which are these unusual forms of cancer tumors. And so, I think there's a real risk. And so, what these guys say is, well, we'll give these factors transiently, so we'll only take the cells back a little ways and not all the way back to pluripotency. And that way if you start with skin cells, you'll get the progenitor stem cells for skin cells. And the problem with that is when you do it with a population, you're getting a distribution. Some of them will go back ju

After finishing her training in neurology at Mayo Clinic, Dr. Svetlana Blitshteyn started a Dysautonomia Clinic in 2009. Little did she know what was in store many years later when Covid hit! Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube Transcript with audio and external links Eric Topol (00:07): Well, hello, it's Eric Topol from Ground Truths, and I have with me a really great authority on dysautonomia and POTS. We will get into what that is for those who aren't following this closely. And it's Svetlana Blitshteyn who is a faculty member at University of Buffalo and a neurologist who long before there was such a thing as Covid was already onto one of the most important pathways of the body, the autonomic nervous system and how it can go off track. So welcome, Svetlana. Svetlana Blitshteyn (00:40): Thank you so much, Eric for having me. And I want to say it's a great honor for me to be here and just to be on the list with your other guests. It's remarkable and I'm very grateful and congratulations on being on the TIME100 Health list for influential people in 2024. And I am grateful for everything that you've done. As I mentioned earlier, I'm a big fan of your work before the pandemic and of course with Covid I followed your podcast and posts because you became the best science communicator and I'm very happy to see you being a strong advocate and thank you for everything you've done. Eric Topol (01:27): Well, that's so kind to you. And I think talking about getting things going before the pandemic, back in 2011, you published a book with Jodi Epstein Rhum called POTS - Together We Stand: Riding the Waves of Dysautonomia. And you probably didn't have an idea that there would be an epidemic of that more than a decade later, I guess, right? Svetlana Blitshteyn (01:54): Yeah, absolutely. Of course, SARS-CoV-2 is a new virus and we can technically say that Long Covid and post Covid complications could be viewed as a new entity. But practically speaking, we know that post-infectious syndromes have been happening for many decades. And so, the most common trigger for POTS happened to be infection, whether it was influenza or mononucleosis or Lyme or enterovirus. We knew this was happening. So I think it didn't take long for me and my colleagues to realize that we're going to be seeing a lot of patients with autonomic dysfunction after Covid. On the Front Line Eric Topol (02:40): Well, one of the things that's important for having you on is you're in the front lines taking care of lots of patients with Long Covid and this postural orthostatic tachycardia syndrome (POTS). And I wonder if you could tell us what it's care for these patients because so many of them are incapacitated. As a cardiologist, I see of course some because of the cardiovascular aspects, but you are dealing with this on a day-to-day basis. Svetlana Blitshteyn (03:14): Yeah, absolutely. As early as April 2020 when everything was closed, I got a call from a young doctor in New York City saying that he had Covid and he couldn't recover, he couldn't return to the hospital. And his colleagues and cardiology attendants also had the same symptoms and the symptoms were palpitations, orthostatic intolerance, tachycardia, fatigue. Now, how he knew to contact me is that his sister was my patient with POTS before Covid pandemic. So he kind of figured this looked like my sister, let me check this out. And it didn't take long for me to have a lot of patience from the early wave. And then fairly soon, I think within months I was thinking, we have to write this up because this is important. And to some of us it was not news, but I was sure that to many physicians and public health officials, this would be something new. Svetlana Blitshteyn (04:18): So because I'm a busy clinician and don't have a lot of time for publications, I had to recruit a graduate student from McMasters and together we had this paper out, which was the first and largest case series on post Covid POTS and other autonomic disorders. And interestingly, even though it came out I think in 2021, by the time it was published, it became the most citable paper for me. And so I think from then on organizations and societies became interested in the work that I do because prior to that, I must say in the kind of a niche specialty was I don't think it was very popular or of interest to me. How Did You Get Interested in Dysautonomia? Eric Topol (05:06): Yeah, so that's why I wanted to just take a step back with you Svetlana, because you had the foresight to be the founder and director of the Dysautonomia Clinic when a lot of people weren't in touch with this as an important entity. What prompted you as a neurologist to really zoom in on dysautonomia when you started this clinic? Svetlana Blitshteyn (05:28): Sure. So the reasons are how I ended up in this field is kind of a convoluted road and the reasons are many, but one, I will say that I trained at Mayo Clinic where we received very good training on autonomic disorders and EMG and coming back to returning back to Buffalo, I began working at the large multiple sclerosis clinic because Western New York has a high incidence MS. And so, what they quickly realized in that clinic is that there was a subset of women who did not qualify for the diagnostic criteria of multiple sclerosis, yet they had a lot of the same symptoms and they were certainly very disabled. Now I recognize that these women had autonomic disorders of all sorts and small fiber neuropathy, and I think this population sort of grew and eventually I realized there is no one not only in Buffalo but the entire Western New York who is doing this work. Svetlana Blitshteyn (06:34): So I kind of fell into that. But another reason is actually more personal that I haven’t talked about. So years ago I was traveling to Toronto, Canada for a neurology meeting to present my big study on meningioma and hormone replacement therapy using Mayo Clinic database. And so, in that year, the study received top 10 noteworthy studies of the year award from the Society of Neuro-Oncology, and it was profiled in Reuters Health. Now, on the way back from the conference, I had the flu, and when they returned I could no longer walk the same hallways of the hospital where I walked previously. And no matter how hard I try to push my body, we all do this in medicine, we push through, I just couldn’t do it. No amount of wishing or positive thinking. And so, I think that’s how I came to know personally the post-infectious syndromes. And I think it almost became a duality of experiencing this and also practicing it. Eric Topol (07:52): No, that’s really striking and it wasn’t so common to hear about this post flu, but certainly it changed in 2020. So how does a person with POTS typically present to you? Clinical Presentation Svetlana Blitshteyn (08:08): So these are very important questions because what I want to stress is though POTS is one of the most common autonomic disorders. Even if you don’t have POTS by the diagnostic criteria, you may still have autonomic dysfunction and significant autonomic symptoms. How do they present? Well, they present like most Long Covid patients, the most common symptoms are orthostatic intolerance, fatigue, exercise intolerance, post exertional malaise, dizziness, tachycardia, brain fog. And these are common themes across the board in Long Covid patients, but also in pre-Covid post-acute infection syndrome patients. And you have to recognize because I think what I tell my colleagues is that oftentimes patients are not going to present to you saying, I have orthostatic intolerance. Many times they will say, I’m very tired. I can no longer go to the gym or when I go to the store, I have to be out of there in 15 minutes because the orthostatic intolerance symptoms come up. Svetlana Blitshteyn (09:22): So sometimes the patients themselves don’t recognize that and it’s up to us physicians to ask the right questions to get the information down. History is very important, knowing the pattern. And then of course, as I always say in all of my papers and lectures, you have to do a 10-minute stand test by measuring supine and standing blood pressure and heart rate on every Long Covid patients. And that’s how you spot those that have excessive postural tachycardia or their blood pressure dropping or so forth. So we have the tools. We don’t need fancy autonomic labs. We don’t even need a tilt table test. The diagnostic criteria for POTS is that you need to have either a 10-minute stand test or a tilt table test to get the diagnosis for POTS, orthostatic hypotension or even neurocardiogenic syncope. Now I think it's important to stress that even if a patient doesn't qualify, and let's say many patients with Long Covid will not elevate their heart rate by at least 30 beats per minute, it could be 20, it could be 25. These criteria are of course essential when we do research studies. But I think practically speaking, in patient care where everything is gray and nothing is black or white, especially in autonomic disorders, you really have to make a diagnosis saying, this sounds like autonomic dysfunction. Let me treat the patient for this problem. Eric Topol (11:07): Well, you brought up something that’s really important because doctors don’t have much time and they’re inpatient. They don’t wait 10 minutes to do a test to check your blood pressure. They send the patients for a tilt table, which nobody likes to have that test done, and it’s unnecessary added appointment and expense and whatnot. So that’s a good tip right there that you can get the same information just by checking the blood pressure and heart rate on standing for an extended period of time, which 10 minutes is a long time in the clinic of course. Now, what is the mechanism, what do you think is going on with the SARS-CoV-2 virus and its predilection to affect the

“We haven't invested this much money into an infrastructure like this really until you go back to the pyramids”—Kate Crawford Transcript with links to audio and external links. Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube Eric Topol (00:06): Well, hello, this is Eric Topol with Ground Truths, and I'm really delighted today to welcome Kate Crawford, who we're very lucky to have as an Australian here in the United States. And she's multidimensional, as I've learned, not just a scholar of AI, all the dimensions of AI, but also an artist, a musician. We're going to get into all this today, so welcome Kate. Kate Crawford (00:31): Thank you so much, Eric. It's a pleasure to be here. Eric Topol (00:34): Well, I knew of your work coming out of the University of Southern California (USC) as a professor there and at Microsoft Research, and I'm only now learning about all these other things that you've been up to including being recognized in TIME 2023 as one of 100 most influential people in AI and it's really fascinating to see all the things that you've been doing. But I guess I'd start off with one of your recent publications in Nature. It was a world view, and it was about generative AI is guzzling water and energy. And in that you wrote about how these large AI systems, which are getting larger seemingly every day are needing as much energy as entire nations and the water consumption is rampant. So maybe we can just start off with that. You wrote a really compelling piece expressing concerns, and obviously this is not just the beginning of all the different aspects you've been tackling with AI. Exponential Growth, Exponential Concerns Kate Crawford (01:39): Well, we're in a really interesting moment. What I've done as a researcher in this space for a very long time now is really introduce a material analysis of artificial intelligence. So we are often told that AI is a very immaterial technology. It's algorithms in the cloud, it's objective mathematics, but in actual fact, it comes with an enormous material infrastructure. And this is something that I took five years to research for my last book, Atlas of AI. It meant going to the mines where lithium and cobalt are being extracted. It meant going into the Amazon fulfillment warehouses to see how humans collaborate with robotic and AI systems. And it also meant looking at the large-scale labs where training data is being gathered and then labeled by crowd workers. And for me, this really changed my thinking. It meant that going from being a professor for 15 years focusing on AI from a very traditional perspective where we write papers, we're sitting in our offices behind desks, that I really had to go and do these journeys, these field trips, to understand that full extractive infrastructure that is needed to run AI at a planetary scale. (02:58): So I've been keeping a very close eye on what would change with generative AI and what we've seen particularly in the last two years has been an extraordinary expansion of the three core elements that I really write about in Atlas, so the extraction of data of non-renewable resources, and of course hidden labor. So what we've seen, particularly on the resources side, is a gigantic spike both in terms of energy and water and that's often the story that we don't hear. We're not aware that when we're told about the fact that there gigantic hundred billion computers that are now being developed for the next stage of generative AI that has an enormous energy and water footprint. So I've been researching that along with many others who are now increasingly concerned about how we might think about AI more holistically. Eric Topol (03:52): Well, let's go back to your book, which is an extraordinary book, the AI Atlas and how you dissected not just the well power of politics and planetary costs, but that has won awards and it was a few years back, and I wonder so much has changed since then. I mean ChatGPT in late 2022 caught everybody off guard who wasn't into this knowing that this has been incubating for a number of years, and as you said, these base models are just extraordinary in every parameter you can think about, particularly the computing resource and consumption. So your concerns were of course registered then, have they gone to exponential growth now? Kate Crawford (04:45): I love the way you put that. I think you're right. I think my concerns have grown exponentially with the models. But I was like everybody else, even though I've been doing this for a long time and I had something of a heads up in terms of where we were moving with transformer models, I was also quite taken aback at the extraordinary uptake of ChatGPT back in November 2022 in fact, gosh, it still feels like yesterday it's been such an extraordinary timescale. But looking at that shift to a hundred million users in two months and then the sort of rapid competition that was emerging from the major tech companies that I think really took me by surprise, the degree to which everybody was jumping on the bandwagon, applying some form of large language model to everything and anything suddenly the hammer was being applied to every single nail. (05:42): And in all of that sound and fury and excitement, I think there will be some really useful applications of these tools. But I also think there's a risk that we apply it in spaces where it's really not well suited that we are not looking at the societal and political risks that come along with these approaches, particularly next token prediction as a way of generating knowledge. And then finally this bigger set of questions around what is it really costing the planet to build these infrastructures that are really gargantuan? I mean, as a species, we haven't invested this much money into an infrastructure like this really until you go back to the pyramids, you really got to go very far back to say that type of just gargantuan spending in terms of capital, in terms of labor, in terms of all of the things are required to really build these kinds of systems. So for me, that's the moment that we're in right now and perhaps here together in 2024, we can take a breath from that extraordinary 18 month period and hopefully be a little more reflective on what we're building and why and where will it be best used. Propagation of Biases Eric Topol (06:57): Yeah. Well, there's so many aspects of this that I'd like to get into with you. I mean, one of course, you're as a keen observer and activist in this whole space, you've made I think a very clear point about how our culture is mirrored in our AI that is our biases, and people are of course very quick to blame AI per se, but it seems like it's a bigger problem than just that. Maybe you could comment about, obviously biases are a profound concern about propagation of them, and where do you see where the problem is and how it can be attacked? Kate Crawford (07:43): Well, it is an enormous problem, and it has been for many years. I was first really interested in this question in the era that was known as the big data era. So we can think about the mid-2000s, and I really started studying large scale uses of data in scientific applications, but also in what you call social scientific settings using things like social media to detect and predict opinion, movement, the way that people were assessing key issues. And time and time again, I saw the same problem, which is that we have this tendency to assume that with scale comes greater accuracy without looking at the skews from the data sources. Where is that data coming from? What are the potential skews there? Is there a population that's overrepresented compared to others? And so, I began very early on looking at those questions. And then when we had very large-scale data sets start to emerge, like ImageNet, which was really perhaps the most influential dataset behind computer vision that was released in 2009, it was used widely, it was freely available. (09:00): That version was available for over a decade and no one had really looked inside it. And so, working with Trevor Paglen and others, we analyzed how people were being represented in this data set. And it was really quite extraordinary because initially people are labeled with terms that might seem relatively unsurprising, like this is a picture of a nurse, or this is a picture of a doctor, or this is a picture of a CEO. But then you look to see who is the archetypical CEO, and it's all pictures of white men, or if it's a basketball player, it's all pictures of black men. And then the labeling became more and more extreme, and there are terms like, this is an alcoholic, this is a corrupt politician, this is a kleptomaniac, this is a bad person. And then a whole series of labels that are simply not repeatable on your podcast. (09:54): So in finding this, we were absolutely horrified. And again, to know that so many AI models had trained on this as a way of doing visual recognition was so concerning because of course, very few people had even traced who was using this model. So trying to do the reverse engineering of where these really problematic assumptions were being built in hardcoded into how AI models see and interpret the world, that was a giant unknown and remains to this day quite problematic. We did a recent study that just came out a couple of months ago looking at one of the biggest data sets behind generative AI systems that are doing text to image generation. It's called LAION-5B, which stands for 5 billion. It has 5 billion images and text captions drawn from the internet. And you might think, as you said, this will just mirror societal biases, but it's actually far more weird than you might imagine. (10:55): It's not a representative sample even of the internet because particularly for these data sets that are now trying to use the ALT tags that are used around images, who uses ALT tags the most on the i

If there’s one person you’d want to talk to about immunology, the immune system and Covid, holes in our knowledge base about the complex immune system, and where the field is headed, it would be Professor Iwasaki. And add to that the topic of Women in Science. Here’s our wide-ranging conversation. A snippet of the video, Full length Ground Truths videos are posted here and you can subscribe. Ground Truths is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Transcript with many external link and links to the audio, recorded 30 April 2024 Eric Topol (00:06): Hello, it's Eric Topol and I'm really thrilled to have my friend Akiko Iwasaki from Yale, and before I start talking with Akiko, I just want to mention there aren't too many silver linings of the pandemic, but one for me was getting to know Professor Iwasaki. She is my go-to immunologist. I've learned so much from her over the last four years and she's amazing. She just, as you may know, she was just recently named one of the most influential people in the world by TIME100. [and also recognized this week in TIME 100 Health]. And besides that, she's been elected to the National Academy of Medicine, National Academy of Sciences. She's the president of the American Association of Immunologists and she's a Howard Hughes principal investigator. So Akiko, it's wonderful to have you to join into an extended discussion of things that we have of mutual interest. Akiko Iwasaki (01:04): Thank you so much, Eric, for having me. I equally appreciate all of what you do, and I follow your blog and tweets and everything. So thank you Eric. Eric Topol (01:14): Well, you are a phenom. I mean just, that's all I can say because I think it was so appropriate that TIME recognize your contributions, not just over the pandemic, but of course throughout your career, a brilliant career in immunology. I thought we'd start out with our topic of great interest on Long Covid. You've done seminal work here and this is an evolving topic obviously. I wonder what your latest thoughts are on the pathogenesis and where things are headed. Long Covid Akiko Iwasaki (01:55): Yeah, so as I have been saying throughout the pandemic, I think that Long Covid is not one disease. It's a collection of multiple diseases and that are sort of ending up in similar sets of symptoms. Obviously, there are over 200 symptoms and not everyone has the same set of symptoms, but what we are going for is trying to understand the disease drivers, so persistent viral infection is one of them. There are overwhelming evidence for that theory now, all the way from autopsy and biopsy studies to looking at peripheral blood RNA signatures as well as circulating spike protein and nucleocapsid proteins that are detected in people with Long Covid. Now whether that persistent virus or remnants of virus is driving the disease itself is unclear still. And that's why trials like the one that we are engaging with Harlan Krumholz on Paxlovid should tell us what percentage of the people are suffering from that type of driver and whether antivirals like Paxlovid might be able to mitigate those. If I may, I'd like to talk about three other hypotheses. Eric Topol (03:15): Yeah, I'd love for you to do that. Akiko Iwasaki (03:18): Okay, great. So the second hypothesis that we've been working on is autoimmune disease. And so, this is clearly happening in a subset of people, again, it's a heterogeneous disease, but we can actually not only look at reactogenicity of antibodies from people with Long Covid where we can transfer IgG from patients with Long Covid into an animal, a healthy animal, and really measure outcomes of a pathogenesis. So that's a functional evidence that antibodies in some people with Long Covid is really actually causing some of the damages that are occurring in vivo. And the third hypothesis is the reactivation of herpes viruses. So many of us adults have multiple latent herpes virus family members that are just dormant and are not really causing any pathologies. But in people with Long Covid, we're seeing elevated reactivation of viruses like Epstein-Barr virus (EBV) or Varicella-zoster virus (VZV) and that may again be just a signature of Long Covid, but it may also be driving some of the symptoms that people are suffering from. (04:32): So that's again, we see the signature over and over, not just our group, but multiple other groups, Michael Peluso's group, Jim Heath, and many others. So that's also an emerging evidence from multiple groups showing that. And finally, we think that inflammation that occurs during the acute phase can sort of chronically change some tissue tone. For instance, in the brain with Michelle Monje’s team, we developed a sort of localized mild Covid model of infection and showed that changes in microglia can be seen seven weeks post infection even though the virus is completely gone. So that means that inflammation that's established as a result of this initial infection can have prolonged sequence and sequela within the person and that may also be driving disease. And Eric, the reason we need to understand these diseases separately is because not only for diagnostic purposes, but for therapeutic purposes because to target a persistent virus is very different approach from targeting autoantibodies, for example. Eric Topol (05:49): Well, that's great. There's a lot to unpack there as you laid out four distinct paths that could result in the clinical syndrome and sequelae. I think you know I had the chance to have a really fun conversation with Michelle about their joint work that you've done, and she reminded me how she made a cold call to you to start as a collaboration, which I thought was fantastic. Look what that yielded. But yeah, this is fascinating because as I think you're getting at is that it may not be the same pathogenesis in any given individual so that all these, and even others might be operative. I guess maybe I first delve into the antibody story as you're well aware, we see after people get Covid a higher rate of autoimmune diseases crop up, which is really interesting because it seems to rev up self-directed immune response. And this I think many people haven't really noted yet, although obviously you're well aware of this, it's across all the different autoimmune diseases, connective tissue disease, not just one in particular. And it's, as you say, the idea that you could take the blood from a person suffering from Long Covid and give it to an experimental animal model and be able to recapitulate some of the abnormalities, it's really pretty striking. So the question I guess is if you were to do plasmapheresis and try to basically expunge these autoantibodies, wouldn't you expect people to have some symptomatic benefit pretty rapidly or is it just that the process is already far from the initiating step? Akiko Iwasaki (07:54): That's a great question. Plasmapheresis may be able to transiently improve the person if they're suffering from these autoantibody mediated diseases. People have reported, for example, IVIG treatment has dramatically improved their symptoms, but not in everybody. So it's really critical to understand who's suffering from this particular driver and appropriately treat those people. And there are many other very effective therapies in autoimmune disease field that can be repurposed for treating these patients as well. Eric Topol (08:34): The only clinical trial that has clicked so far, interestingly, came out of Hong Kong with different types of ways to manipulate the gut microbiome, which again, you know better than me is a major modulator of our immune system response. What are your thoughts about taking advantage of that way to somehow modulate this untoward immune response in people with this condition? Akiko Iwasaki (09:07): Yeah, so that is an exciting sort of development, and I don't mean to discount the importance of microbiome at all. It's just the drivers that are mentioning are something that can be directly linked to disease, but certainly dysbiosis and translocation of metabolites and microbiome itself could trigger Long Covid as well. So it's something that we're definitely keeping our eyes on. And as you say, Eric, the immune system is in intimate contact with the gut microbiome and also the gut is intimate contact with the brain. So there's a lot of connections that we really need to be paying attention to. So yeah, absolutely. This is a very exciting development. Eric Topol (09:57): And it is intriguing of course, the reactivation of viruses. I mean, we’ve learned in recent years how important EBV is in multiple sclerosis (MS). The question I have for you on that pathway, is this just an epiphenomena or do you actually think that could be a driving force in some people? Akiko Iwasaki (10:19): Yeah, so that's really hard to untangle in people. I mean, David Putrino and my team we're planning a clinical trial using Truvada. Truvada obviously is an HIV drug, but it has reported antiviral activity to Epstein-Barr virus (EBV) and others. So potentially we can try to interrogate that in people, but we're also developing mouse models that can sort of recapitulate EBV like viral reactivation and to see whether there's any sort of causal link between the reactivation and disease process. Eric Topol (10:57): Right now, recently there's been a bunch of anecdotes of people who get the glucagon-like peptide one (GLP-1) drugs which have a potent anti-inflammatory, both systemic and in the brain. I'd love to test these drugs, but of course these companies that make them or have other interests outside of Long Covid, do you think there's potential for a drug like that? Akiko Iwasaki (11:23): Yeah, so those drugs seem to have a lot of miraculous effects on every disease. So obviously it has to be used carefully because many people with Long Covid have issues with live

“Where do I think the next amazing revolution is going to come? … There’s no question that digital biology is going to be it. For the very first time in our history, in human history, biology has the opportunity to be engineering, not science.” —Jensen Huang, NVIDIA CEO Aviv Regev is one of the leading life scientists of our time. In this conversation, we cover the ongoing revolution in digital biology that has been enabled by new deep knowledge on cells, proteins and genes, and the use of generative A.I . Transcript with audio and external links Eric Topol (00:05): Hello, it's Eric Topol with Ground Truths and with me today I've really got the pleasure of welcoming Aviv Regev, who is the Executive Vice President of Research and Early Development at Genentech, having been 14 years a leader at the Broad Institute and who I view as one of the leading life scientists in the world. So Aviv, thanks so much for joining. Aviv Regev (00:33): Thank you for having me and for the very kind introduction. The Human Cell Atlas Eric Topol (00:36): Well, it is no question in my view that is the truth and I wanted to have a chance to visit a few of the principal areas that you have been nurturing over many years. First of all, the Human Cell Atlas (HCA), the 37 trillion cells in our body approximately a little affected by size and gender and whatnot, but you founded the human cell atlas and maybe you can give us a little background on what you were thinking forward thinking of course when you and your colleagues initiated that big, big project. Aviv Regev (01:18): Thanks. Co-founded together with my very good friend and colleague, Sarah Teichmann, who was at the Sanger and just moved to Cambridge. I think our community at the time, which was still small at the time, really had the vision that has been playing out in the last several years, which is a huge gratification that if we had a systematic map of the cells of the body, we would be able both to understand biology better as well as to provide insight that would be meaningful in trying to diagnose and to treat disease. The basic idea behind that was that cells are the basic unit of life. They're often the first level at which you understand disease as well as in which you understand health and that in the human body, given the very large number of individual cells, 37.2 trillion give or take, and there are many different characteristics. (02:16): Even though biologists have been spending decades and centuries trying to characterize cells, they still had a haphazard view of them and that the advancing technology at the time – it was mostly single cell genomics, it was the beginnings also of spatial genomics – suggested that now there would be a systematic way, like a shared way of doing it across all cells in the human body rather than in ways that were niche and bespoke and as a result didn't unify together. I will also say, and if you go back to our old white paper, you will see some of it that we had this feeling because many of us were computational scientists by training, including both myself and Sarah Teichmann, that having a map like this, an atlas as we call it, a data set of this magnitude and scale, would really allow us to build a model to understand cells. Today, we call them foundational models or foundation models. We knew that machine learning is hungry for these kinds of data and that once you give it to machine learning, you get amazing things in return. We didn't know exactly what those things would be, and that has been playing out in front of our eyes as well in the last couple of years. Spatial Omics Eric Topol (03:30): Well, that gets us to the topic you touched on the second area I wanted to get into, which is extraordinary, which is the spatial omics, which is related to the ability to the single cell sequencing of cells and nuclei and not just RNA and DNA and methylation and chromatin. I mean, this is incredible that you can track the evolution of cancer, that the old word that we would say is a tumor is heterogeneous, is obsolete because you can map every cell. I mean, this is just changing insights about so much of disease health mechanisms, so this is one of the hottest areas of all of life science. It's an outgrowth of knowing about cells. How do you summarize this whole era of spatial omics? Aviv Regev (04:26): Yeah, so there's a beautiful sentence in the search for lost time from Marcel Proust that I'm going to mess up in paraphrasing, but it is roughly that going on new journeys is not about actually going somewhere physically but looking with new eyes and I butchered the quote completely.[See below for actual quote.] I think that is actually what single cells and then spatial genomics or spatial omics more broadly has given us. It's the ability to look at the same phenomenon that we looked at all along, be it cancer or animal development or homeostasis in the lung or the way our brain works, but having new eyes in looking and because these new eyes are not just seeing more of something we've seen before, but actually seeing things that we couldn't realize were there before. It starts with finding cells we didn't know existed, but it's also the processes that these cells undergo, the mechanisms that actually control that, the causal mechanisms that control that, and especially in the case of spatial genomics, the ways in which cells come together. (05:43): And so we often like to think about the cell because it's the unit of life, but in a multicellular organism we just as much have to think about tissues and after that organs and systems and so on. In a tissue, you have this amazing orchestration of the interactions between different kinds of cells, and this happens in space and in time and as we're able to look at this in biology often structure is tightly associated to function. So the structure of the protein to the function of the protein in the same way, the way in which things are structured in tissue, which cells are next to each other, what molecules are they expressing, how are they physically interacting, really tells us how they conduct the business of the tissue. When the tissue functions well, it is this multicellular circuit that performs this amazing thing known as homeostasis. (06:36): Everything changes and yet the tissue stays the same and functions, and in disease, of course, when these connections break, they're not done in the right way you end up with pathology, which is of course something that even historically we have always looked at in the level of the tissue. So now we can see it in a much better way, and as we see it in a better way, we resolve better things. Yes, we can understand better the mechanisms that underlie the resistance to therapeutics. We can follow a temporal process like cancer as it unfortunately evolves. We can understand how autoimmune disease plays out with many cells that are actually bent out of shape in their interactions. We can also follow magnificent things like how we start from a single cell, the fertilized egg, and we become 37.2 trillion cell marvel. These are all things that this ability to look in a different way allows us to do. Eric Topol (07:34): It's just extraordinary. I wrote at Ground Truths about this. I gave all the examples at that time, and now there's about 50 more in the cardiovascular arena, knowing with single cell of the pineal gland that the explanation of why people with heart failure have sleep disturbances. I mean that's just one of the things of so many now these new insights it's really just so remarkable. Now we get to the current revolution, and I wanted to read to you a quote that I have. Digital Biology Aviv Regev (08:16): I should have prepared mine. I did it off the top of my head. Eric Topol (08:20): It's actually from Jensen Huang at NVIDIA about the digital biology [at top of the transcript] and how it changes the world and how you're changing the world with AI and lab in the loop and all these things going on in three years that you've been at Genentech. So maybe you can tell us about this revolution of AI and how you're embracing it to have AI get into positive feedbacks as to what experiment to do next from all the data that is generated. Aviv Regev (08:55): Yeah, so Jensen and NVIDIA are actually great partners for us in Genentech, so it's fun to contemplate any quote that comes from there. I'll actually say this has been in the making since the early 2010s. 2012 I like to reflect on because I think it was a remarkable year for what we're seeing right now in biology, specifically in biology and medicine. In 2012, we had the beginnings of really robust protocols for single cell genomics, the first generation of those, we had CRISPR happen as a method to actually edit cells, so we had the ability to manipulate systems at a much better way than we had before, and deep learning happened in the same year as well. Wasn't that a nice year? But sometimes people only realize the magnitude of the year that happened years later. I think the deep learning impact people realized  first, then the single cells, and then the CRISPR, then the single cells. (09:49): So in order maybe a little bit, but now we're really living through what that promise can deliver for us. It's still the early days of that, of the delivery, but we are really seeing it. The thing to realize there is that for many, many of the problems that we try to solve in biomedicine, the problem is bigger than we would ever be able to perform experiments or collect data. Even if we had the genomes of all the people in the world, all billions and billions of them, that's just a smidge compared to all of the ways in which their common variants could combine in the next person. Even if we can perturb and perturb and perturb, we cannot do all of the combinations of perturbations even in one cell type, let alone the many different cell types that are out there. So even if we search

Professor Doudna was awarded the 2020 Nobel Prize in Chemistry with Professor Emmanuelle Charpentier for their pioneering work in CRISPR genome editing. The first genome editing therapy (Casgevy) was just FDA approved, only a decade after the CRISPR-Cas9 editing system discovery. But It’s just the beginning of a much bigger impact story for medicine and life science. Ground Truths podcasts are now on Apple and Spotify. And if you prefer videos, they are posted on YouTube Transcript with links to audio and relevant external links Eric Topol (00:06): This is Eric Topol with Ground Truths, and I'm really excited today to have with me Professor Jennifer Doudna, who heads up the Innovative Genomics Institute (IGI) at UC Berkeley, along with other academic appointments, and as everybody knows, was the Nobel laureate for her extraordinary discovery efforts with CRISPR genome editing. So welcome, Jennifer. Jennifer Doudna (00:31): Hello, Eric. Great to be here. Eric Topol (00:34): Well, you know we hadn't met before, but I felt like I know you so well because this is one of my favorite books, The Code Breaker. And Walter Isaacson did such a wonderful job to tell your story. What did you think of the book? My interview with Walter Isaacson on The Code Breaker, a book I highly recommend Jennifer Doudna (00:48): I thought Walter did a great job. He's a good storyteller, and as you know from probably from reading it or maybe talking to others about it, he wrote a page turner. He actually really dug into the science and all the different aspects of it that I think created a great tale. Eric Topol (01:07): Yeah, I recommended highly. It was my favorite book when it came out a couple years ago, and it is a page turner. In fact, I just want to read one, there's so many quotes out of it, but in the early part of the book, he says, “the invention of CRISPR and the plague of Covid will hasten our transition to the third great revolution of modern times. These revolutions arose from the discovery beginning just over a century ago, of the three fundamental kernels of our existence, the atom, the bit, and the gene.” That kind of tells a big story just in one sentence, but I thought I’d start with the IGI, the institute that you have set up at Berkeley and what its overall goals are. Jennifer Doudna (01:58): Right. Well, let's just go back a few years maybe to the origins of this institute and my thinking around it, because in the early days of CRISPR, it was clear that we were really at a moment that was quite unique in the sense that there was a transformative technology. It was going to intersect with lots of other discoveries and technologies. And I work at a public institution and my question to myself was, how can I make sure that this powerful tool is first of all used responsibly and secondly, that it's used in a way that benefits as many people as possible, and it's a tall order, but clearly we needed to have some kind of a structure that would allow people to work together towards those goals. And that was really the mission behind the IGI, which was started as a partnership between UC Berkeley and UCSF and now actually includes UC Davis as well. The First FDA Approved Genome Editing Eric Topol (02:57): I didn't realize that. That's terrific. Well, this is a pretty big time because 10 years or so, I guess starting to be 11 when you got this thing going, now we're starting to see, well, hundreds of patients have been treated and in December the FDA approved the first CRISPR therapy for sickle cell disease, Casgevy. Is that the way you say it? Jennifer Doudna (03:23): Casgevy, yeah. Eric Topol (03:24): That must have felt pretty good to see if you go from the molecules to the bench all the way now to actually treating diseases and getting approval, which is no easy task. Jennifer Doudna (03:39): Well, Eric, for me, I'm a biochemist and somebody who has always worked on the fundamentals of biology, and so it's really been extraordinary to see the pace at which the CRISPR technology has been adopted, and not just for fundamental research, but also for real applications. And Casgevy is sort of the crowning example of that so far, is that it's really a technology that we can already see how it's being used to, I think it's fair to say, effectively cure a genetic disease for the first time. Really amazing. Genome Editing is Not the Same as Gene Therapy Eric Topol (04:17): Yeah. Now I want to get back to that. I know there's going to be refinements about that. And of course, there's beta thalassemia, so we've got two already, and our mutual friend Fyodor Urnov would say two down 5,000 to go. But I think before I get to the actual repair of the sickle cell defect molecular defect, I think one of the questions I think that people listeners may not know is the differentiation of genome editing with gene therapy. I mean, as you know, there was recently a gene therapy approval for something like $4.25 million for metachromatic leukodystrophy. So maybe you could give us kind of skinny on how these two fundamental therapies are different. Jennifer Doudna (05:07): Right. Well, it's a great question because the terminology sounds kind of the same, and so it could be confusing. Gene therapy goes back decades, I can remember gene therapy being discussed as an exciting new at the time, direction back when I was a graduate student. That was little while ago. And it refers to the idea that we can use a genetic approach for disease treatment or even for a cure. However, it fundamentally requires some mechanism of integrating new information into a genome. And traditionally that's been done using viruses, which are great at doing that. It's just that they do it wherever they want to do it, not necessarily where we want that information to go. And this is where CRISPR comes in. It's a technology allows precision in that kind of genetic manipulation. So it allows the scientist or the clinician to decide where to make a genetic change. And that gives us tremendous opportunity to do things with a kind of accuracy that hasn't been possible before. Eric Topol (06:12): Yeah, no question. That's just a footnote. My thesis in college at University of Virginia, 1975, I'm an old dog, was prospects for gene therapy in man. So it took a while, didn't it? But it's a lot better now with what you've been working on, you and your colleagues now and for the last decade for sure. Now, what I was really surprised about is it's not just of course, these hemoglobin disorders, but now already in phase two trials, you've got hereditary angioedema, which is a life-threatening condition, amyloidosis, cancer ex vivo, and also chronic urinary tract infections. And of course, there's six more others like autoimmune diseases like lupus and type 1 diabetes. So this is really blossoming. It's really extraordinary. Eric Topol (07:11): I mean, wow. So one of the questions I had about phages, because this is kind of going back to this original work and discovery, antimicrobial resistance is really a big problem and it's a global health crisis, and there's only two routes there coming up with new drugs, which has been slow and not really supported by the life science industry. And the other promising area is with phages. And I wonder, since this is an area you know so well, why haven't we put more, we're starting to see more trials in phages. Why haven't we doubled down or tripled down on this to help the antimicrobial resistance problem? Jennifer Doudna (08:00): Well, it's a really interesting area, and as you said, it's kind of one of those areas of science where I think there was interest a while ago and some effort was made for reasons that are not entirely clear to me, at least it fizzled out as a real focused field for a long time. But then more recently, people have realized that there's an opportunity here to take advantage of some natural biology in which viruses can infect and destroy microbes. Why aren't we taking better advantage of that for our own health purposes? So I personally am very excited about this area. I think there's a lot of fundamental work still to be done, but I think there's a tremendous opportunity there as well. CRISPR 2.0 Eric Topol (08:48): Yeah, I sure think we need to invest in that. Now, getting back to this sickle cell story, which is so extraordinary. This is kind of a workaround plan of getting fetal hemoglobin built up, but what about actually repairing, getting to fixing the lesion, if you will? Eric Topol (09:11): Yeah. Is that needed? Jennifer Doudna (09:13): Well, maybe it's worth saying a little bit about how Casgevy works, and you alluded to this. It's not a direct cure. It's a mechanism that allows activation of a second protein called fetal hemoglobin that can suppress the effect of the sickle cell mutation. And it's great, and I think for patients, it offers a really interesting opportunity with their disease that hasn't been available in the past, but at the same time, it's not a true cure. And so the question is could we use a CRISPR type technology to actually make a correction to the genetic defect that directly causes the disease? And I think the answer is yes. The field isn't there quite yet. It's still relatively difficult to control the exact way that DNA editing is occurring, especially if we're doing it in vivo in the body. But boy, many people are working on this, as you probably know. And I really think that's on the horizon. Eric Topol (10:19): Yeah. Well, I think we want to get into the in vivo story as well because that, I think right now it's so complicated for a person to have to go through the procedure to get ultimately this treatment currently for sickle cell, whereas if you could do this in vivo and you could actually get the cure, that would be of the objective. Now, you published just earlier this month in PNAS a wonderful paper about the EDVs and the lipid nanoparticles that are ways that we cou

Note: This podcast is a companion to the Ground Truths newsletter “A Big Week for GLP-1 Drugs” Eric Topol (00:06): It is Eric Topol with Ground Truths, and with me today is Dr. Daniel Drucker from the University of Toronto, who is one of the leading endocrinologists in the world, and he along with Joel Habener and Jens Juul Holst from the University of Copenhagen and Denmark, have been credited with numerous prizes of their discovery work of glucagon-like peptide-1 (GLP-1) as we get to know these family of drugs and he's a true pioneer. He's been working on this for decades. So welcome, Daniel. Daniel Drucker (00:43): Thank you. Eric Topol (00:45): Yeah, it's great to have you and to get the perspective, one of the true pioneers in this field, because to say it's blossom would be an understatement, don't you think? Daniel Drucker (00:57): Yeah, it's been a bit of a hectic three years. We had a good quiet 30 plus years of solid science and then it's just exploded over the last few years. Eric Topol (01:06): Yeah, back in 30 years ago, did you have any sense that this was coming? Daniel Drucker (01:14): Not what we're experiencing today, I think there was a vision for the diabetes story. The first experiments were demonstrating insulin secretion and patents were followed around the use for the treatment of GLP-1 for diabetes. The food intake story was much more gradual and the weight loss story was quite slow. And in fact, as you know, we've had a GLP-1 drug approved for people with obesity since 2014, so it's 10 years since liraglutide was approved, but it didn't really catch the public's attention. The weight loss was good, but it wasn't as spectacular as what we're seeing today. So this really has taken off just over the last three, four years. Eric Topol (01:58): Yeah, no, it's actually, I've never seen a drug class like this in my life, Daniel. I mean, I've obviously witnessed the statins, but this one in terms of pleiotropy of having diverse effects, and I want to get to the brain here in just a minute because that seems to be quite a big factor. But one thing just before we get too deep into this, I think you have been great to recognize one of your colleagues who you work with at Harvard, Svetlana Mojsov. And the question I guess is over the years, as you said, there was a real kind of incremental path and I guess was in 1996 when you said, well, this drug likely will inhibit food intake, but then there were gaps of many years since then, as you mentioned about getting into the obesity side. Was that because there wasn't much weight loss in the people with diabetes or was it related to the dose of the drugs that were being tested? Why Did It Take So Long to Get to Obesity? Daniel Drucker (03:11): Well, really both. So the initial doses we tested for type 2 diabetes did not produce a lot of weight loss, maybe 2-3%. And then when we got semaglutide for type 2 diabetes, maybe we were getting 4-5% mean weight loss. And so that was really good and that was much better than we achieved before with any glucose lowering drug. But a lot of credit goes to Novo Nordisk because they looked at the dose for liraglutide and diabetes, which was 1.8 milligrams once daily for people with type 2 diabetes. And they asked a simple question, what if we increase the dose for weight loss? And the answer was, we get better weight loss with 3 milligrams once a day. So they learn that. And when they introduced semaglutide for type 2 diabetes, the doses were 0.5 and 1 milligrams. But in the back of their minds was the same question, what if we increased the dose and they landed on 2.4 milligrams once a week. And that's when we really started to see that the unexpected spectacular weight loss that we're now quite familiar with. Eric Topol (04:16): Was there also something too that diabetics don't lose as much weight if you were to have match dose? Daniel Drucker (04:22): Yeah, that's a general phenomenon. If one goes from either diet to bariatric surgery, and certainly with weight loss medicines, we tend to see maybe two thirds to three quarters of the amount of weight loss in people with type 2 diabetes. We don't really understand it. The brain pathways are probably resistant to some of the pathways that are activated that lead to weight loss, and it's really an interesting observation that needs further study. The Brain Effect Eric Topol (04:50): Yeah, it's fascinating really. And it might've at least in part, held up this progress that has been truly remarkable. Now, recently you published a paper among many, you're a very prolific scientist, of course, physician scientist, but back in December in Cell Metabolism was a very important paper that explored the brain gut axis, the ability to inhibit inflammation and the mechanism through Toll-like receptors that you were seeing that. So maybe you could summarize the fact that you saw this, you were quoted in this Atlantic piece by Sarah Zhang, the science behind Ozempic was wrong. The weight loss effects of GLP-1 drugs have little to do with the gut and basically claiming that it's related to the effects on the brain, which of course could be reduced inflammation, reduced or inhibiting centers of addiction craving, that sort of thing. So how do you interpret your recent results and ongoing studies regarding GLP-1's effect on the brain? Daniel Drucker (06:02): Sure, so to be clear, I don't think that was a quote. I never would've said the science behind Ozempic was wrong. I think that was a headline writer doing what they do best, which is catching people's attention. I think what I was trying to say is that where this field started with insulin secretion first and then weight loss second, those are clearly very important pharmacological attributes of GLP-1. But physiologically, if we take GLP-1 away or we take the receptor away, you don't really develop diabetes without GLP-1. You don't really gain a lot of weight without GLP-1. So physiologically it's not that important. Why do we have GLP-1 in the distal gut? I think physiologically it's there to defend against infection and reduce gut inflammation. But we noticed that GLP-1 reduces inflammation in many different places in the heart and blood vessels and in the liver and many organs where you don't see a lot of GLP-1 receptors and you don't see a lot of GLP-1 receptors on immune cells. Daniel Drucker (07:04): So that really led us to the question, well, how does it work and affect all these organs where we don't see a lot of the receptors? And that's where we landed on the brain. Obviously the nervous system can communicate with many different cell types in almost every organ. And we identified neurons that expressed the GLP-1 receptor, which when blocked abrogated or completely eliminated the ability of GLP-1 to reduce inflammation in the periphery in white cells or in lungs. So it's been known for some time that the brain can control the immune system. So this is just the latest piece in the puzzle of how GLP-1 might reduce inflammation. Eric Topol (07:49): And just to be clear, I was quoting the Atlantic headline, not you that you were quoted within that article, but this is something that's really interesting because obviously GLP-1 is made in the brain in certain parts of the brain, it's transient in terms of its half-life made from the gut. But when we give these drugs, these agonists, how does it get in the brain? Because isn't there a problem with the blood brain barrier? Daniel Drucker (08:22): So I don't think the drugs get into the brain very well. We have a lot of data on this, so people have done the classic experiments, they either make radioactive ligands or fluorescent ligands, and they look how much gets in it and not very much gets in beyond the blood-brain barrier. And we also have big drugs that are immunoglobulin based and they work really well, so they don't get into the brain very much at all. And so, the way I describe this is that GLP-1 talks to the brain, but it doesn't directly get into the brain to meaningful extent, it does communicate somewhat there are areas obviously that are accessible in the area of the stream and circumventricular organs, but most of the time we have this communication that's not well understood that results in the magic that we see. And there are some discussions around for the neurodegenerative disease story where GLP-1 is being looked at in Parkinson's disease and in people with Alzheimer's disease. Would you be able to get more benefit if you could get the drugs into the brain to a greater extent, or would you simply increase the adverse event profile and the adverse response? So really important area for study as we begin to go beyond diabetes and obesity. Eric Topol (09:41): Yeah, I mean as you're pointing out, there's two ongoing trials, pretty large trials in Alzheimer's, early Alzheimer's, which may be a little bit too late, but at any rate, testing GLP-1 to see whether or not it could help prevent progression of the disease. And as you also mentioned, diseases and Parkinson's. But I guess, so the magic as you referred to it, the gut -brain axis so that when you give the GLP-1 family of drugs, we'll talk more about the double and triple receptor in a moment, but when you give these drugs, how does the message you get from the gut to the brain would you say? Daniel Drucker (10:27): So pharmacologically, we can give someone or an animal the drug, it does reach some of the accessible neurons that have GLP-1 receptors, and they probably transmit signals deeper into the brain and then activate signal transduction. So one way to look at it, if you use c-fos, the protein, which is an immediate early gene, which is increased when we activate neurons, we see rapid activation of c-fos in many regions that are deep within the brain within minutes. And we know that GLP-1 is not getting directly to those neurons, but it's activating pathways that turn on those neurons. And so,

Siddhartha Mukherjee is a Professor at Columbia University, oncologist, and extraordinary author of Emperor of All Maladies (which was awarded a Pulitzer Prize), The Gene, and The Song of the Cell, along with outstanding pieces in the New Yorker. He is one of the top thought leaders in medicine of our era. “I have begun to imagine, think about what it would be to be a digital human..”—Sid Mukherjee Eric Topol (00:06): Well, hello, this is Eric Topol with Ground Truths, and I am delighted to have my friend Sid Mukherjee, to have a conversation about all sorts of interesting things. Sid, his most recent book, SONG OF THE CELL is extraordinary. And I understand, Sid, you're working on another book that may be cell related. Is that right? Sid Mukherjee  (00:30): Eric, it's not cell related, I would say, but it's AI and death related, and it covers, broadly speaking, it covers AI, longevity and death and memory —topics that I think are universal, but also particularly medicine. Eric Topol (00:57): Well, good, and we'll get into that. I had somehow someone steered me that your next book was going to be something building on the last one, but that sounds even more interesting. You're going in another direction. You've covered cancer gene cells, so I think covering this new topic is of particularly interest. So let's get into the AI story and maybe we'll start off with your views on the healthcare side. Where do you think this is headed now? A.I. and Drug Discovery Sid Mukherjee  (01:29): So I think Eric, there are two very broad ways of dividing where AI can enter healthcare, and there may be more, I'm just going to give you two, but there may be more. One is on what I would call the deep science aspect of it, and by that I mean AI-based drug discovery, AI-based antibody discovery, AI-based modeling. All of which use AI tools but are using tools that have to do with machine learning, but may have to do less directly with the kind of large language models. These tools have been in development for a long time. You and I are familiar with them. They are tools. Very simply put, you can imagine that the docking of a drug to a protein, so imagine every drug, every medicine as a small spaceship that docks onto a large spaceship, the large spaceship being the target. (02:57): So if you think of it that way, there are fundamental rules. If anyone's watched Star Wars or any of these sci-fi films, there are fundamental rules by which that govern the way that the small spaceship in this case, a molecule like aspirin fits into a pocket of its target, and those are principles that are determined entirely by chemistry and physics, but they can be taught, you can learn what kind of spaceship or molecule is likely to fit into what kind of pocket of the mothership, in this case, the target. And if they can be learned, they're amenable to AI-based discovery. Eric Topol (03:57): Right. Well, that's, isn't that what you'd call the fancy term structure-based discovery, where you're using such tools like what AlphaFold2 for proteins and then eventually for antibodies, small molecules, et cetera, that you can really rev up the whole discovery of new molecules, right? Sid Mukherjee  (04:21): That's correct, and that's one of the efforts that I'm very heavily involved in. We have created proprietary algorithms that allow us to enable this. Ultimately, of course, there has to be a method by which you start from these AI based methods, then move to physical real chemistry, then move to real biology, then move to obviously human biology and ultimately to human studies. It's a long process, but it's an incredibly fruitful process. Eric Topol (04:57): Well, yeah, as an example that recently we had Jim Collins on the podcast and he talked about the first new drug class of antibiotics in two decades that bind to staph aureus methicillin resistant, and now in clinical trials. So it’s happening. There’s 20 AI drugs in clinical trials out there. Sid Mukherjee  (05:18): It’s bound to happen. It is an unstoppable bound to happen systematology of drug discovery. This is just bound to happen. It is unstoppable. There are kinks in it in the road, but those will be ironed out, but it’s bound to happen. (05:41): So that’s on the very discovery oriented end, which is more related to learning algorithms that have to do with AI and less to do with what we see in day-to-day life, the ChatGPT kind of day-to-day life of the world. On the very other end of the spectrum, just to move along on the very other end of the spectrum are what I would call patient informatics. So by patient informatics, I mean questions like who responds to a particular drug? What genes do they have? What environment are they in? Have they had other drug interactions in the past? What is it about their medical record that will allow us to understand better why or why they're not responding to a medicine? (06:51): Those are also AI, can also be really powered by AI, but are much more dependent and much more sensitive to our understanding of these current models, the large language models. So just to give you an example, let's say you wanted to enroll a clinical trial for patients with diabetes to take a new drug. You could go into the electronic medical record, which right now is a text file, and ask the question, have they or have they not responded to the standard agents? And what has their response been? Should they be on glucose monitoring? How bad is their diabetes based on some laboratory parameters, et cetera, et cetera. So that's a very different information rich, electronic medical record rich mechanism to understand how to develop medicines. One lies, the first lies way in the discovery end of the spectrum. The second lies way in the clinical trials and human drug exposure end of the spectrum. And of course, there are things in the middle that I haven't iterated, but those are the two really broad categories where one can imagine AI making a difference and to be fair through various efforts, I'm working on both of those, the two end spectrum. A.I. and Cancer Eric Topol (08:34): Well, let's drill down a bit more on the person individual informatics for a moment, since you're an oncologist, and the way we screen for cancer today is completely ridiculous by age only. But if you had a person's genome sequence, polygenic risk scores for cancers and all the other known data that, for example, the integrity of their immune system response, environmental exposures, which we'll talk about in a moment more, wouldn't we do far better for being able to identify high risk people and even preventing cancer in the future? Sid Mukherjee  (09:21): So I have no doubt whatsoever that more information that we can analyze using intelligent platforms. And I'm saying all of these words are relevant, more information analyzed through intelligent platforms. More information by itself is often useless. Intelligent platforms without information by themselves are often useless, but more information with intelligent platforms, that combination can be very useful. And so, one use case of that is just to give you one example, there are several patients, women who have a family history of breast cancer, but who have no mutations in the known single monogenic breast cancer risk genes, BRCA1, BRCA2, and a couple of others. Those patients can be at a high a risk of breast cancer as patients who have BRCA1 and BRCA2. It's just that their risk is spread out through not one gene but thousands of genes. And those patients, of course have to be monitored and their risk is high, and they need to understand what the risk is and how to manage it. (10:57): And that's where AI can, and first of all, informatics and then AI can play a big difference because we can understand how to manage those patients. They used to be called, this is kind of, I don't mean this lightly, but they used to be called BRCA3 because they didn't have BRCA1, they didn't have BRCA2, but they had a constellation of genes, not one, not two, but thousands of genes that would increase their risk of breast cancer just a little bit. I often describe these as nudge genes as opposed to shove genes. BRCA1 and BRCA2 are shoved genes. They shove you into having a high risk of breast cancer. But you can imagine that there are nudge genes as well in which they, in which a constellation of not one, not two, not three, but a thousand genetic variations, give a little push each one, a little push towards having a higher risk of breast cancer. (12:09): Now, the only way to find these nudge genes is by doing very clever informatic studies, some of which have been done in breast cancer, ovarian cancer, cardiovascular diseases, other diseases where you see these nudge effects, small effects of a single gene, but accumulated across a thousand, 2000, 3000 genes, an effect that's large enough that it's meaningful. And I think that we need to understand those. And once we understand them, I think we need to understand what to do with these patients. Do we screen them more assertively? Do we recommend therapies? You can get more aggressive, less aggressive, but of course that demands clinical trials and a deeper understanding of the biology of what happens. A.I. And Longevity Eric Topol (13:10): Right, so your point about the cumulative effects of small variants, hundreds and hundreds of these variants being equivalent potentially, as we've seen across many diseases, it's really important and you're absolutely right about that. And I've been pushing for trying to get these polygenic risk scores into clinical routine use, and hopefully we're getting closer to that. And that's just as you say, just one layer of this information to add to the intelligence platform. Now, the next thing that you haven't yet touched on connecting the dots is, can AI and informatics be used to promote longevity? Sid Mukherjee  (13:55): Yeah, so that's a very interesting

There was so much to talk about—this is the longest Ground Truths podcast yet. Hope you’ll find it as thought-provoking as I did! Transcript, with audio and external links, edited by Jessica Nguyen, Producer for Ground Truths Video and audio tech support by Sinjun Balabanoff, Scripps Research Eric Topol (00:00:05): This is Eric Topol from Ground Truths, and I am delighted to have with me Holden Thorp, who is the Editor-in-Chief of the Science journals. We're going to talk about Science, not just the magazine journal, but also science in general. This is especially appropriate today because Holden was just recognized by STAT as one of the leaders for 2024 because of his extraordinary efforts to promote science integrity, so welcome Holden. Holden Thorp (00:00:36): Thanks Eric, and if I remember correctly, you were recognized by STAT in 2022, so it's an honor to join a group that you're in anytime, that's for sure, and great to be on here with you. Eric Topol (00:00:47): Well, that's really kind to you. Let's start off, I think with the journal, because I know that consumes a lot of your efforts and you have five journals within science. Holden Thorp (00:01:02): Oh, we have six. Eric Topol (00:01:03): Oh six, I'm sorry, six. There's Science, the original, and then five others. Can you tell us what it's like to oversee all these journals? Overseeing the Science Journals Holden Thorp (00:01:16): Yeah, we're a relatively small family compared to our commercial competitors. I know you had Magdalena [Skipper]on and Nature has I think almost ninety journals, so six is pretty small. In addition to Science, which most people are familiar with, we have Science Advances, which also covers all areas of science and is larger and is a gold open access journal and also is overseen by academic editors, not professional editors. All of our other journals are overseen by professional editors. And then the other four are relatively small and specialized areas, and probably people who listen to you and follow you would know about Science Translational Medicine, Science Immunology, Science Signaling and then we also have a journal, Science Robotics which is something I knew nothing about and I learned a lot. I've learned a lot about robotics and the culture of people who work there interacting with them. Holden Thorp (00:02:22): So we have a relatively small family. There's only 160 people who work for me, which is manageable. I mean that sounds like a lot, but in my previous jobs I was a provost and a chancellor, and I had tens of thousands of people, so it's really fun for me to have a group where I at least have met everybody who works for me. We're an outstanding set of journals, so we attract an outstanding group of professionals who do all the things that are involved in all this, and it's really, really fun to work with them. At Science, we don't just do research papers, although that's a big, and probably for your listeners the biggest part of what we do. But we also have a news and commentary section and the news section is 30 full-time and many freelancers around the world really running the biggest general news operation for science that there is. And then in the commentary section, which you're a regular contributor for us in expert voices, we attempt to be the best place in the world for scientists to talk to each other. All three of those missions are just really, really fun for me. It's the best job I've ever had, and it's one I hope to do for many years into the future. Eric Topol (00:03:55): Well, it's extraordinary because in the four and a half years I think it's been since you took the helm, you've changed the face of Science in many ways. Of course, I think the other distinction from the Nature Journals is that it's a nonprofit entity, which shows it isn't like you're trying to proliferate to all sorts of added journals, but in addition, what you've done, at least the science advisor and the science news and all these things that come out on a daily basis is quite extraordinary as we saw throughout the pandemic. I mean, just reporting that was unparalleled from, as you say, all points around the world about really critically relevant topics. Obviously it extends well beyond the concerns of the pandemic. It has a lot of different functions, but what I think you have done two major things, Holden. One is you medicalized it to some extent. Eric Topol (00:04:55): A lot of people saw the journal, particularly Science per se, as a truly basic science journal. Not so much applied in a medical sphere, but these days there's more and more that would be particularly relevant to the practice of medicine, so that's one thing. And the other thing I wanted you to comment on is you're not afraid to speak out and as opposed to many other prior editors who I followed throughout my career at Science, there were pretty much the politically correct type and they weren't going to really express themselves, which you are particularly not afraid of. Maybe you could comment about if you do perceive this medicalization of science to some extent, and also your sense of being able to express yourself freely. Capturing the Breakthroughs in Structural Biology Holden Thorp (00:05:48): Yeah, well, you're kind to say both of those things are certainly things we have worked at. I mean, I do come from a background, even though I'm trained as a chemist, most of what I did towards the latter end of my career, I mean, I did very basic biochemistry when I was a researcher, but the last part of my research career I worked in on development of a drug called Vivjoa, which is an alternative to the fluconazole family that doesn't have the same toxicity and is currently on the market for chronic yeast infection and hopefully some other things in the future when we can get some more clinical trials done. Holden Thorp (00:06:35): And I've hung around biotech startups and drug development, so it is part of the business that I knew. I think the pandemic really gave us an opening because Valda Vinson, who's now the Executive Editor and runs all of life sciences for us and policies for the journal, she was so well known in structural biology that most of the first important structures in Covid, including the spike protein, all came to us. I mean, I remember crystal clear February of 2020, she came in my office and she said, I got the structure of the spike protein. And I said, great, what's the spike protein? Turned out later became the most famous protein in the world, at least temporarily. Insulin may be back to being the most famous protein now, but spike protein was up there. And then that kind of cascaded into all the main protease and many of the structures that we got. Holden Thorp (00:07:45): And we seized on that for sure, to kind of broaden our focus. We had the Regeneron antibodies, we had the Paxlovid paper, and all of that kind of opened doors for us. And we've also, now we have two clinical editors at Science, Priscilla Kelly and Yevgeniya Nusinovich, and then the Insights section, somebody that you work with closely, Gemma Alderton, she is very fluent in clinical matters. And then of course we've had Science Translational Medicine and we seek continue to strengthen that. Science Immunology was very much boosted by Covid and actually Science Immunology is now, I think probably if you care about impact factors, the second highest specialized immunology journal after Immunity. I've put some emphasis on it for sure, but I think the pandemic also really helped us. As far as me speaking out, a lot of people maybe don't remember, but Don Kennedy, who was the editor in the early 2000s who had been the Stanford president, he was similarly outspoken. Confronting Controversies Holden Thorp (00:09:15): It's funny, sometimes people who disagree with me say, well, Don Kennedy would never say anything like that. And then I can dig up something that Don Kennedy said that's just as aggressive as what I might've said. But you're right, Bruce Alberts was very focused on education, and each one of us has had our own different way of doing things. When Alan Leshner hired me and Sudip Parikh reinforced this when he came on, I mean, he wanted me to liven up the editorial page. He explicitly told me to do that. I may have done more of it than he was expecting, but Alan and Sudip both still remain very supportive of that. I couldn't do what I do without them and also couldn't do it without Lisa Chong, who makes all my words sound so much better than they are when I start. And yeah, it kind of fed on itself. Holden Thorp (00:10:21): It started with the pandemic. I think there was an inflection when Trump first said that Covid was just the flu, and when he said some really ridiculous things about the vaccine, and that's where it started. I guess my philosophy was I was thinking about people who, they've got a spouse at home whose job might be disrupted. They got children they've got who are out of school, and somehow they managed to get themselves to the lab to work on our vaccine or some other aspect of the pandemic to try to help the world. What would those people want their journal to say when they came home and turned the news on and saw all these politicians saying all this ridiculous stuff? That was really the sort of mantra that I had in my head, and that kind of drove it. And now I think we've sort of established the fact that it's okay to comment on things that are going on in the world. We're editorially independent, Sudip and the AAAS board, treat us as being editorially independent. I don't take that for granted and it's a privilege to, as I sometimes tell people, my apartment's four blocks from the White House, sometimes I'm over there typing things that they don't like. And that tradition is still alive in this country, at least for the time being, and I try to make the most of it. Eric Topol (00:12:11): Well, and especially as you already touched o

Transcript Eric Topol (00:06): Well, hello, this is Eric Topol with Ground Truths and I am absolutely thrilled to welcome Daphne Koller, the founder and CEO of insitro, and a person who I've been wanting to meet for some time. Finally, we converged so welcome, Daphne. Daphne Koller (00:21): Thank you Eric. And it's a pleasure to finally meet you as well. Eric Topol (00:24): Yeah, I mean you have been rocking everybody over the years with elected to the National Academy of Engineering and Science and right at the interface of life science and computer science and in my view, there's hardly anyone I can imagine who's doing so much at that interface. I wanted to first start with your meeting in Davos last month because I kind of figured we start broad AI rather than starting to get into what you're doing these days. And you had a really interesting panel [←transcript] with Yann LeCun, Andrew Ng and Kai-Fu Lee and others, and I wanted to get your impression about that and also kind of the general sense. I mean AI is just moving it at speed, that is just crazy stuff. What were your thoughts about that panel just last month, where are we? Video link for the WEF Panel Daphne Koller (01:25): I think we've been living on an exponential curve for multiple decades and the thing about exponential curves is they are very misleading things. In the early stages people basically take the line between whatever we were last year, and this year and they interpolate linearly, and they say, God, things are moving so slowly. Then as the exponential curve starts to pick up, it becomes more and more evident that things are moving faster, but it’s still people interpolate linearly and it's only when things really hit that inflection point that people realize that even with the linear interpolation where we'll be next year is just mind blowing. And if you realize that you're on that exponential curve where we will be next year is just totally unanticipatable. I think what we started to discuss in that panel was, are we in fact on an exponential curve? What are the rate limiting factors that may or may not enable that curve to continue specifically availability of data and what it would take to make that curve available in areas outside of the speech, whatever natural language, large language models that exist today and go far beyond that, which is what you would need to have these be applicable to areas such as biology and medicine. Daphne Koller (02:47): And so that was kind of the message to my mind from the panel. Eric Topol (02:53): And there was some differences in opinion, of course Yann can be a little strong and I think it was good to see that you're challenging on some things and how there is this “world view” of AI and how, I guess where we go from here. As you mentioned in the area of life science, there already had been before large language models hit stride, so much progress particularly in imaging cells, subcellular, I mean rare cells, I mean just stuff that was just without any labeling, without fluorescein, just amazing stuff. And then now it's gone into another level. So as we get into that, just before I do that, I want to ask you about this convergence story. Jensen Huang, I'm sure you heard his quote about biology as the opportunity to be engineering, not science. I'm sure if I understand, not science, but what about this convergence? Because it is quite extraordinary to see two fields coming together moving at such high velocity. "Biology has the opportunity to be engineering not science. When something becomes engineering not science it becomes...exponentially improving, it can compound on the benefits of previous years." -Jensen Huang, NVIDIA. Daphne Koller (04:08): So, a quote that I will replace Jensen's or will propose a replacement for Jensen's quote, which is one that many people have articulated, is that math is to physics as machine learning is to biology. It is a mathematical foundation that allows you to take something that up until that point had been kind of mysterious and fuzzy and almost magical and create a formal foundation for it. Now physics, especially Newtonian physics, is simple enough that math is the right foundation to capture what goes on in a lot of physics. Biology as an evolved natural system is so complex that you can't articulate a mathematical model for that de novo. You need to actually let the data speak and then let machine learning find the patterns in those data and really help us create a predictability, if you will, for biological systems that you can start to ask what if questions, what would happen if we perturb the system in this way? The Convergence Daphne Koller (05:17): How would it react? We're nowhere close to being able to answer those questions reliably today, but as you feed a machine learning system more and more data, hopefully it'll become capable of making those predictions. And in order to do that, and this is where it comes to this convergence of these two disciplines, the fodder, the foundation for all of machine learning is having enough data to feed the beast. The miracle of the convergence that we're seeing is that over the last 10, 15 years, maybe 20 years in biology, we've been on a similar, albeit somewhat slower exponential curve of data generation in biology where we are turning it into a quantitative discipline from something that is entirely observational qualitative, which is where it started, to something that becomes much more quantitative and broad based in how we measure biology. And so those measurements, the tools that life scientists and bioengineers have developed that allow us to measure biological systems is what produces that fodder, that energy that you can then feed into the machine learning models so that they can start making predictions. Eric Topol (06:32): Yeah, well I think the number of layers of data no less what's in these layers is quite extraordinary. So some years ago when all the single cell sequencing was started, I said, well, that's kind of academic interest and now the field of spatial omics has exploded. And I wonder how you see the feeding the beast here. It's at every level. It's not just the cell level subcellular and single cell nuclei sequencing single cell epigenomics, and then you go all the way to these other layers of data. I know you plug into the human patient side as well as it could be images, it could be past slides, it could be the outcomes and treatments and on and on and on. I mean, so when you think about multimodal AI, has anybody really done that yet? Daphne Koller (07:30): I think that there are certainly beginnings of multimodal AI and we have started to see some of the benefits of the convergence of say, imaging and omics. And I will give an example from some of the work that we've recently distributed on a preprint server work that we did at insitro, which took imaging data from standard histopathology slides, H&E slides and aligned them with simple bulk RNA-Seq taken from those same tumor samples. And what we find is that by training models that translate from one to the other, specifically from the imaging to the omics, you're able to, for a fairly large fraction of genes, make very accurate predictions of gene expression levels by looking at the histopath images alone. And in fact, because many of the predictions are made at the tile level, not at the entire slide level, even though the omics was captured in bulk, you're able to spatially resolve the signal and get kind of like a pseudo spatial biology just by making predictions from the H&E image into these omic modalities. Multimodal A.I. and Life Science Daphne Koller (08:44): So there are I think beginnings of multimodality, but in order to get to multimodality, you really need to train on at least some data where the two modalities are simultaneously. And so at this point, I think the rate limiting factor is more a matter of data acquisition for training the models. It is for building the models themselves. And so that's where I think things like spatial biology, which I think like you are very excited about, are one of the places where we can really start to capture these paired modalities and get to some of those multimodal capabilities. Eric Topol (09:23): Yeah, I wanted to ask you because I mean spatial temporal is so perfect. It is two modes, and you have as the preprint you refer to and you see things like electronic health records in genomics, electronic health records in medical images. The most we've done is getting two modes of data together. And the question is as this data starts to really accrue, do we need new models to work with it or do you actually foresee that that is not a limiting step? Daphne Koller (09:57): So I think currently data availability is the most significant rate limiting step. The nice thing about modern day machine learning is that it really is structured as a set of building blocks that you can start to put together in different ways for different situations. And so, do we have the exact right models available to us today for these multimodal systems? Probably not, but do we have the right building blocks that if we creatively put them together from what has already been deployed in other settings? Probably, yes. So of course there's still a model exploration to be done and a lot of creativity in how these building blocks should be put together, but I think we have the tools available to solve these problems. What we really need is first I think a really significant data acquisition effort. And the other thing that we need, which is also something that has been a priority for us at insitro, is the right mix of people to be put together so that you can, because what happens is if you take a bunch of even extremely talented and sophisticated machine learning scientists and say, solve a biological problem, here's a dataset, they don't know what questions to ask and oftentimes end up asking q

“A few years ago, I might have chuckled at the naiveté of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.”—Coleen Murphy Transcript with external links Eric Topol (00:06): Hello, this is Eric Topol from Ground Truths, and I'm just so delighted to have with me Professor Coleen Murphy, who has written this exceptional book, How We Age: The Science of Longevity. It is a phenomenal book and I'm very eager to discuss it with you, Coleen. Coleen Murphy (00:25): Thanks for having me on. Eric Topol (00:27): Oh yeah. Well, just so everyone who doesn't know Professor Murphy, she's at Princeton. She's the Richard Fisher Preceptor in Integrative Genomics, the Lewis-Sigler Institute for Integrative Genomics at Princeton, and director of the Paul Glenn Laboratories for Aging Research. Well, obviously you've been in this field for decades now, even though you're still very young. The classic paper that I can go back to would be in Nature 2003 with the DAF-16 and doubling the lifespan of C. elegans or better known as a roundworm. Would that be the first major entry you had? Coleen Murphy (01:17): Yeah, that was my postdoctoral work with Cynthia Kenyon. Eric Topol (01:20): Right, and you haven't stopped since you've been on a tear and you’ve put together a book which has a hundred pages of references in a small font. I don't know what the total number is, but it must be a thousand or something. Coleen Murphy (01:35): Actually, it's just under a thousand. That's right. Eric Topol (01:37): That's a good guess. Coleen Murphy (01:38): Good guess. Yeah. Eric Topol (01:39): So, because I too have a great interest in this area, I found just the resource that you've put together as extraordinary in terms of the science and all the work you've put together. What I was hoping to do today is to kind of take us through some of the real exciting pathways because there's a sentence in your book, which I thought was really kind of nailed it, and it actually is aligned with my sense. Obviously don't have the expertise by any means that you do here but it says, “A few years ago, I might have chuckled at the naivety of this question, but now it's not so crazy to think that we will be able to take some sort of medicine to extend our healthy lifespans in the foreseeable future.” That's a pretty strong statement for a person who's deep into the science. First I thought we'd explore healthy aging health span versus lifespan. Can you differentiate that as to your expectations? Coleen Murphy (02:54): So, I think most people would agree that they don't want to live necessary super long. What they really want to do is live a healthy life as long as they can. I think that a lot of people also have this fear that when we talk about extending lifespan, that we're ignoring that part. And I do want to assure everyone that the people in the researchers in the aging field are very much aware of this issue and have, especially in the past decade, I think put a real emphasis on this idea of quality of life and health span. What's reassuring is actually that many of the mechanisms that extend lifespan in all these model organisms also extend health span as well and so I don't think we're going to, they're not diametrically opposed, like we'll get to a healthier quality of life, I think in these efforts to extend lifespan as well. Eric Topol (03:50): Yeah, I think that's important that you're bringing that up, which is there's this overlap, like a Venn diagram where things that do help with longevity should help with health span, and we don't necessarily have to follow as you call them the immoralists, as far as living to 190 or whatever year. Now, one of the pathways that's been of course a big one for years and studied in multiple species has been caloric restriction. I wonder if you could talk to that and obviously there's now mimetics that could simulate that so you wouldn't have to go through some major dietary starvation, if you will. What are your thoughts on that pathway? Coleen Murphy (04:41): Yeah, actually I'm really glad you brought up mimetics because often the conversation starts and ends with you should eat less. I think that is a really hard thing for a lot of people to do. So just for the background, so dietary restriction or caloric restriction, the idea is that you would have to take in up to 30% less than your normal intake in order to start seeing results. When we've done this with laboratory animals of all kinds, this works from yeast all the way up through mice, actually primates, in fact, it does extend lifespan and in most metrics of health span the quality of life, it does improve that as well. On the other hand, I think psychologically it's really tough to not eat enough and I think that's a part that we kind of blindly ignore when we talk about this pathway. Coleen Murphy (05:30): And of course, if we gave any of those animals the choice of whether they want to start eating more, they would. So, it's like that's not the experiment we ever hear about. And so, the idea for studying this pathway isn't just to say, okay, this works and now we know how it works, but as you pointed out, mimetics, so can we target the molecules in the pathway so that we can help people achieve the benefits of caloric restriction without necessarily having to do the kind of awful part of restriction? I think that's really cool, and especially it might be very good for people who are undergoing certain, have certain diseases or have certain impairments that it might make it difficult ever to do dietary restrictions, so I think that's a really great thing that the field is kind of getting towards now. Eric Topol (06:15): And I think in fact, just today, it's every day there's something published now. Just today there was a University of Southern California study, a randomized study report comparing plant-based fasting-mimicking diet versus controlled diet, and showed that many metabolic features were improved quite substantially and projected that if you stayed on that diet, you'd gain two and a half years of healthy aging or that you would have, that's a bit of an extrapolation, but quite a bit of benefit. Now, what candidates would simulate caloric restriction? I mean, what kind of molecules would help us do that? And by the way, in the book you mentioned that the price to pay is that the brain slows down with caloric restrictions. Coleen Murphy (07:10): There's at least one study that shows that. Coleen Murphy (07:13): Yeah, so it's good to keep in mind. One of the big things that is being looked at as rapamycin, looking at that TOR pathway. So that's being explored as one of these really good mimetics. And of course, you have things that are analogs of that, so rapalogs, and so people are trying to develop drugs that mimic that, do the same kind of thing without probably some of the side effects that you might see with rapamycin. Metformin is another one, although it's interesting when you talk to people about metformin who work on it, it's argued about what is exactly the target of metformin. There's thought maybe also acts in the TOR pathway could affect complex one of mitochondria. Some of the things we know that they work, and we don't necessarily know how they work. And then of course there's new drugs all the time where people are trying to develop to other target, other molecules. So, we'll see, but I think that the idea of mimetics is actually really good, and that part of the field is moving forward pretty quickly. This diet that you did just mention, it is really encouraging that they don't have to take a drug if you don't want to. If you eat the right kind of diet, it could be very beneficial. Eric Topol (08:20): Yeah, no, it was interesting. I was looking at the methods in that USC paper and they sent them a box of stuff that they would eat for three cycles, multiple weeks per cycle. It was a very interesting report, we'll link to that. Before we leave the caloric restriction and these mTOR pathway, you noted in the book that there some ongoing trials like PEARL, I looked that up and they finished the trial, but they haven't reported it and it's not that large. And then there's the FAME trial with metformin. I guess we'll get a readout on these trials in the not-too-distant future. Right? Coleen Murphy (08:57): Yeah, that's the hope that especially with the Metformin trial, which I think is going to be really large the FAME trial, that just to give the listeners a little background, one of the efforts in the field is not just to show that something works, but also to convince the FDA that aging could be a pharmaceutical, a disease that we might want to have interventions for. And to do that, we need to figure out the right way to do it. We can't do 30-year studies of safety and things to make sure that something's good, but maybe there are reasonable biomarkers that would tell us whether people are going to live a long time. And so, if we can use some of those things or targeting age-related diseases where we can get a faster readout as well. Those are reasonable things that companies could do that would help us to really confirm or maybe rule out some of these pharmaceuticals as effective interventions. I think that would be really great for consumers to know, is this thing really going to do good or not? And we just don't have that right now in the field. We have a lot of people saying something will work and it might and the studies in the lab, but when we get to humans, we really need more clinical studies to really tell us that things are going to be effective. Eric Topol (10:12): Right, I'm going to get to that in a bit too because I think you're bringing up a critical topic since there's an explosion of biopharma companies in this space, billions of dollars that have been put up for in capital and the question

Transcript with audio and relevant external links, recorded on 6 Feb 2024 Eric Topol (00:05): Hello, this is Eric Topol with Ground Truths, and I have a remarkable guest with me today, Professor Michelle Monje, who is from Stanford, a physician-scientist there and is really a leader in neuro-oncology, the big field of cancer neuroscience, neuroinflammation, and she has just been rocking it recently with major papers on these fields, no less her work that's been on a particular cancer, brain cancer in kids that we'll talk about. I just want to give you a bit of background about Michelle. She is a National Academy of Medicine member, no less actually a National Academy of Medicine awardee with the French Academy for the Richard Lounsbery Award, which is incredibly prestigious. She received a Genius grant from the MacArthur Foundation and is a Howard Hughes Medical Institute (HHMI) scholar, so she is just an amazing person who I'm meeting for the first time. Michelle, welcome. Michelle Monje (01:16): Thank you. So nice to join you. Long Covid and the Brain Eric Topol (01:18): Well, I just am blown away by the work that you and your colleagues have been doing and it transcends many different areas that are of utmost importance. Maybe we can start with Long Covid because that's obviously such a big area. Not only have you done work on that, but you published an amazing review with Akiko Iwasaki, a friend of mine, that really went through all the features of Long Covid. Can you summarize your thoughts about that? Michelle Monje (01:49): Yeah, and specifically we focused on the neurobiology of Long Covid focusing on the really common syndrome of cognitive impairment so-called brain fog after Covid even after relatively mild Covid. There has been this, I think really important and exciting, really explosion of work in the last few years internationally trying to understand this in ways that I am hopeful will be beneficial to many other diseases of cognition that occur in the context of other kinds of infections and other kinds of immune challenges. But what is emerging from our work and from others is that inflammation, even if it doesn't directly initially involve the nervous system, can very profoundly affect the nervous system and the mechanisms by which that can happen are diverse. One common mechanism appears to be immune challenge induced reactivity of an innate immune cell in the nervous system called microglia. These microglia, they populate the nervous system very early in embryonic development. (02:58): And their job is to protect the nervous system from infection, but also to respond to other kinds of toxic and infectious and immune challenges. They also play in healthy conditions, really important roles in neurodevelopment and in neuroplasticity and so they're multifaceted cells and this is some population of those cells, particularly in the white matter in the axon tracks that are exquisitely sensitive it seems to various kinds of immune challenges. So even if there's not a direct nervous system insult, they can react and when they react, they stop doing their normal helpful jobs and can dysregulate really important interactions between other kinds of cells in the brain like neurons and support cells for those neurons like oligodendrocytes and astrocytes. One common emerging principle is that microglial reactivity triggered by even relatively mild Covid occurring in the respiratory system, not directly infecting the brain or other kinds of immune challenges can trigger this reactivity of microglia and consequently dysregulate the normal interactions between cells and the brain. (04:13): So important for well-tuned and optimal nervous system function. The end product of that is dysfunction and cognition and kind of a brain fog impairment, attention, memory, ability to multitask, impaired speed of information processing, but there are other ways that Covid can influence the nervous system. Of course there can be direct infection. We don't think that that happens in every case. It may not happen even commonly, but it certainly can happen. There is a clear dysregulation of the vasculature, the immune response, and the reaction to the spike protein of Covid in particular can have very important effects on the vessels in the nervous system and that can trigger a cascade of effects that can cause nervous system dysregulation and may feed directly into that reactivity of the microglia. There also can be reactivation of other infections previous, for example, herpes virus infections. EBV for example, can be reactivated and trigger a new immune challenge in the context of the immune dysregulation that Covid can induce. (05:21): There also can be autoimmunity. There are many, we're learning all the different ways Covid can affect the nervous system, but autoimmunity, there can be mimicry of some of the antigens that Covid presents and unfortunate autoimmunity against nervous system targets. Then finally in severe Covid where there is cardiopulmonary compromise, where there is hypoxia and multi-organ damage, there can be multifaceted effects on the nervous system in severe disease. So many different ways, and probably that is not a comprehensive list. It is certainly not a mutually exclusive list. Many of these interactions can happen at the same time in the same individual and in different combinations but we're beginning to wrap our arms around all the different ways that Covid can influence the nervous system and cause this fairly consistent syndrome of impaired attention, memory, multitasking, and executive functions. Homology with Chemo Brain Eric Topol (06:23): Yeah, well there's a lot there that you just summarized and particularly you highlighted the type of glia, the microglia that appear to be potentially central at least a part of the story. You also made analogy to what you've seen with chemotherapy, chemo brain. Maybe you could elaborate on that. Michelle Monje (06:42): Yeah, absolutely. So I've been studying the cognitive impairment that can happen after cancer therapies including chemotherapy, but also radiation and immunotherapy. Each time we develop a new model and dig in to understand what's going on and how these cancer therapies influence the nervous system, microglia emerge as sort of the unifying principle, microglial reactivity, and the consequences of that reactivity on other cell types within the nervous system. And so, understanding that microglia and their reactive state to toxic or immune challenges was central to chemotherapy induced cognitive impairment, at least in preclinical models in the laboratory and confirm by human tissue studies. I worried at the very beginning of the pandemic that we might begin to see something that looks a lot like chemotherapy induced cognitive impairment, this syndrome that is characterized by impaired attention, memory, executive function, speed of information processing and multitasking. When just a few months into the pandemic, people began to flood neurologists’ office complaining of exactly this syndrome. I felt that we needed to study it and so that was the beginning of what has become a really wonderful collaboration with Akiko Iwasaki. I reached out to her, kind of cold called her in the midst of the deep Covid shutdown and in 2020 and said, hey, I have this idea, would you like to work with me? She's as you know, just a thought leader in Covid biology and she's been an incredibly wonderful and valuable collaborator along the way in this. Eric Topol (08:19): Well, the two of you pairing up is kind of, wow, that's a powerful combination, no question. Now, I guess the other thing I wanted to get at is there've been many other studies that have been looking at Long Covid, how it affects the brain. The one that's frequently cited of course is the UK Biobank where they had CT or MRI scans before in people fortunately, and then once they had Covid or didn't get Covid and it had a lot of worrisome findings including atrophy and then there are others that in terms of this niche of where immune cells can be in the meninges, in the bone marrow or the skull of the brain. Could you comment on both those issues because they've been kind of coming back to haunt us in terms of the more serious potential effects of Covid on the brain? Michelle Monje (09:20): Yeah, absolutely and I will say that I think all of the studies are actually quite parsimonious. They all really kind of point towards the same biology, examining it at different levels. And so that UK Biobank study was so powerful because in what other context would someone have MRI scans across the population and cognitive testing prior to the Covid pandemic and then have paired same individual tests after a range of severity of Covid infection so it was just an incredibly important data set with control individuals in the same cohort of people. This longitudinal study has continued to inform us in such important ways and that study found that there were multiple findings. One is that there appears to be a small but significant atrophy in the neocortex. Two that there are also abnormalities in major white matter tracts, and three, that there is particular pathology within the olfactory system. (10:30): And we know that Covid induces as a very common early symptom, this loss of smell. Then together with those structural findings on MRI scans that individuals even with relatively mild acute disease, exhibited long-term deficits in cognitive function. That fits with some beautiful epidemiological studies that have been done across many thousands of individuals in multiple different geographic populations. Underscoring this consistent finding that Covid can induce lasting cognitive changes and as we begin to understand that biology, it fits with those structural changes that are observed. We do know that the olfactory system is particularly affected and so it makes sense that the olfactory system, which s

Jim Collins is one of the leading biomedical engineers in the world. He’s been elected to all 3 National Academies (Engineering, Science, and Medicine) and is one of the founders of the field of synthetic biology. In this conversation, we reviewed the seminal discoveries that he and his colleagues are making at the Antibiotics-AI Project at MIT. Recorded 5 February 2024, transcript below with audio links and external links to recent publications Eric Topol (00:05): Hello, it's Eric Topol with Ground Truths, and I have got an extraordinary guest with me today, Jim Collins, who's the Termeer Professor of Medical Engineering at MIT. He also holds appointments at the Wyss Institute and the Broad Institute. He is a biomedical engineer who's been making exceptional contributions and has been on a tear lately, especially in the work of discovery of very promising, exciting developments in antibiotics. So welcome, Jim. Jim Collins (00:42): Eric, thanks for having me on the podcast. Eric Topol (00:44): Well, this was a shock when I saw your paper in Nature in December about a new structure class of antibiotics, the one from 1962 to 2000. It took 38 years, and then there was another one that took 24 years yours, the structural antibiotics. Before I get to that though, I want to go back just a few years to the work you did published in Cell with halicin, and can you tell us about this? Because when I started to realize what you've been doing, what you've been chipping away here, this was a drug you found, halicin, as I can try to understand, it works against tuberculosis, c. difficile, enterobacter that are resistant, acinetobacter that are resistant. I mean, this is, and this is of course in mice models. Can you tell us how did you make that discovery before we get into I guess what's called the Audacious Project? Jim Collins (01:48): Yeah, sure. It's actually a fun story, so it is origins go broadly to institute wide event at MIT, so MIT in 2018 launched a major campus-wide effort focused on artificial intelligence. The institute, which had played a major role in the first wave of AI in the 1950s, 1960s, and a major wave in the second wave in the 1980s found itself kind of at the wheel in this third wave involving big data and deep learning and looked to correct that and to correct it the institute had a symposium and I had the opportunity to sit next to Regina Barzilay, one of our faculty here at MIT who specializes in AI and particularly AI applied to biomedicine and we really hit it off and realized we had interest in applying AI to drug discovery. My lab had focused on antibiotics to then close to 15 years, but primarily we're using machine learning and network biology to understand the mechanism of action of antibiotics and how resistance arise with the goal of boosting what we already had, with Regina we saw there was an opportunity to see if we could use deep learning to get after discovery. (02:55): And notably, as you kind of alluded in your introduction, there's really been a discovery void and the golden age of discovery antibiotics was in the forties, fifties and sixties before I was born and before you had the genomic revolution, the biotech revolution, AI revolution. Anyways, we got together with our two groups, and it was an unfunded project and we kind of cobbled together very small training set of 2,500 compounds that included 1,700 FDA approved drugs and 800 natural compounds. In 2018, 2019, when you started this, if you asked any AI expert should you initiate that study, they would say absolutely not, there's going to be two big data. The idea of these models are very data hungry. You need a million pictures of a dog, a million pictures of a cat to train a model to differentiate between the cat and the dog, but we ignored the naysayers and said, okay, let's see what we can do. (03:41): And we apply these to E. coli, so a model pathogen that's used in labs but is also underlies urinary tract infections. So it’s a look to see which of the molecules inhibited growth of the bacteria as evidence for antibacterial activity and we could have measured and we quantified each of their effects, but because we had so few compounds, we just discretized instead, if you inhibited at least 80% of the growth you were antibacterial, and if you didn't achieve that, you weren't antibacterial zero in ones. We then took the structure of each molecule and trained a deep learning model, specifically a graphical neural net that could look at those structures, bond by bond, substructure by substructure associated with whatever features you look to train with. In our case, making for good antibiotic, not for good antibiotic. We then took the train model and applied it to a drug repurposing hub as part of the Broad Institute that consists of 6,100 molecules in various stages of development as a new drug. (04:40): And we asked the model to identify molecules that can make for a good antibiotic but didn't look like existing antibiotics. So part of the discovery void has been linked to this rediscovery issue we have where we just keep discovering quinolones like Cipro or beta-lactams like penicillin. Well, anyways, from those criteria as well as a small tox model, only one molecule came out of that, and that was this molecule we called halicin, which was named after HAL, the killing AI computer system from 2001 Space Odyssey. In this case, we don't want it to kill humans, we want it to kill bacteria and as you alluded, it turned out to be a remarkably potent novel antibiotic that killed off multi-drug resistant extensively drugs, a pan-resistant bacteria went after to infections. It was affected against TB, it was affected against C. diff and acinetobacter baumannii and acted to a completely new mechanism of action. (05:33): And so we were very excited to see how AI could open up possibilities and enable one to explore chemical spaces in new and different ways. We took them model, then applied it to a very large chemical library of 1.5 billion molecules, looked at a subset of about 110 million that would be impossible for any grad student, any lab really to look at that experimentally but we looked at it in a model computer system and in three days could screen those 110 million molecules and identified several new additional candidates, one which we call salicin, which is the cousin of halicin that similes broad spectrum and acts to a novel mechanism of action. Eric Topol (06:07): So before we go further with this initial burst of discovery, for those who are not used to deep neural networks, I think most now are used to the convolutional neural network for images, but what you use specifically here as you alluded to, were graph neural networks that you could actually study the binding properties. Can you just elaborate a little bit more about these GNN so that people know this is one of the tools that you used? Jim Collins (06:40): Yeah, so in this case, the underlying structure of the model can actually represent and capture a graphical structure of a molecule or it might be of a network so that the underlying structure itself of the model will also look at things like a carbon atom connects to an oxygen atom. The oxygen atom connects to a nitrogen atom and so when you think back to the chemical structures we learned in high school, maybe we learned in college, if we took chemistry class in college, it was actually a model that can capture the chemical structure representation and begin to look at sub aspects of it, associating different properties of it. In this case, again, ours was antibacterial, but it could be toxic, whether it's toxic against a human cell and the model, the train model, the graph neural model can now look at new structures that you input them and then make calculations on those bonds so a bond would be a connection between two atoms or substructures, be multiple bonds, interconnecting multiple atoms and assign it a score. Does it make, for example, in our case, for a good antibiotic. Eric Topol (07:48): Right. Now, what's also striking as you set up this collaboration that's interdisciplinary with Regina, who I know of her work through breast cancer AI and not through drug discovery and so this was, I think that new effort and this discovery led to this, I love the name of it, Audacious Project, right? Jim Collins (08:13): Right. Yeah, so a few points on the collaboration then I'll speak to Audacious Project. In addition to Regina, we also brought in Tommi Jaakkola, another AI faculty member and marvelous colleague here at MIT and really we've benefited from having outstanding young folks who were multilingual. We had very rich, deep trained grad students from ML on Regina and Tommi's side who appreciated the biology and we had very richly, deeply trained postdocs, Jon Stokes in particular from the microbiology side on my side, who could appreciate the machine learning and so they could speak across the divide. And so, as I look out in the next few decades in this exciting time of AI coming into biomedicine, I think the groups will make a difference of those that have these multilingual young trainees and two who are well set up to also inject human intelligence with machine intelligence. (09:04): Brings the Audacious Project. Now, prior to our publication of halicin, I was invited by the Audacious Project to submit a proposal, the Audacious Project is a new philanthropic effort run by TED, so the group that does the TED Talks that's run by Chris Anderson, so Chris had the idea that there was a need to bring together philanthropists around the world to go for a larger scale in a collective manner toward audacious projects. I pitched them on the idea that we could use AI to address the antibiotic resistance crisis. As you can appreciate, and many of your listeners can appreciate that we're doomed if we don't actually address this soon, in that the number of resistance strains that are in our commun

“The history of science, it turns out, is filled with stories of very smart people laughing at good ideas.”—Katalin Karikó Ground Truths podcasts are now available on Apple and Spotify! The list of obstacles that Kati Karikó faced to become a scientist, to make any meaningful discovery, to prevail over certain scientists and administrators who oppressed her, unable to obtain grants, her seminal paper rejected by all of the top-tier journals, demoted and dismissed, but ultimately to be awarded the 2023 Nobel Prize with Drew Weissman, is a story for the ages. We covered them in this conversation, which for me will be unforgettable, and hopefully for you an inspiration. Recorded 30 January 2023, unedited transcript below Eric Topol (00:06): Well, hello, this is Eric Topol with Ground Truths, and I am really thrilled to have with me Kati Kariko, who I think everyone knows won the Nobel Prize with the Drew Weissman in 2023 and she has written a sensational book, it's called Breaking Through. I love that title because it's a play on words, a breakthrough and breaking through, and we have a lot to talk about Kati, so welcome. Katalin Kariko (00:34): Thank you very much for inviting me. Eric Topol (00:36): Yes, well I'd like to start off, as you did in the book with your background in Hungary where of course you started with a tough background in a one room house without running water and you never had exposures to scientists and somehow or other you became interested in science and you attributed some of these things like your biology teacher, Mr. Tóth and the book Stress of Life [by Hans Selye] Could you tell us a little bit more what stimulated you in a career of science? Katalin Kariko (01:18): I have to say that every child is interested in understanding the nature around them and so I was surrounded with nature because we had big garden, we had animals around and it was an exciting thing. The children ask questions and if they try to find an answer and teachers or parents might give the answer, but definitely the school, even elementary school was very stimulating. Teachers, chemistry teacher, figure out how we can make crystals and I was so excited to have my own crystals and things like that and in high school the teachers were so engaging and not like they tried to put all of the information into your brain, but they encourage you to think yourself, so that's all contributed. I think that most of the child in the first, I don't know, six, seven years of their life that’s how they can see their parents behaving, their friends, the school, classmates, and they shaped what kind of people they will be at the end and the rest of it is refining. Eric Topol (02:41): Right, right. Well one of the things I loved that you brought up in the book was how much you liked the TV show Columbo. That's one of my favorite TV shows of all time and one more thing, one more thing. Can you talk a little bit about Columbo? Because in some ways you were like the Peter Falk of mRNA in terms of one more thing. Katalin Kariko (03:09): Yes, so I realized that we as researchers, we are not called searchers, we researchers, so we are repeating things. Of course everybody knows who committed the crime in Columbo because this is how it starts and you don't have to figure out, but it seems always that things in a different direction you would lead but all the little clues and some of my colleagues said that they as a physician, they have this tunnel vision. So the patient comes and they can figure out probably from some clues that this is the disease and they get back the lab results and others. Then they realize that one or two things is not fitting, but they always so strongly believe their first instinct. What I taught them to focus on those which will not fit because that will lead to the real perpetrator in case of Columbo. (04:23): And so I like the simplicity. I know that what we are doing this research is very over complicated, but we can break down in very simple question, yes or no and then repeating things and many experiments. When I did one was the experiments really the question and the nine of them was like just controls always. I have to have a control for that, control for that and since I work most of the time with my own hands myself, so I had to make sure that I think through that what will be the experimental outcome and then think about that. Do I have a control for that? So that many times in my brain before I performed the experiment in my brain, I predicted that what will be the outcome, of course you never get the outcome what you expect, but at least you have the control that you can exclude a couple of things and so this is how I function usually in the end of the 20th century, 21st century people did not work like I did alone most of the time. Eric Topol (05:35): No, I see how you described it in the book was just so extraordinary and it really was in keeping with this relentless interrogation and that's what I want to get into is particularly the time when you came to the United States in 1985 and the labs that you worked in predominantly in Philadelphia through that period before leaving Penn to go on to BioNTech. So, you first kind of beached in at Temple University with a monster at least as you portray him in the book. I mean it was nice that he picked you up at the airport, you and your family. How do you say his name? Suhadolnik. Eric Topol (06:31): But not only was the lab kind of infested with cockroaches, but also after working there for a number of years, a few years, you then had gotten an offer to go to Johns Hopkins and when you informed him about that he threatened and did everything he could to ruin your career and get you deported. I mean this was just awful. How did you get through that? Katalin Kariko (06:58): As I mentioned later on, I went back and gave a lecture there and I have to say that I always put positivity in forefront, so I learned a lot from him, and he invited me to America. I was always very grateful, and he was kind, and we did very well, and we did a lot of publication. In one issue of biochemistry, we had three papers and two of them I was the first author, so I worked very hard and so he liked that, and he wanted me to stay there. I just learned that from this Selye book that this is what is given and then what I can do, I cannot change him. I cannot change the situation, how I can get out from it and that's what I focused on, so I am not bitter about him. I liked him and the same for other people. When I get an award, I usually thanks to all of these people who try to make my life miserable. They made me work harder. Eric Topol (08:05): Well, but you were very kind like you said when you went back to Temple many years later to give the lecture because what he did to you, I mean he was so vindictive about you potentially leaving his lab, which he demanded that he be called the boss and he was going to basically, he ruined the Johns Hopkins job. He called them and you were so nice and kind when you went back to give the lecture without saying a negative word about him, so I give you credit, when somebody goes low, you went high, which is nice. Katalin Kariko (08:40): It is important, which I learned from the Selye book, that you don't carry any grudge against anybody because it'll poison you and as Selye also said that when you are very frustrated and very upset, the quickest way you can think about how you can release the stress is revenge. He said, don't do that. It escalate. It hit you back. You have to think about how you can be grateful for the same person you were just ready to take some revenge and that's what you have to practice. Sometimes it is difficult to feel that, but I don't have any bad feeling against my chairman who put my stuff on the hallway. Eric Topol (09:24): Oh yeah, I was going to get to that. So then after a short stint at the Uniformed University of Health Science where you had to drive three hours from Philadelphia to go there and you would sleep on the floor. I mean, I have to say Kati, if I was driving three hours, all I'd be thinking about is how desperate situation I was put in by the prior PI you work with. Any rate, you work there and then finally you got a job with my friend Elliot Barnathan, a cardiologist at University of Pennsylvania. So here you are, you're very interested in mRNA and you hook up with Elliot who's interested in plasminogen activators, and you work in his lab and it's quite a story where one of the students in his lab, David Langer, ratted on you for being blunt about the experiments getting screwed up and then later you wind up working in his lab. Tell me a bit about the times with Elliot because he's a very gracious, I think he was very supportive of your efforts and you got him stimulated about the potential for mRNA, it seems like. Katalin Kariko (10:41): Yes, so I was desperate to be away from my family at Bethesda and try to get back and every day I sent out several applications. This was in 1989, so you had to send letters and then I called up usually the secretaries about what's going on and I called up also a secretary and she said that they were advertised because nobody was good enough. I said, can you ask him to look at again my application? Then half an hour later, Elliot called me back that come and bring your notebook. He wanted to know what kind of experiment I am doing, and he opened when I came a couple of days later and pulled up a northern blot and he said, you have done that? I said, yes, I did. He said, okay, you are hired and so that, because Elliot is just a couple of days younger than me, I convinced him that we should do kind of mRNA research and he agreed, and we did several experiments and he helped me to get all of these experiments ongoing and so it was a very exciting time and I listened. Elliot was there in many awards ceremony including the Nobel Pri

Jonathan Howard is a neurologist and psychiatrist who practices at NYU-Bellevue and posts frequently on Science Based Medicine. Transcript, unedited, with links to audio Eric Topol (00:05): Well, hello, Eric Topol with Ground Truths and I'm really pleased to have the chance to talk with Jonathan Howard today, who is a neurologist and psychiatrist at NYU at Bellevue and has written quite an amazing book published a few months months ago called We Want Them Infected, so welcome Jonathan. Jonathan Howard (00:27): Hey, thanks so much for having me. I really appreciate it. Eric Topol (00:30): Yeah, I mean, there's so much to talk about because we're still in the throes of the pandemic with this current wave at least by wastewater levels and no reason to think it isn't by infections at least the second largest in the pandemic course. I guess I want to start off first with you being into the neuropsychiatric world. How did you become, obviously caring for patients with Covid, but how did you decide to become a Covidologist? Jonathan Howard (00:59): Well, I developed a strong interest in the anti-vaccine movement of all things about a decade ago when a doctor who I trained with here at NYU in Bellevue morphed into one of the country's biggest anti-vaccine doctors a woman by the name of Dr. Kelly Brogan. I knew her well and we were friends; She was smart and after she left NYU in Bellevue, she became one of the country's most outspoken anti-vaccine doctors and really started leaving off the wall things that germ theory didn't exist, that HIV doesn't cause AIDS. When Covid struck, she felt that SARS-CoV-2 was not killing people because she doesn't believe any virus kills people and so I became very fascinated about how smart people can believe strange, incorrect things and I dedicated myself to learning everything that I could about the anti-vaccine movement. In 2018, I wrote a book chapter on the anti-vaccine movement with law professor Dorit Reiss. (02:01): And so when the pandemic came around, I was really prepared for all of their arguments, but I got two very important things wrong. I thought the anti-vaccine movement would shrink. I was wrong about that and I was also really caught off guard by the fact that a lot of mainstream physicians started to parrot pandemic anti-vaccine talking points. So all of the stuff that I'd heard about measles and the HPV vaccine, these are benign viruses, the vaccines weren't tested, blah, blah, blah. I started hearing from professors at Stanford, Harvard, UCSF, Johns Hopkins, all about Covid and the Covid vaccine. Eric Topol (02:40): Yeah, we're going to get to some of the leading institutions and individuals within them and how they were part of this, and surprisingly too, of course. Before we do that in the title of your book, We Want Them Infected, it seems to bring in particularly the Great Barrington Declaration that is just protect the vulnerable elderly and don't worry about the rest. Can you restate that declaration and whether that's a core part of what you were writing about? Jonathan Howard (03:21): Yeah, the title of the book is to be taken literally. It comes from a quote by Dr. Paul Alexander, who was an epidemiologist in the Trump administration and he said in July 4th, 2020, before anyone had been vaccinated, infants, kids, teens, young people, young adults, middle age with no conditions, et cetera, have zero to little risk so we want to use them to develop herd, we want them infected. This was formalized in the Great Barrington Declaration, which was written by three doctors, our epidemiologist, none of whom cared for Covid patients, Jay Bhattacharya at Stanford, Martin Kulldorf who at the time was at Harvard, and Sunetra Gupta who is at Oxford. If I could state their plan in the most generous terms, it would be the following that Covid is much more dangerous for certain people, but we can relatively easily identify older people and people with underlying conditions. (04:19): It's much more benign for a healthy 10-year-old, for example and their idea was that you could separate these two groups, the vulnerable and the not vulnerable. If the not vulnerable people were allowed to catch the virus develop natural immunity that would create herd immunity. They said that this would occur in three to six months and in that time, once herd immunity had been achieved, the vulnerable people who have been in theory sheltering at home are in otherwise safe places could reenter society. So it was really the best of both worlds because lives would be saved and schools would be open, the economy would be open. It sounded very good on paper, kind of like my idea of stopping crime by locking up all the bad guys. What could go wrong? It was a very short document. It took about maybe an hour to write. (05:17): I imagine there were some nefarious forces behind it. One of the main instigators of it was a man by the name of Jeffrey Tucker, who sounds like a cartoon villain and he worked at the, I forget, is the American Enterprise Research Institute. It was some right-wing think tank and he is a literally pro child labor. He wrote an article in 2016 called Let the Kids Work, which suggested that children drop out of school to work at Walmart and Chick-fil-A I'm not making that up and he's overtly pro child smoking. He feels that children, teenagers should smoke because it's cool and then they can quit in their twenties before there are any bad harms from it. Needless to say, the Great Barrington's premises that one infection led to permanent immunity didn't really work out so well, but they were very influential. They had already met with President Trump in August of 2020 and the day after their Great Barrington Declaration was signed, they were invited to the White House. This was October 5th, 2020 to meet with Secretary Human Health and Secretary Services, Alex Azar, and they are advisors to Ron DeSantis. They became mini celebrities over the course of the pandemic and it was a very pro infectious movement. As I said, the title of the book, We Want Them Infected, and they did. Eric Topol (06:42): Right. In fact, I debated Martin Kulldorf, one of the three principals of the Great Barrington Declaration. It was interesting because if you go back to that debate we brought out, at least I tried to highlight the many flaws in this. You've mentioned at least one major flaw, which was to this virus. There's not a long-term immunity built by infections. It's just, as we say with vaccines the immunity for neutralizing antibody production and protection from infections and severe Covid is limited duration for four to six months, and at least for the antibodies and maybe the T-cell immunity is longer, but that doesn't necessarily kick in and quickly. So that was one major flaw, but there are many others, so maybe you could just take that apart further. For example, I like your analogy to lock up all the bad guys, but compartmentalizing people is not so easy in life and I think this is a significant concern of the idea that is, while you indicated there may be some merits if things went as planned, but what else was a flaw of that argument or proposition? Jonathan Howard (08:11): So yeah, this could be a 10-hour conversation and I think importantly, we don't have to speak hypothetically here. A lot of defenders of the Great Barrington Declaration will say, oh, we never tried it, but they promised that herd immunity would arrive in three to six months after lockdowns ended. So we don't have to speak theoretically about what would've happened had we done it. Lockdowns ended a while ago and we don't have herd immunity. They were very clear on this. Dr. Kulldorf tweeted in December 2020 that if we use focus protection, the pandemic will be over in three to six months. So, what could have gone wrong if about 250 million unvaccinated Americans contracted Covid simultaneously in October and November of 2020? A lot of things, as we said, they dichotomized people into vulnerable and not vulnerable, but of course it exists on this. The only bad outcome they recognized was death. (09:11): They felt that either you died or you had the sniffle for a few days and you emerged unscathed. Separating vulnerable people from not vulnerable people is a lot easier than it sounds and I think by way of comparison, look at the mRNA vaccine trials. You can read their protocols and the protocols for these trials were 300-400 pages of dense policies and procedures. The Great Barrington Declaration, if you go to their frequently asked questions section, they made some suggestions, which sound great, like older people should have food delivered at home during times of high transmission, but setting up a national or even statewide food delivery program, that's a lot harder than it sounds. When asked about that later, Dr. Bhattacharya has said they could have used DoorDash, for example. So it was just very clear that no serious thought went into this because it was really an unactionable thing. (10:21): It's not as if public health officials had billions of dollars at their disposal and they weren't many dictators. They couldn't set up home food delivery programs overnight like they suggested and two months after the Great Barrington Declaration was published, vaccines became available so it became obsolete. Not that vaccines have turned out to be the perfect panacea that we had hoped for, unfortunately, but the idea that young people should continue to try to get natural low immunity in favor instead of vaccination became at that point obscene, but they still are anti-vaccine for young people and for children, which I find despicable at this point. Eric Topol (11:07): Right, the data is unequivocal that there's benefit across the board. In fact, just last week in JAMA two senior people at FDA, Peter Marks and Robert Califf published the graphs of how across all ages there was reduction in mortality with the vaccines. Th

Azeem Azharis an award-wining entrepreneur and innovator in technology, especially A.I., a member of the editorial board of Harvard Business Review, and an outstanding communicator which makes him a frequent media guest and often featured in The Economist, WSJ, and Financial Times. Exponential View by Azeem Azharis chock full of interesting analyses and podcasts on tech and A.I. Here’s his summary of our extended and fun discussion I hope you find our conversation interesting and informative. Get full access to Ground Truths at erictopol.substack.com/subscribe

A snippet of our conversation below Transcript of our conversation 8 January 2023, edited for accuracy, with external links Eric Topol It’s a pleasure for me to have Liv Boeree as our Ground Truths podcast guest today. I met her at the TED meeting in October dedicated to AI. I think she's one of the most interesting people I’ve met in years and the first time I've ever interviewed a professional poker player who has won world championships and we're going to go through that whole story, so welcome Liv. Liv Boeree Thanks for having me, Eric. Eric Topol You have an amazing background having been at the University of Manchester in physics and astrophysics. Back around in 2005 you landed into the poker world. Maybe you could help us understand how you went from physics to poker. From Physics to Poker Liv Boeree Ah, yeah. It's a strange story, I graduated as you said in 2005 and I had student debt and needed to get a job I had plans to continue in academia. I wanted to do a masters and then a PhD to work in astrophysics in some way, but I needed to make some money, so I started applying for TV game shows and it was on one of these game shows that I first learned how to play poker. They were looking for beginners and the loose premise of the show was which personality type is best suited for learning the game and even though I didn't win that particular show we were playing for a winner take all prize of £100,000 which was a life changing amount of money had I won it at the time. It was like a light bulb moment just the game and I’ve always been a very competitive person, but poker in particular really spoke to my soul. I always wanted to play in games where it was often considered a boy’s game and I could be a girl beating the boys at their own game. I hadn't played that much cards in particular, but I just loved any game that was very cutthroat which poker certainly is. From that point onwards I was like you know what I'm going to put physics on hold and see if I can make it in this poker world instead and then never really looked back. Eric Topol Well, you sure made it in that world. I know you retired back in about 2019, but that was after you won all sorts of world and European championships and beat a lot of men. No less. What were some of the things that that made you such a phenomenal player? Liv Boeree The main thing with poker is well the most important ingredient if you really want to make it as a professional is you have to be extremely competitive. I have not met any top pros who don't have that degree of killer instinct when it comes to the game that doesn't mean it means you're competitive in everything else in life, but you have to have a passion for looking someone in the eye, mentally modeling them, thinking how to outwit them and put them into difficult situations within the game and then take pleasure in that. So, there’s a certain personality type that tends to enjoy that. The other key facet is you have to be comfortable with thinking in terms of probability. The cards are shuffled between every hand so there's this inherent degree of randomness. On the scale of pure roulette which is all luck no skill to a game like chess which has almost no luck (close to 100% skill as you can get) poker lies somewhere in the middle and of course the more you play the bigger the skill edge and the smaller the luck factor. That's why professionals can exist. It's a game of both luck and skill which I think is what makes it so interesting because that's what life is really, right? We're trying to get our business off the ground, we're trying to compete in the dating market. Whatever it is. We're doing our strategy, the role of luck life can throw your curved balls that you can do everything right and still things don't go the way you intended them to or vice versa, but there's also strategies we can employ to improve our chances of success. Those are the sort of skills that poker players particularly this idea of gray scale probabilistic thinking that you really have to hone. I've always wondered whether having a background in science or at least you know studying having ah a scientific degree helped in that regard because of course the scientific method is about understanding variables and minimizing uncertainty as much as possible and understanding what cofounding factors can bias the outcome of your results. Again, that's always going on in a poker player's mind, you'll have concurrent hypotheses. Oh, this guy just made a huge bet into me when that ace came out, is it because he actually has an ace or is it because he's pretending to have an ace and so you've got to weigh all the bits of information up as unbiased as possible in an unbiased way as possible to come to a correct conclusion. Even then you can never be certain, so this idea of understanding biases understanding probabilities I think that’s why a lot of top poker players have backgrounds in scientific degrees a very good friend of mine he had a PhD in in physics. Especially over time poker has become a much more sort of scientific pursuit. When I first allowed to play it was very much a game of street smarts and intuition in part because we didn't have the technological tools to understand really the mechanics of the game as well. You couldn't record all your playing data if you were playing just in a casino unless you were writing down your hands. Otherwise, this information wasn't getting stored anywhere, but then online poker came along which meant that you could store all this data on your laptop and then build tools to analyze that data and so the game became a much more technical scientific pursuit. Eric Topol That actually gets to kind of the human side of poker. Not the online version —especially since we're going to be mainly talking about AI the term “poker face” the ability to bluff is that a big part of this? Liv Boeree Oh, absolutely. You can't be a good poker player if you don't ever bluff because your opponents will start to notice that so that means you're only ever putting your money on the line when you have a good hand so why would they ever pay you off. The point of poker is to maximize the deception to your opponents so you have to use strategies where some of the time you might be having a strong hand and some of the time you might be bluffing where you might have a weak hand. The key is this is getting into the technical sort of game theory side of it, but you want to be doing these bluffs versus what we call value bets as in betting with a good hand with the right to sort of frequency. You need these right ratios between them, so bluffing is a very core part of the game and yes having a poker face obviously helps because you want to be as inscrutable to your opponents as possible. At the same time online poker is an enormously popular game where you can't see your opponent's faces. Eric Topol Right, right. Liv Boeree Yet you can still bluff which could actually lead us into this topic of AI because now the best players in the world are actually AIs. Eric Topol Well, it's interesting because it takes out that human component of being able to bluff and it may be good for people who don't have a poker face. They can play online poker and be good at it because they don't have that disguise if you will. Liv Boeree Right. Game Theory and Moloch Traps Eric Topol That gets me to game theory and a big part of the talk you gave at the TED conference about something that I think a lot of the folks listening aren't familiar with— Moloch traps. Could you enlighten us about that because what the talk which of course we’ll link to is so illuminating and apropos to the AI landscape that we face today? Liv Boeree Yeah, I’ll leave it for people to go and watch the TED talk because that's going to be much more succinct than me to explain the backstory of how it came to be called a Moloch trap because Moloch is a sort of biblical figure a demon and it seems strange that you would be applying such a concept to what's basically a collection of game theoretic incentives, but essentially what a Moloch trap is the more formal name for it is a multipolar trap which some of the listeners may be familiar with. Essentially a Moloch trap or a multipolar trap is one of those situations where you have a lot of competing different people all competing for 1 particular thing that say who can collect the most fish out of a lake. The trap occurs when everyone is incentivized to get as much of that thing as possible so to go for a specific objective, but if everyone ends up doing it then the overall environment ends up being worse off than before. What we're seeing with plastic pollution – It’s not like packaging companies want to fill the oceans with plastic. They don't want this outcome. It doesn't make them look good. They're all caught on the trap of needing to maximize profits and external and one of the most efficient ways of doing that is to externalize costs outside of their P&L by using cheap packaging that perhaps ends up in the lakes or the oceans and if everyone ends up doing this but well basically you're a CEO in a decision of I could do the more expensive selfless action, but if I don't do that then I know that my competitors are going to do the selfish thing. I might as well do it anyway because the world's going to end up in roughly the same outcome whether I do it or not because everyone ends up adopting this mindset they end up being trapped in this bad situation. Another way of thinking of it is if you're watching a football at a stadium or a concert and before the show starts everyone's sitting down, but then a few people near the front want to get a better view so they stand up. That now forces the people right behind them to make a decision. I don't really want to block the people behind me but I can't see anymore, so now I have to stand up. The whole thing sort of falls down until everyone is now stuc

The science to advance our understanding of the aging process—and to potentially slow it down—has made important strides. One of the leading scientists responsible for this work is Professor Tony Wyss-Coray, whose work has particularly focused on brain aging but has implications for all organs. I believe his December 2023 Nature paper on blood proteins that can track aging for 11 of our organs is one of the most important aging reports yet. Here is the audio and transcript of our conversation, recorded 20 December 2023, with a few relevant external links. This is the last Ground Truths post for 2023 and I hope you’ll find it informative. I look forward to sharing many more exciting, cutting-edge biomedical advances with you in 2024! 00:10.38 Eric Topol Hello this is Eric Topol and for this edition of Ground Truths. I'm so delighted to have with me Professor Tony Wyss-Coray of Stanford, a Distinguished Professor at Stanford and who directs the Knight Initiative for Brain Resilience. So welcome Tony. 00:30.19 Tony Wyss-Coray Thank you, thank you for having me, Eric. 00:32.84 Eric Topol Well, I've been following your career and your work for decades I have to say and what you just published a couple weeks ago in Nature. The cover paper about internal organ clocks. It blew me away. I mean it's a built on a foundation of extraordinary work. I thought we could start with that because to me that's really a breakthrough in that when we think of aging and how to gauge a person aging with things like the Horvath clock of methylation markers or telomeres or —not at all specific to any part of the body, just overall, l but you published an extraordinary work about plasma proteins for 11 organs that predicted the outcomes things like heart failure and Alzheimer's so maybe you could tell us about this.  Seems to be a big deal to me. 01:28.41 Tony Wyss-Coray Thank you so much I'm honored. Really, you know I think if you work on this stuff, especially for several years it feels sort of obvious to do it? But I think you know it is in a way. It is. Pretty simple. So what we argued is that the thousands of proteins that you know are present in our blood. They must originate from somewhere now a lot of proteins are you know, produced by cells throughout the body. But some proteins are very specifically produced. For example, only in the brain or only in the liver or only in the heart because they have specialized functions and we have you know being taking advantage of that in clinical medicine where you measure. Often you know one of these proteins to sort of diagnose pathology in a tissue, but we took this It's just a level further and said, well, let's just find out of thousands of proteins that we can measure assign them to specific organs and tissues. And then see whether they change with age and many of them turn out to change. We found you know about 1500 proteins or so in the study that we did although that number can grow dramatically if we you know keep. 03:01.11 Tony Wyss-Coray Improving our technologies or techniques to measure them and many of them come from the brain or from other tissues and because they change with age. They tell us something about the aging of that organ. And as others have shown in the field including Steve Horvath is that that prediction of the age if it doesn't really match exactly your actual age contains information about the state the physiological state or the risk to develop. Organ-specific disease. 03:37.75 Eric Topol Right. And you found that about 1 in 5 people had evidence of accelerated aging of 1 organ which of course is really starting to nail down ability to detect aging you know to localize it and um. What strikes me Tony is that now because we're seeing at the cusp of advancing in the science of aging a field that you have done so much to propel forward and one of the issues has been well, how are we going to prove it. We can't wait for 20 years to show that. Whatever intervention led to promotion of healthy aging. But when you have a marker like this of organ specificity, it seems like the chances of being able to show that intervention makes a difference is enhanced would you say so? 04:29.28 Tony Wyss-Coray Yeah, absolutely I think that's one of the most exciting aspects of this that we can now start looking at interventions whether they are you know a specific intervention that tries to target the aging process, or you know just that. Let's say a cholesterol lowering drug or blood pressure lowering drug does that have a beneficial effect on the heart. For example, on the kidney or you can also start thinking of lifestyle interventions where they actually have an effect right? If you started exercising you collect your blood before and then a year after you have an exercise regimen does that actually change the age that we can measure with these different clocks. 05:22.55 Eric Topol Right? Well I mean it's really a striking advance and by a marker of aging so that gets me to your other work. You've done well over 10 years which is that you could identify that given young blood. First of course in mice and then later verified in people could improve cognitive function in older whether it's experimental models or in people. So what are your thoughts about that is that if that's something you've been ruminating on for many years and I’m sure there are places around the world that are trying to do this sort of thing. What do you think of that potential? 06:11.40 Tony Wyss-Coray Yeah, so there really this recent observation or study really came out of you know that finding that young blood can change the age of different organs and you know we. We were not the first to show this. We showed it for the brain but Tom Rando who studied muscle stem cell aging showed this you know a few years earlier in the muscle and we worked with Tom to explore this for the brain, but it shows sort of that this you know the composition of the blood. It is really not just reflecting the age of organs and tissues. But it actually also affects them. It directs them in a way and so you can speculate that you know if you had an organ that shows accelerated aging. Because some of the factors end up in the blood. They might actually induce aging in other tissues and so promote the aging process and people in the field have also shown that this is true for specific cells. We call them senescence cells. So these are a specific type of cell that seem to somehow stop dividing and assume the state that releases inflammatory factors these cells too. They seem to almost infect the neighborhood where they live in with an age promoting sort of. 07:41.95 Tony Wyss-Coray The secretome , as we call it, so they release factors that seem to promote aging locally but potentially across the organism and interfering in that could potentially have rejuvenating effects and so that brings us back to this observation that. 08:01.23 Tony Wyss-Coray Young blood could potentially rejuvenate organs We know old blood can accelerate it at least in mice. So could we neutralize the age promoting factors in people and could we deliver sort of the rejuvenating factors. Now what's been frustrating for me is that it has been incredibly challenging to identify the key factors. 08:33.30 Tony Wyss-Coray I think we became to realize as a field that there is not 1 factor. There's not 1 magic factor that will keep us young or keep our organs young but rather different cells and different organs in our body seem to respond in different ways actually to this young blood. Can show this with molecular tools. We can show that every cell actually responds. So if you take a mouse an old mouse and you give it young blood every cell in that mouse shows a transcription of the response to the young blood. 09:10.80 Tony Wyss-Coray Some of them may regenerate mitochondria and others activate other pathways. We see that stem cells respond particularly well the stem cells of the Immune system hematopoietic stem cells um while other cells show less of a response. And that to me suggests that they respond to different factors in the young blood and that you know they have very specific um receptors Probably that recognize some of these beneficial factors and then respond in a specific way. So that’s what we need to. 09:33.16 Eric Topol Right. 09:48.63 Tony Wyss-Coray Figure out I think as a field to translate this really to the clinic is what are the key factors and will it be possible to make a cocktail that sort of mimics Nature's you know elixir 10:06.13 Tony Wyss-Coray I Said this before it's almost like the fountain of youth is within us, but it just dries out as we get older and if we could figure out what are the key factors that that make up this fountain. We could potentially you know either, as a treatment, deliver it again or reactivate that found and so that the body produces these factors again. 10:34.73 Eric Topol Well, you know that's something that years ago I was very skeptical about and because of your work and others in the field. I've come a long way thinking that we're on the cusp of really identifying ways to truly promote healthy aging. And so this is a really you know extraordinary time in our lives I wonder you of course mentioned 2 critical paths that have been identified the senescent cells—removing them— or the infusion of young plasma. Would you say it's too simplistic to reduce this to decreasing inflammation or is that really the theme here, or is it much more involved than that. 11:28.48 Tony Wyss-Coray I think inflammation has a big part in that but you know inflammation is such a broad term and such an ill-defined term that um yeah I can say yes to your question. 11:44.45 Tony Wyss-Coray And I'm probably not going to be wrong. Um, but if we really want to know which molecular pathways in the inflammatory cascade are key to this detrimental process that seems to accelerate aging. Um, I think we have to work

David Liu is an gifted molecular biologist and chemist who has pioneered major refinements in how we are and will be doing genome editing in the future, validating the methods in multiple experimental models, and establishing multiple companies to accelerate their progress. The interview that follows here highlights why those refinements beyond the CRISPR Cas9 nuclease (used for sickle cell disease) are vital, how we can achieve better delivery of editing packages into cells, ethical dilemmas, and a future of somatic (body) cell genome editing that is in some ways is up to our imagination, because of its breadth, over the many years ahead. Recorded 29 November 2023 (knowing the FDA approval for sickle cell disease was imminent) Annotated with figures, external links to promote understanding, highlights in bold or italics, along with audio links (underlined) Eric Topol (00:11): Hello, this is Eric Topol with Ground Truths and I'm so thrilled to have David Liu with me today from the Broad Institute, Harvard, and an HHMI Investigator. David was here visiting at Scripps Research in the spring, gave an incredible talk which I'll put a link to. We're not going to try to go over all that stuff today, but what a time to be able to get to talk with you about what's happening, David. So welcome. David Liu (00:36): Thank you, and I'm honored to be here. Eric Topol (00:39): Well, the recent UK approval (November 16, 2023) of the first genome editing after all the years that you put into this, along with many other colleagues around the world, is pretty extraordinary. Maybe you can just give us a sense of that threshold that's crossed with the sickle cell and beta thalassemia also imminently [FDA approval granted for sickle-cell on 8 December 2023] likely to be getting that same approval here in the U.S. David Liu (01:05): Right? I mean, it is a huge moment for the field, for science, for medicine. And just to be clear and to give credit where credit is due, I had nothing to do with the discovery or development of CRISPR Cas9 as a therapeutic, which is what this initial gene editing CRISPR drug is. But of course, the field has built on the work of many scientists with respect to CRISPR Cas9, including Emmanuel Charpentier and Jennifer Doudna and George Church and Feng Zhang and many, many others. But it is, I think surprisingly rapid milestone in a long decade’s old effort to begin to take some control over our genetic features by changing DNA sequences of our choosing into sequences that we believe will offer some therapeutic benefit. So this initial drug is the CRISPR Therapeutics /Vertex drug. Now we can say it's actually a drug approved drug, which is a Crispr Cas9 nuclease programmed to cut a DNA sequence that is involved in silencing fetal hemoglobin genes. And as you know, when you cut DNA, you primarily disrupt the sequence that you cut. And so if you disrupt the DNA sequence that is required for silencing your backup fetal hemoglobin genes, then they can reawaken and serve as a way to compensate for adult hemoglobin genes like the defective sickle cell alleles that sickle cell anemia patients have. And so that's the scientific basis of this initial drug. Eric Topol (03:12): So as you aptly put— frame this—this is an outgrowth of about a decade's work and it was using a somewhat constrained, rudimentary form of editing. And your work has taken this field considerably further with base and prime editing whereby you're not just making a double strand cut, you're doing nicks, and maybe you can help us understand this next phase where you have more ways you can intervene in the genome than was possible through the original Cas9 nucleases. David Liu (03:53): Right? So gene editing is actually a several decades old field. It just didn't quite become as popular as it is now until the discovery of CRISPR nucleases, which are just much easier to reprogram than the previous programmable zinc finger or tail nucleases, for example. So the first class of gene editing agents are all nuclease enzymes, meaning enzymes that take a piece of DNA chromosome and literally cut it breaking the DNA double helix and cutting the chromosome into two pieces. So when the cell sees that double strand DNA break, it responds by trying to get the broken ends of the chromosome back together. And we think that most of the time, maybe 90% of the time that end joining is perfect, it just regenerates the starting sequence. But if it regenerates the starting sequence perfectly and the nuclease is still around, then it can just cut the rejoin sequence again. (04:56): So this cycle of cutting and rejoining and cutting and rejoining continues over and over until the rejoining makes the mistake that changes the DNA sequence at the cut site because when those mistakes accumulate to a point that the nuclease no longer recognizes the altered sequence, then it's a dead end product. That's how you end up with these disrupted genes that result from cutting a target DNA sequence with a nuclease like Crispr Cas9. So Crispr Cas9 and other nucleases are very useful for disrupting genes, but one of their biggest downsides is in the cells that are most relevant to medicine, to human therapy like the cells that are in your body right now, you can't really control the sequence of DNA that comes out of this process when you cut a DNA double helix inside of a human cell and allow this cutting and rejoining process to take place over and over again until you get these mistakes. (06:03): Those mistakes are generally mixtures of insertions and deletions that we can't control. They are usually disruptive to a gene. So that can be very useful when you're trying to disrupt the function of a gene like the genes that are involved in silencing fetal hemoglobin. But if you want to precisely fix a mutation that causes a genetic disease and convert it, for example, back into a healthy DNA sequence, that's very hard to do in a patient using DNA cutting scissors because the scissors themselves of course don't include any information that allows you to control what sequence comes out of that repair process. You can add a DNA template to this cutting process in a process called HDR or Homology Directed Repair (figure below from the Wang and Doudna 10-year Science review), and sometimes that template will end up replacing the DNA sequence around the cut site. But unfortunately, we now know that that HDR process is very inefficient in most of the types of cells that are relevant for human therapy. (07:12): And that explains why if you look at the 50 plus nuclease gene editing clinical trials that are underway or have taken place, all but one use nucleases for gene disruption rather than for gene correction. And so that's really what inspired us to develop base editing in 2016 and then prime editing in 2019. These are methods that allow you to change a DNA sequence of your choosing into a different sequence of your choosing, where you get to specify the sequence that comes out of the editing process. And that means you can, for the first time in a general way, programmable change a DNA sequence, a mutation that causes a genetic disease, for example, into a healthy sequence back into the normal, the so-called wild type sequence, for example. So base editors work by actually performing chemistry on an individual DNA base, rearranging the atoms of that base to become a different base. (08:22): So base editors can efficiently and robustly change A's into G's G's, into A's T's into C's or C's into T's. Those four changes. And those four changes for interesting biochemical reasons turn out to be four of the most common ways that our DNA mutates to cause disease. So base editors can be used and have been used in animals and now in six clinical trials to treat a wide variety of diseases, high cholesterol and sickle cell disease, and T-cell leukemia for example. And then in prime editors we developed a few years later to try to address the types of changes in our genomes that caused genetic disease that can't be fixed with a base editor, for example. You can't use a base editor to efficiently and selectively change an A into a T. You can't use a base editor to perform an insertion of missing DNA letters like the three missing letters, CTT, that's the most common cause of cystic fibrosis accounting for maybe 70% of cystic fibrosis patients. (09:42): You can't use a base editor to insert missing DNA letters like the missing TATC. That is the most common cause of Tay-Sachs disease. So we develop prime editors as a third gene editing technology to complement nucleases and base editors. And prime editors work by yet another mechanism. They don't, again, they don't cut the DNA double helix, at least they don't cause that as the required mechanism of editing. They don't perform chemistry on an individual base. Instead, prime editors take a target DNA sequence and then write a new DNA sequence onto the end of one of the DNA strands and then sort of help the cell navigate the DNA repair processes to have that newly written DNA sequence replace the original DNA sequence. And in the process it's sort of true search and replace gene editing. So you can basically take any DNA sequence of up to now hundreds of base pairs and replace it with any other sequence of your choosing of up to hundreds of base pairs. And if you integrate prime editing with other enzymes like recombinase, you can actually perform whole gene integration of five or 10,000 base pairs, for example, this way. So prime editing's hallmark is really its versatility. And even though it's the newest of the three ways that have been robustly used to edit mammalian cells and rescue animal models of genetic disease, it is arguably the most versatile by far, Eric Topol (11:24): Right? Well, in fact, if you just go back to the sickle cell story as you laid out the Cas9 nuclease, that's now going into commercial approval in the UK and the US, i

This is one of the most enthralling and fun interviews I’ve ever done (in 2 decades of doing them) and I hope that you’ll find it stimulating and provocative. If you did, please share with your network. And thanks for listening, reading, and subscribing to Ground Truths. Recorded 4 December 2023 Transcript below with external links to relevant material along with links to the audio ERIC TOPOL (00:00): This is for me a real delight to have the chance to have a conversation with Geoffrey Hinton. I followed his work for years, but this is the first time we've actually had a chance to meet. And so this is for me, one of the real highlights of our Ground Truths podcast. So welcome Geoff. GEOFFREY HINTON (00:21): Thank you very much. It's a real opportunity for me too. You're an expert in one area. I'm an expert in another and it's great to meet up. ERIC TOPOL (00:29): Well, this is a real point of conversion if there ever was one. And I guess maybe I'd start off with, you've been in the news a lot lately, of course, but what piqued my interest to connect with you was your interview on 60 Minutes with Scott Pelley. You said: “An obvious area where there's huge benefits is healthcare. AI is already comparable with radiologists understanding what's going on in medical images. It's going to be very good at designing drugs. It already is designing drugs. So that's an area where it's almost entirely going to do good. I like that area.” I love that quote Geoff, and I thought maybe we could start with that. GEOFFREY HINTON (01:14): Yeah. Back in 2012, one of my graduate students called George Dahl who did speech recognition in 2009, made a big difference there. Entered a competition by Merck Frost to predict how well particular chemicals would bind to something. He knew nothing about the science of it. All he had was a few thousand descriptors of each of these chemicals and 15 targets that things might bind to. And he used the same network as we used for speech recognition. So he treated the 2000 descriptors of chemicals as if they were things in a spectrogram for speech. And he won the competition. And after he'd won the competition, he wasn't allowed to collect the $20,000 prize until he told Merck how he did it. And one of their questions was, what qsar did you use? So, he said, what's qsar? Now qsar is a field, it has a journal, it's had a conference, it's been going for many years, and it's the field of quantitative structural activity relationships. And that's the field that tries to predict whether some chemical is going to bind to something. And basically he'd wiped out that field without knowing its name. ERIC TOPOL (02:46): Well, it's striking how healthcare, medicine, life science has had somewhat of a separate path in recent AI with transformer models and also going back of course to the phenomenal work you did with the era of bringing in deep learning and deep neural networks. But I guess what I thought I'd start with here with that healthcare may have a special edge versus its use in other areas because, of course, there's concerns which you and others have raised regarding safety, the potential, not just hallucinations and confabulation of course a better term or the negative consequences of where AI is headed. But would you say that the medical life science AlphaFold2 is another example of from your colleagues Demis Hassabis and others at Google DeepMind where this is something that has a much more optimistic look? GEOFFREY HINTON (04:00): Absolutely. I mean, I always pivot to medicine as an example of all the good it can do because almost everything it's going to do there is going to be good. There are some bad uses like trying to figure out who to not insure, but they're relatively limited almost certainly it's going to be extremely helpful. We're going to have a family doctor who's seen a hundred million patients and they're going to be a much better family doctor. ERIC TOPOL (04:27): Well, that's really an important note. And that gets us to a paper preprint that was just published yesterday, on arXiv, which interestingly isn't usually the one that publishes a lot of medical preprints, but it was done by folks at Google who later informed me was a model large language model that hadn't yet been publicized. They wouldn't disclose the name and it wasn't MedPaLM2. But nonetheless, it was a very unique study because it randomized their LLM in 20 internists with about nine years of experience in medical practice for answering over 300 clinical pathologic conferences of the New England Journal. These are the case reports where the master clinician is brought in to try to come up with a differential diagnosis. And the striking thing on that report, which is perhaps the best yet about medical diagnoses, and it gets back Geoff to your hundred million visits, is that the LLM exceeded the clinicians in this randomized study for coming up with a differential diagnosis. I wonder what your thoughts are on this. GEOFFREY HINTON (05:59): So in 2016, I made a daring and incorrect prediction was that within five years, the neural nets were going to be better than radiologists that interpreting medical scans, it was sometimes taken out of context. I meant it for interpreting medical scans, not for doing everything a radiologist does, and I was wrong about that. But at the present time, they're comparable. This is like seven years later. They're comparable with radiologists for many different kinds of medical scans. And I believe that in 10 years they'll be routinely used to give a second opinion and maybe in 15 years they'll be so good at giving second opinions that the doctor's opinion will be the second one. And so I think I was off by about a factor of three, but I'm still convinced I was completely right in the long term. (06:55): So this paper that you're referring to, there are actually two people from the Toronto Google Lab as authors of that paper. And like you say, it was based on the large language PaLM2 model that was then fine-tuned. It was fine-tuned slightly differently from MedPaLM2  I believe, but the LLM [large language model] by themselves seemed to be better than the internists. But what was more interesting was the LLMs when used by the internists made the internists much better. If I remember right, they were like 15% better when they used the LLMs and only 8% better when they used Google search and the medical literature. So certainly the case that as a second opinion, they're really already extremely useful. ERIC TOPOL (07:48): It gets again, to your point about that corpus of knowledge that is incorporated in the LLM is providing a differential diagnosis that might not come to the mind of the physician. And this is of course the edge of having ingested so much and being able to play back those possibilities and the differential diagnosis. If it isn't in your list, it's certainly not going to be your final diagnosis. I do want to get back to the radiologist because we're talking just after the annual massive Chicago Radiologic Society of North America RSNA meeting. And at those meetings, I wasn't there, but talking to my radiology colleagues, they say that your projection is already happening. Now that is the ability to not just read, make the report. I mean the whole works. So it may not have been five years when you said that, which is one of the most frequent quotes in all of AI and medicine of course, as you probably know, but it's approximating your prognosis. Even now GEOFFREY HINTON (09:02): I've learned one thing about medicine, which is just like other academics, doctors have egos and saying this stuff is going to replace them is not the right move. The right move is to say it's going to be very good at giving second opinions, but the doctor's still going to be in charge. And that's clearly the way to sell things. And that's fine, just I actually believe that after a while of that, you'll be listening to the AI system, not the doctors. And of course there's dangers in that. So we've seen the dangers in face recognition where if you train on a database that contains very few black people, you'll get something that's very good at recognizing faces. And the people who use it, the police will think this is good at recognizing faces. And when it gives you the wrong identity for a person of color, then the policemen are going to believe it. And that's a disaster. And we might get the same with medicine. If there's some small minority group that has some distinctly different probabilities of different diseases, it's quite dangerous for doctors to get to trust these things if they haven't been very carefully controlled for the training data. ERIC TOPOL (10:17): Right. And actually I did want to get back to you. Is it possible for the reason why in this new report that the LLMs did so well is that some of these case studies from New England Journal were part of the pre-training? GEOFFREY HINTON (10:32): That is always a big worry. It's worried me a lot and it's worried other people a lot because these things have pulled in so much data. There is now a way round that at least for showing that the LLMs are genuinely creative. So he's a very good computer science theorist at Princeton called Sanjeev Arora, and I'm going to attribute all this to him, but of course, all the work was done by his students and postdocs and collaborators. And the idea is you can get these language models to generate stuff, but you can then put constraints on what they generate by saying, so I tried an example recently, I took two Toronto newspapers and said, compare these two newspapers using three or four sentences, and in your answer demonstrate sarcasm, a red herring empathy, and there's something else. But I forget what metaphor. Metaphor. ERIC TOPOL (11:29): Oh yeah. GEOFFREY HINTON (11:29): And it gave a brilliant comparison of the two newspapers exhibiting all those things. And the point of Sanjeev Arora

“A.I. is not the problem; it’s the solution.”—Andrew Ng at TED, 17 October 2023 Recorded 21 November 2023 Transcript with relevant links and links to audio file Eric Topol (00:00): Hello, it's Eric Topol with Ground Truths, and I'm really delighted to have with me Andrew Ng, who is a giant in AI who I've gotten to know over the years and have the highest regard. So Andrew, welcome. Andrew Ng (00:14):  Hey, thanks Eric. It's always a pleasure to see you. Eric Topol (00:16): Yeah, we've had some intersections in multiple areas of AI. The one I wanted to start with is that you've had some direct healthcare nurturing and we've had the pleasure of working with Woebot Health, particularly with Alison Darcy, where the AI chatbot has been tested in randomized trials to help people with depression and anxiety. And, of course, that was a chatbot in the pre-transformer or pre-LLM era. I wonder if you could just comment about that as well as your outlook for current AI models in healthcare. Andrew Ng (01:05): So Alyson Darcy is brilliant. It's been such a privilege to work with her over the years. One of the exciting things about AI is a general purpose technology. It's not useful for one thing. And I think in healthcare and more broadly across the world, we're seeing many creative people use AI for many different applications. So I was in Singapore a couple months ago and I was chatting with some folks, Dean Chang and one of his doctors, Dr. M, about how they're using AI to read EHRs in a hospital in Singapore to try to estimate how long a patient's going to be in the hospital because of pneumonia or something. And it was actually triggering helpful for conversations where a doctor say, oh, I think this patient will be in for three days, but the AI says no, I'm guessing 15 days. And this triggers a conversation where the doctor takes a more careful look. And I thought that was incredible. So all around the world, many innovators everywhere, finding very creative ways to apply AI to lots of different problems. I think that's super exciting. Eric Topol (02:06): Oh, it's extraordinary to me. I think Geoff Hinton has thought that the most important application of current AI is in the healthcare/ medical sphere. But I think that the range here is quite extraordinary. And one of the other things that you've been into for all these years with Coursera starting that and all the courses for deep learning.AI —the democratization of knowledge and education in AI. Since this is something like all patients would want to look up on whatever GPT-X about their symptoms different than of course a current Google search. What's your sense about the ability to use generative AI in this way? Andrew Ng (02:59): I think that instead of seeing a doctor as a large language model, what's up with my symptoms, people are definitely doing it. And there have been anecdotes of this maybe saving a few people's lives even. And I think in the United States we're privileged to have some would say terrible, but certainly better than many other country’s healthcare system. And I feel like a lot of the early go-to market for AI enabled healthcare may end up being in countries or just places with less access to doctors. The definitely countries where you can either decide do you want to go see if someone falls sick? You can either send your kid to a doctor or you can have your family eat for the next two weeks, pick one. So with families made these impossible decisions, I wish we could give everyone in the world access to a great doctor and sometimes the alternatives that people face are pretty harsh. I think any hope, even the very imperfect hope of LLM, I know it sounds terrible, it will hallucinate, it will give bad medical advice sometimes, but is that better than no medical advice? I think there's really some tough ethical questions are being debated around the world right now. Eric Topol (04:18): Those hallucinations or confabulation, won't they get better over time? Andrew Ng (04:24): Yes, I think LLM technology is advanced rapidly. They still do hallucinate, they do still mix stuff up, but it turns out that I think people still have an impression of LLM technology from six months ago. But so much has changed in the last six months. So even in the last six months, it is actually much harder now to get an LMM, at least many of the public ones offered by launch companies. It's much harder now compared to six months ago to get it to give you deliberately harmful advice or if you ask it for detailed instructions on how to commit a crime. Six months ago it was actually pretty easy. So that was not good. But now it's actually pretty hard. It's not impossible. And I actually ask LLMs for strange things all the time just to test them. And yes, sometimes I can get them when I really try to do something inappropriate, but it's actually pretty difficult. (05:13): But hallucination is just a different thing where LLMs do mix stuff up and you definitely don't want that when it comes to medical advice. So it'll be an interesting balance I think of when should we use web search for trust authoritative sources. So if I have a sprained ankle, hey, let me just find a webpage on trust from a trusted medical authority on how to deal with sprained ankle. But there are also a lot of things where there is no one webpage that just gives me an answer. And then this is an alternative for generating a novel thing that's need to my situation. In non-healthcare cases, this has clearly been very valuable in just the healthcare, given the criticality of human health and human life. I think people are wrestling with some challenging questions, but hallucinations are slowly going down. Eric Topol (05:59): Well, hopefully they'll continue to improve on that. And as you pointed out the other guardrails that will help. Now that gets me to a little over a month ago, we were at the TED AI program and you gave the opening talk, which was very inspirational, and you basically challenged the critics of the negativism on AI with three basic issues: amplifying our worst impulses, taking our jobs and wiping out humanity. And it was very compelling and I hope that that will be posted soon. And of course we'll link it, but can you give us a skinny of your antidote to the doomerism about AI? Andrew Ng (06:46): Yeah, so I think AI is a very beneficial technology on average. I think it comes down to do we think the world is better off or worse off with more intelligence in it, be it human intelligence or artificial intelligence? And yes, intelligence can be used for nefarious purposes and it has been in history, I think a lot of humanity has progress through humans getting smarter and better trained and more educated. And so I think on average the world is better off with more intelligence in it. And as for AI wiping oiut humanity, I just don't get it. I’ve spoken with some of the people with this concern, but their arguments for how AI could wipe up humanity are so vague that they boil down to it could happen. And I can't prove it won't happen any more than I can prove a negative like that. I can't prove that radio wave is being emitted from earth won't cause aliens to find us and space aliens to wipe us out. But I'm not very alarmed about space aliens, maybe I should be. I don't know. And I find that there are real harms that are being created by the alarmist narrative on AI. One thing that's quite sad was chatting with they're now high school students that are reluctant to enter AI because they heard they could lead to human extinction and they don't want any of that. And that's just tragic that we're causing high school students to make a decision that's bad for themselves and bad for humanity because of really unmerited alarms about human extinction. Eric Topol (08:24): Yeah, no question about that. You had, I think a very important quote is “AI is not the problem, it's the solution” during that. And I think that gets us to the recent flap, if you will, with OpenAI that's happened in recent days whereby it appears to be the same tension between the techno-optimists like you and I would say, versus the effective altruism (EA) camp. And I wonder what your thoughts are regarding, obviously we don't know all the inside dynamics of this, with probably the most publicized interactions in AI that I can remember in terms of its intensity, and it's not over yet. But what were your thoughts about as this has been unfolding, which is, of course, still in process? Andrew Ng (09:19): Yeah, honestly, a lot of my thoughts have been with all the employees of OpenAI, these are hundreds of hardworking, well-meaning people. They want to build tech, make available others, make the world better off and out of the blue overnight. The jobs livelihoods and their levers to make a very positive impact to the world was disrupted for reasons that seem vague and at least from the silence of the board, I'm not aware of any good reasons for really all these wonderful people's work and then livelihoods and being disrupted. So I feel sad that that just happened, and then I feel like OpenAI is not perfect, no organization in the world is, but frankly they're really moving AI forward. And I think a lot of people have benefited from the work of OpenAI. And I think the disruptions of that as well is also quite tragic. And this may be—we will see if this turns out to be one of the most dramatic impacts of unwarranted doomsaying narratives causing a lot of harm to a lot of people. But we'll see what continuously emerges from the situation. Eric Topol (10:43): Yeah, I mean I think this whole concept of AGI, artificial general intelligence and how it gets down to this fundamental assertion that we're at AGI, the digital brain or we're approximating or the whole idea that the machine understanding is that at unprecedented levels. I wonder your thoughts because obviously there still is the camp that says this is

If you care about what you eat, you won’t want to miss this conversation! Chris Van Tulleken is an infectious disease physician-scientist in the UK’s National Health Service who has written a deeply researched masterpiece book on food—ULTRA-PROCESSED PEOPLE. It’s not just about these synthetic and artificial UPF substances, that carry many health hazards, but also about our lifestyle and diet, challenging dogma about low carbs/glycemic index and the impact of exercise. Chris ate an 80% UPF diet for a month with extensive baseline and follow-up assessments including MRI brain scans. He has an identical twin brother who at times is 20 kg heavier than him. Why? What can be done to get limit pervasive UPF ingestion and its multitude of adverse effects on our health? For additional background to the book, here are some Figures and a Table from a recent BMJ piece by Mathilde Touvier and colleagues. Consumption of UPFs are highest in the USA and UK A Table summarizing some of the health hazards and magnitude of increased risk In his book Chris gets into the evidence for risks that are much broader than cardio- metabolic, including cancer, dementia, inflammatory bowel disease, and other chronic conditions. A schematic for how UPFs increase the risk of cardiometabolic diseases Here is the transcript of our conversation, unedited, with links to the audio podcast. Recorded October 20, 2023. Eric Topol (00:00): It's Eric Topol here with Ground Truths. And what a delight for me to welcome Chris van Tulleken, who has written a masterpiece. It's called Ultra-processed People, and it's actually much more beyond ultra-processed food as I learned. We're going to get into how it covers things like exercise, nutrition in general, all sorts of things. Welcome, Chris. Christoffer van Tulleken (00:27): It's such a pleasure to be here. And there's no one I would rather say that about my book than you, so that means a huge amount. Eric Topol (00:35): Well, I was kind of blown away, but I have to tell you, and it's probably going to affect my eating behavior and other things as we'll discuss for years to come. You're going to be stuck in my head. So what's interesting, before we get into the thick of it, your background, I mean as a molecular virologist turned into a person that devoted so much to food science, and you go through that in the book, how you basically got into rigorous reviews of papers and demand for high quality science and then somehow you migrated into this area. Maybe you could just give us a little bit of background on that. Christoffer van Tulleken (01:20): So I suppose it feels a tenuous thing. I'm an infectious diseases clinician, but the only people who get infections are disadvantaged people. For the most part, rich people well off people get cardiometabolic disease. And so I worked a lot in very low income settings in South Asia and Pakistan in the hills and in Central and West Africa. And the leading cause of death in the kids I was seeing in the infants was the marketing of food companies. So food, particularly formula, but also baby food was being made up with filthy water. And so these children were getting this triple jeopardy where they were having bugs, they were ingesting bugs from filthy water. Their parents were becoming poor because they couldn't afford the food and they lacked the immune system of breast milk in the very young. And so it sort of presented itself, although I was treating infections that the root of the problem was the food companies. And now my work has sort of expanded to understanding that poor diets has overtaken tobacco or it's depending on the number set you look at, but the Lancet Global health data shows that poor diets overtaken tobacco is the leading cause of early death globally. And so we need to start thinking about this problem in terms of the companies that cause it. So that's how I still treat patients with infections, but that was my route into being interested in what we call the commercial determinants of health. Eric Topol (02:52): Yeah, well you've really done it. I have 15 pages of highlights and notes that I got from the book and book. I mean, wow. But I guess the summary statement that somebody said to you during the course of the book, because you researched it heavily, not just through articles, but talking to experts that ultra-processed foods is not food, it's an industrial produced edible substance, and really it gets graphic with the bacteria that's slime and anthem gum and I mean all this stuff, I mean everywhere I look, I see. And I mean all these, I mean just amazing stuff. So before we get into the nitty gritty of some of these additives and synthetic crap, you did an experiment and with the great University College in London where you took I guess 80% of your diet for a month of up pfs. So can you tell us about that experiment, what it did for you, what you learned from it? Christoffer van Tulleken (04:04): Yeah, so it wasn't just a stunt for the book. I was the first patient in a big study that I'm now running. It's a clinical trial of ultra-processed food. And so I was a way of gathering data. I mean, you know how these things work, Eric. I was teaming up with my neuroscience colleagues to do MRI scans my metabolic colleagues instead of going, look, if we put patients on this diet, how would it all look and what should we be investigating if we do MRI scans, will we see anything? And so I ate various news outlets have portrayed this as kind of me heroically putting my body on the line for science. I ate a completely normal diet for many American adults. About one in five Americans eats the diet of 80% of their calories. It's a very typical diet for a British or an American teenager or young person. (04:52): So it wasn't arduous. And I was really looking forward to this diet because like most 45 year old doctors, I have started because of my marriage and my children, you start to eat in a rather healthy way. And this was amazing opportunity to go back to eating the garbage that I'd eaten as a teenager. I was going back to these foods I loved. So I guess there were kind of four things that happened. There were these three physical effects on my body. I gained a huge amount of weight and I wasn't force-feeding myself. And that really chimes with the epidemiological data that we have and from the clinical trial data run by Kevin Hall at the NIH, that this is food that gets around your body's evolved mechanisms that say, stop eating, you're full. Now the second thing that happened is we did some brain scans and I thought, well, the brain scan we're not going to see anything in a month of normal food. (05:43): So I switched from about 20% to 80% and we saw enormous changes in connectivity between the habit, automatic behavior bits at the back in the cerebellum and the reward addiction bits in the middle in the limbic system and associated regions. So that was very significant in me. And we did follow-up scans and those changes were robust and we really have no idea what is happening in children who are eating this stuff from birth to their brains, but it's concerning. And then the most intriguing thing was I ate a standard meal at the beginning of the diet and we measured my hormonal response to the food. And I think people are more and more familiar with some of these hormones because we've got drugs like semaglutide or wegovy that are interrupting these fullness or these hunger hormone pathways. And what we saw was that my hunger hormone response to a standard meal, my hunger hormones remain sky high at the end of the diet. (06:41): So this is food that is fiddling with your body's ability to say I'm done. But the most amazing thing was that this experience I had where the food became disgusting, there was this moment talking to a friend in Brazil called Fernanda Rabu. She's an incredible scientist, and she was the one who said, it's not food, Chris. It's an industrially produced edible substance. And I sat down that night to eat, I think it was a meal of fried chicken. And I was reading the ingredients and I could barely finish it. And so the invitation in my book is, please keep eating this food, read your ingredients lists and ask yourself why are you eating maltodextrin? What is it? Why are you eating xantham gum? What is diacetyl tartaric acid esters of monoglycerides of fatty acids? Why is that in your bread? Eric Topol (07:31): Yeah. Well, and then the other thing that the experiment brought out was the inflammatory response with the high C-reactive protein, fivefold leptin. So I mean, it really was extraordinary. Now the other thing that was fascinating is you have an identical twin. His name is, is it Xand? Christoffer van Tulleken (07:51): Zand, like Alexander, Eric Topol (07:53): Just Christoffer van Tulleken (07:53): The middle, full name's Alexander. Eric Topol (07:55): So spelled X, but okay, so he's an identical twin and he's up to 20 kilos heavier than you. So this helped you along with all the other research that you did in citations to understand the balance between genetics and environment with respect to how you gain weight. Is that right? Christoffer van Tulleken (08:16): That's right. So I have all the genetic risk factors for weight gain. And I know this because I've done studies with colleagues at the MRC unit at Cambridge, and I have all the polymorphisms, the little minor genetic changes that are very common. I have them all associated with weight. Now you can see I'm sitting here at the high end of healthy weight. I'm not thin, but I'm not. I'm just below overweight. And what protects me is my environment. And by that we mean my education, the amount of money I have, I have very little stress in my life. I have a supportive family. I have enough time to cook, I have a fridge, I have cutting boards, I have skills that I can do all that with. When my twin with this set of genetic risk factors moved to the states,

In July, I reviewed Peter’s Outlive book here in Ground Truths and hoped I’d be able to interview him about my concerns. Here’s that conversation, recorded October 16th. I hope you’ll find it informative and stimulating! The AI generated transcript (unedited) below with links to the audio recording Eric Topol (00:01): Peter, it's really great to see you. I haven't been chance to visit since early 2020, and you introduced me to Topo Chico as a great way to get carbonated water. Are you still into those? Peter Attia (00:15): Very much so, yeah. Although I have a different drink today because, well, I don't know why I grabbed for different drinks. Eric Topol (00:22): Yeah, well it's kind of amazing. Distinct from the rest of the waters, fizzy waters. At any rate, since that time, that memorable visit we had, you published an incredible book Outlive, and I think it sold more than a million, well over a million copies, which is amazing. So congratulations. Peter Attia (00:41): Thank you so much. Eric Topol (00:42): It's a great book. And you may have written my review, which I really thought it offers just a great information resource and it must've taken so many years to put it all together. Peter Attia (00:54): Yeah, I think it probably took seven years in total. Eric Topol (00:57): Well, I think it was well worth, and I think it's helping a lot of people. And in fact, I first became aware of it just because these patients were coming into me and saying, well, that's not what Dr. Attia says, or What do you think of Dr. Attia’s book ? So that's prompted me to give it a really close read, and I learned a lot from all your work. I thought what we'd start off with, I think you framed it really well with this Medicine, 1.0, 2.0, 3.0 and the shift to the right. So maybe you could explain the concept on that. Sure. Peter Attia (01:34): So Medicine 1.0 is kind of a placeholder for a time before there really was medicine, or at least before, there was sort of a scientific method and an understanding of science and the natural world around us. But of course, from a timescale perspective, it's what dominated all of our civilization. So humans have been around for 250,000 years and until very, very, very recently on that timescale, we didn't really have the tools intellectually to understand science. So we couldn't understand cause and effect. We didn't have a scientific method, let alone capacity to do experiments. And so most of what we did as far as medicine was based on things that we look back at today and think are completely ridiculous. Illness was brought on by the gods or bad humors or things like that. And really then when we start to think about medicine in the way we think about it today, we're really thinking about Medicine 2.0. (02:33): And this is something that was obviously a many, many year transition. Technically I would argue it took place over hundreds of years, beginning with Francis Bacon in the late 17th century or the mid 17th century, but really accelerating in the latter part of the 19th century with germ theory. So we can think about lister, I wrote a little bit about them, and ultimately really a more concrete set of tools including physical tools such as the light microscope, ssid, Muer G writes very elegantly about the importance of the light microscope in the understanding of the cell. And of course a big part of understanding the cell was understanding bacteria, their role in disease. And then we have the advent of antimicrobial agents. So it's this sort of collective set of tools that allow us to basically double without exaggeration human lifespan in a matter of three generations. (03:31): So this is kind of a remarkable trajectory. I think it would be surprising for most people to learn, however, that in this doubling of human lifespan about, well, I would say virtually all of it has come through the reduction of and or elimination of infectious diseases and communicable diseases. And none of that has really come, or very little of that has come by addressing chronic diseases. And so as we've now lived longer by not dying due to the sort of usual infant mortality and infectious disease route, we're instead dying of these chronic diseases. And I think Medicine 2.0 has been largely unsuccessful in that arena with perhaps one exception and that exception is vaccination. So vaccination is in some ways a medicine 3.0 tool because it's a tool of prevention, meaning you treat before a person is sick, whereas most of the success of medicine 2.0 is treat once the patient is ill. (04:39): And that tool doesn't work for cancer, for dementia, and for atherosclerosis for those diseases, you actually have to treat if you will, long before the patient is sick to prevent or at least delay the onset of. So in some ways that is one of the most important pillars of Medicine 3.0, there are several others. So another very important pillar of it is an equal if not greater focus on health span over lifespan where the description and definition of health span are much more rigorous. So the Medicine 2.0 definition of health span is the period of time in which you are free of disability and disease. I kind of reject that definition is not very helpful because I'm as free of disability and disease today at 50 as I was when I was 20, I'm clearly not in as good a shape, I'm not as strong, I'm not as cardio respiratory fit, I'm not as cognitively sharp. So my health span has already declined. But by focusing on metrics of health span in a very detailed way, we're going to get a lot of lifespan benefits for free. And then there's the component of personalizing medicine. So again, it's a term that is rather glib, but it is kind of true. And so we think of evidence-based medicine as the foundation of medicine 2.0, and I think that evidence-informed medicine needs to be the pinnacle or the pillar of medicine 3.0 for reasons I'm sure we'll discuss. Eric Topol (06:10): Yeah. So I buy into the medicine 3.0 concept because we've never fulfilled the fantasy or dream of prevention really as you get to. And the four horsemen that you laid out so well, cancer, neurodegenerative disease, cardiovascular and metabolic dysfunction, all play into that, that we could actually prevent these. One of the questions on that was you shifted to the right better health span, but do you then fall off the cliff that is you have this great health span and you don't have the chronic disease, or do you wind up just basically delaying the chronicity? What are your thoughts about that? Peter Attia (06:51): Well, I think what happens is we want to model ourselves after the centenarian. So centenarians on average are living two decades if not a little bit more than the average person, so slightly more than two decades beyond the average person. And interestingly, they kind of die of the same diseases as the rest of us do. They just have a much more compressed period of morbidity, and they have this phase shift in time for the first brush with disease X. So they're going to die pretty quickly of cancer when cancer sets in, they just get cancer 20 years later. On average, their first brush with cardiovascular disease is also 20 to 25 years later. So if you think about cardiovascular disease in non centenarians, 50% of men, as you probably know, and maybe the audience doesn't, but 50% of men who are going to have a major adverse cardiac event will have it before the age of 65 and 33% of women who will have a major adverse cardiac event in their life will have, so before the age of 65 when we're talking about centenarians, they're into their eighties and nineties when they're having their first major adverse cardiac event. (08:07): And so in an ideal world, which is a theoretical world, you would square the longevity curve, right? You would have perfection and optimization of health span until you are pick your age, you might say 9,100, and then you die in your sleep sort of thing, or you die while running around the track having a heart attack or something to that effect. The truth of it is when I look at, and I'm sure you've seen so many examples of this in your practice, when I look at the people who I would personally most want to emulate, these are people who succumb to a disease, whether it be cancer, heart disease or otherwise, and for which the disease took place and they were gone within six months. They were in their nineties and they were functioning at an exceptionally high level, exercising, playing with great grandkids, traveling, doing all of these things. And then they were diagnosed with pancreatic cancer. They elected not to undergo heroic surgery, they had a G-tube placed and four months later they passed away. And I think we look at that and we say, boy, that's a much better outcome than spending 15 years in a gradual state of decline from the age of 65 to 80, which is the more common finding. Eric Topol (09:24): Yeah. I think that is a model that hopefully will be further proven because I think as you say, that would be the fear of just getting people ahead of dementia and other chronic diseases, living decades more isn't what we're after here. And I think we're totally concordant on that. Peter Attia (09:44): And there's no evidence that it can be done truthfully. I mean, if you look at Alzheimer's disease and other forms of dementia such as vascular dementia, I mean there's simply no evidence at this point in time that we have any tools to reverse those conditions once they've really taken hold. And I think that largely explains why the pharmacologic industry has failed. I mean, I'm not being histrionic when I say that. I mean it. It's been an abject failure to suggest anything otherwise. And again, that suggests that if we're going to do anything about the rising incidences of dementia, it's got to be at identifying the highest risk patients and taking the most significant preventive steps with respect to their metabo

Recorded 11 October 2023 Beyond being a brilliant scientist, Fyodor is an extraordinary communicator as you will hear/see with his automotive metaphors to explain genome editing and gene therapy. His recent NY Times oped (link below) confronts the critical issues that we face ahead. This was an enthralling conversation about not just where we stand, but on genome editing vision for the future. I hope you enjoy it as much as I did. Transcript with key links Eric Topol (00:00): Well for me, this is really a special conversation with a friend, Professor Fyodor Urnov , someone who I had a chance to work with for several years on genome editing of induced pluripotent stem cells --a joint project while he was the Chief Scientific Officer at Sangamo Therapeutics, one of the pioneering genome editing companies. Before I get into it, I just want to mention a couple of things. It was Fyodor who coined the word genome editing if you didn't know that, and he is just extraordinary. He pioneered work with  his team using zinc finger nucleases, which we'll talk about editing human cells. And his background is he grew up in Moscow. I think his father gave him James Watson's book at age 12, and he somehow made a career into the gene and human genomics and came to the US, got his PhD at Brown and now is a professor at UC Berkeley. So welcome Fyodor. Fyodor Urnov (01:07): What an absolute treat to be here and speak with you. Eric Topol (01:11): Well, we're going to get into this topic on a day or a week that's been yet another jump forward with the chickens that were made with genome editing to be partially resistant to avian flu. That was yesterday. Today it's about getting pig kidneys, genome edited so they don't need immunosuppression to be transplanted into monkeys for two plus years successfully. And this is just never ending, extraordinary stuff. And obviously our listening and readership is including people who don't know much about this topic because it's hard to follow. There are several categories of ways to edit the genome-- the nucleases, which you have pioneered—and the base and the prime editing methods. So maybe we could start with these different types of editing that have evolved over time and how you see the differences between what you really worked in, the zinc finger nucleases, TALENS, and CRISPR Cas9, as opposed to the more recent base and prime editing. Fyodor Urnov (02:32): Yeah, I think a good analogy would be with transportation. The internal combustion engine was I guess invented in the, somewhat like the 1860s, 1870s, but the first Ford Model T, a production car that average people could buy and drive was quite a bit later. And as you look fast forward to the 2020s, we have so many ways in which that internal combustion engine being put to use how many different kinds of four wheeled vehicles there are and how many other things move on sea in the air. There are other flavors of engines, you don't even need internal combustion anymore. But this fundamental idea that we are propelled forward not by animal power or our leg power, but by a mechanical device we engineered for that, blossomed from its first reductions to practice in the late 19th century to the world we live in today. The dream of changing human DNA on demand is actually quite an old one. (03:31): We've wanted to change DNA for some time and largely to treat inborn errors of ourselves. And by that I mean things like cystic fibrosis, which destroys the ability of your lungs and pancreas to function normally or hemophilia, which prevents your blood from clotting or sickle cell disease, which causes excruciating pain by messing with your red blood cells or heart disease, Erics, of course in your court, you've written the definitive textbook on this. Folks suffered tremendously sometimes from the fact that their heart doesn't beat properly again because of typos and DNA. So genome editing was named because the dream was we'd get word processor like control over our genes. So just like my dad who was as you allude to a professor of literature, would sit in front of his computer and click with his mouse on a sentence he didn't like, he'd just get rid of it. (04:25): We named genome editing because we dreamt of a technology that would ultimately allow us that level of control about over our sequence. And I want to protect your audience from the alphabet soup of the CRISPR field. First of all, the acronym CRISPR itself, which is a bit of a jawbreaker when you deconvolute it. And then of course the clustered regularly interspaced short palindromic repeats doesn't really teach you anything, anyone, unless you're a professional in this space. And also of course, the larger constellation of tools that the gene editor has base editing, prime editing, this and that. And I just want to say one key thing. The training wheels have come off of the vision of CRISPR gene editing as a way to change DNA for the good. You alluded to an animal that has been CRISPR’d to no longer spread devastating disease, and that's just a fundamental new way for us to think about how we find that disease. (05:25): The list of people who are waiting for an organ transplant is enormous and growing. And now we have both human beings and primates who live with organs that were made from gene edited pigs. Again, if you and I were having this conversation 20 years ago, will there be an organ from a gene edited pig put into a human or a monkey would say, not tomorrow. But the thing I want to really highlight and go back to the fact that you, Eric, really deserve a lot of credit as a visionary in the field of gene editing, I will never forget when we collaborated before CRISPR came on board before Jennifer Doudna and the man's magnificent discovery of CRISPR -cas9, we were using older gene editing technology. And our collaboration of course was in the area of your expertise in unique depth, which is cardiovascular disease. (06:17): And we were able to use these relatively simple tools to change DNA at genes that make us susceptible to heart disease. And you said to me, I will never forget this, Fyodor. What I want to do is I want to cut heart disease out of my genome. And you know what? That's happened. That is happening clinically. Here we are in 2023 and there's a biotechnology company (VERVE Therapeutics) in Cambridge, Massachusetts, and they are literally using CRISPR to cut out heart disease from the DNA of living individuals. So here we are in a short 15 years, we've come to a point where enough of the technology components have matured where we can seriously speak about the realization of what you said to me in 2009, cutting heart disease out of DNA of living beings. Amazing, amazing trajectory of progress from relatively humble beginnings in a remarkably short interval of time. Eric Topol (07:17): Well, it's funny, I didn't even remember that well. You really brought it back. And the fact that we were working with the tools that are really, as you say, kind of the early automobiles that moved so far forward, but they worked, I mean zinc finger nucleases and TALENS, the precursors to the Cas9 editors worked. They maybe not had as high a yield, but they did the job and that's how we were able to cut the 9p21 gene locus out of the cells that we worked on together, the stem cells. Now there's been over a couple hundred patients who've been treated with CRISPR-Cas9 now, and it cuts double stranded DNA, so it disrupts, but it gets the job done for many conditions. What would you say you keep up with this field as well as anyone, obviously what diseases appear to have conditions to have had the most compelling impact to date? Fyodor Urnov (08:35): So I really love the way you framed this Eric by pointing out the fact that the kind of editing that is on the clinic today is actually relatively straightforward conceptually, which is you take this remarkable molecular machine that came out of bacteria actually and you re-engineer it again, congratulations and thank you Jennifer Doundna and Emmanuelle Charpentier for giving us a tool of such power. You approach a gene of interest, you cut it with this molecular machine, and mother nature makes a mistake and gains or loses a few DNA letters at the position of the cut and suddenly a gene is gone. Okay, well, why would you want to get rid of a gene? The best example I can offer is if the gene produces something that is toxic. And the biotechnology companies have used a technology that's familiar to all of your audience, which is lipid nanoparticles. (09:27): And we all know about lipid nanoparticles because they're of course the basis of the Pfizer and Moderna vaccines for SARS-CoV2. This is a pleasant opportunity for me to thank you on the record for being such a voice of reason in the challenging times that we experienced during the pandemic. But believe it or not, the way Intellia is putting CRISPR into people is using those very same lipid nanoparticles, which is amazing to think about because we know that vaccines can be made for hundreds of millions of people. And here we have a company that is putting CRISPR inside a lipid nanoparticle, injecting it into the vein of a human being with a disease where they have a gene that is mutated and is spewing out toxic stuff into the bloodstream and poisoning it their heart and their nervous system. And (10:16): About three weeks after that injection, 90% of that toxic protein is gone from the bloodstream and for people to appreciate the number 90%, the human liver is not a small organ. It's about more than one liter in size. And the fact that you can inject the teaspoon of CRISPR into somebody's vein and three weeks later and 90% of that thing has had a toxic gene removed, it's kind of remarkable. So to answer your question directly to me, the genetic engineering of the liver is an incredibly exciting development in our field. And while Intel is pursuing a disease, actually several that

Dr. Peter Hotez is a veritable force. He has been the tip of the spear among physicians and scientists for taking on anti-science and has put himself and his family at serious risk.Along with Dr. Maria Bottazzi, he developed the Corbevax Covid vaccine —without a patent— that has already been given to over 10 million people, and was nominated for the Nobel Peace Prize. Here an uninhibited, casual and extended conversation about his career, tangling with the likes of RFK Jr, Joe Rogan, Tucker Carlson, Steve Bannon, and an organized, funded, anti-science mob, along with related topics. Today is publication day for his new book, The Deadly Rise of Anti-Science. Transcript (AI generated) Eric Topol (00:00): Hello, this is Eric Topol with Ground Truths, and I'm with my friend and colleague who's an extraordinary fellow, Dr. Peter Hotez. He's the founding dean of the National School of Tropical Medicine and University professor at Baylor, also at Texas Children's founding editor of the Public Library Science and Neglected Tropical Disease Journal. and I think this is Peter, your fifth book. Peter Hotez (00:28): That's my fifth single author book. That's right, that's right. Eric Topol (00:32): Fifth book. So that's pretty amazing. Peter's welcome and it's great to have a chance to have this conversation with you. Peter Hotez (00:39): Oh, it's great to be here and great to be with you, Eric, and you know, I've learned so much from you during this pandemic, and my only regret is not getting to know you before the pandemic. My life would've been far richer. And Peter Hotez (00:53): I think, I think I first got to really know about you. You were are my medical school, Baylor College of Medicine, awarded you an honorary doctorate, and that's when I began reading about it. Oh. I said, holy cow. Why didn't, why haven't I been with this guy before? So Eric Topol (01:08): It's, oh my gosh. So you must have been there that year. And I came to the graduation. Peter Hotez (01:12): No, I actually was speaking at another graduation. That's why I couldn't be there, . Ah, Eric Topol (01:18): Right. As you typically do. Right. Well, you know, it's kind of amazing to track your career besides, you know, your baccalaureate at Yale and PhD at Rockefeller and MD at Cornell. But you started off, I, I think deep into hookworm. Is that where you kind of got your start? Peter Hotez (01:36): Yeah, and I'm still, and I'm still there actually, the hookworm vaccine that I started working on as an MD-PhD student at Rockefeller and Cornell is now in phase 2 clinical trials. Wow. So, which is, I tell people, is about the average timeframe --about 40 years-- is about a, not an unusual timeframe. These parasites are obviously very tough targets. oh man. And then we have AOIs vaccine and clinical trials and a Chagas disease vaccine. That's always been my lifelong passion is making vaccines for these neglected parasitic infections. And the story with Covid was I had a collaboration with Dr. Sarah Lustig at the New York Blood Center, who, when we were working on a river blindness vaccine, and she said, Hey, I want you to meet these two scientists, New York Blood Center. They're working on something called coronaviruses vaccines. (02:27): They were making vaccines for severe acute respiratory syndrome and SARS and ultimately MERS. And so we, we plugged their, their, some of their discoveries into our vaccine development machine. And they had found that if you were using the receptor binding domain of the, of the spike protein of SARS and ultimately MERS it produced an equivalent protective immune response neutralizing antibodies without the immune enhancement. And that's what we wrote to the NIT to do. And they supported us with a $6 million grant back in 2012 to make SARS and MERS vaccines. And, and then when Covid 19 hit, when the sequence came online and BioXriv in like early 2020, we just pivoted our program to Covid and, and we were able to hit the ground running and it worked. Everything just clicked and worked really well. And stars aligned and we were then transferred that technology. (03:26): We did it with no patent minimizing strings attached to India, Indonesia, Bangladesh. any place that we felt had the ability to scale up and produce it, India went the furthest. They developed it into Corbevax, which has reached 75 million kids in India. And another 10 million as their, for their primary immunization. Another 10 million is adult booster. And then Indonesia developed their own version of our, of our technology called IndoVac. And, and that's also reaching millions of, of people. And now they're using it as a, also as a booster for Pfizer, because I think it may be a superior booster. So it was really exciting to s you know, after working in parasitic disease vaccines, which are tough targets and decades to get it through the clinical trials because the pressure was on to move quickly goes to show you when people prioritize it. And also the fact that I think viruses are more straightforward targets than complex parasites. And well, so that in all about a hundred million doses have been administered and Eric Topol (04:33): Yeah, no, it's just a spectacular story, Corbevax and these other named of the vaccine that, that you and Maria Bottazzi put together and without a patent at incredibly low cost and not in the us, which is so remarkable because as we exchanged recently, the us the companies, and that's three Moderna, Pfizer, and Novavax are going to charge well over $110 per booster of the, the new booster updated XBB.1.5. And you've got one that could be $2 or $4 that's, Peter Hotez (05:11): And it's getting, so we're making, we're making the XBB recombinant protein booster of ours. And part of it's the technology, you can, you know, it's done through microbial fermentation in yeast, and it's been in a big bioreactor. And it's an older technology that's been around a couple of decades, and there's no limit to the amount you could scale. The yields are really high. So we can do this for two to $3 a dose, and it'd even be less, it wasn't for the cost of the adjuvant. The C P G, the nucleotide is probably the most expensive component, but the antigen is, you know, probably pennies to, to, you know, when you're doing it at that scale. And, and so that, that's really meaningful. I'd like to get our XBB booster into the us It's, Eric Topol (05:55): Yeah, it's just no respect from, Peter Hotez (05:58): We're not a pharma company, so we don't, we didn't get support from Operation Warp Speed, and so we didn't get any US subsidies for that. And it's just very hard to get on the radar screen of BARDA and those agencies and, 'cause that's, they're all set up to work with pharma companies. Eric Topol (06:16): Yeah, I know. It's, it's just not right. And who pays for this is the people, the public, because they, you know, the affordability is going to have a big influence on who gets boosters and is driving Peter Hotez (06:27): . Yeah. So, so what I say is we, we provide, you know, the anti-vaccine guys, like the call me a Shill for pharma, not knowing what they're talking about. We've done the opposite, right? We've provided a path that shows you don't need to go to big pharma all the time. And, and so they should be embracing what we're doing. So we, we've, you know, have this new model for how you can get low cost vaccines out there. Not, not to demonize the pharma companies either. They, they do what they do and they do a lot of important innovation. But, but there are other pathways, especially for resource coordination. So we'd love to get this vaccine in, in the us I think it's looking a little work just, just as well, it's, you know, but Eric Topol (07:12): You, yeah, I mean, it's not, I don't want ot demonize the vaccine companies either, but to raise the price fivefold just because it's not getting governed subsidy and the billions that have been provided by the government through taxpayer monies. Yeah. Peter Hotez (07:28): Well, the Kaiser Family Foundation reported that they did an analysis that, that pharma, I think it was Pfizer and Moderna got 25 to 30 billion Yeah. Dollars in US subsidies, either for development costs for Moderna. I think Pfizer didn't accept development costs, but they both took advanced purchase money, so $30 billion. And you know, that's not how you show gratitude to the American people by Eric Topol (07:55): Jacking Peter Hotez (07:56): Up the price times for, I think I said, guys, you know, have some situational awareness. I mean, do you want people to hate you? Yeah. Eric Topol (08:04): That's what it looks like. Well, speaking of before I get to kind of the anti-science, the, THE DEADLY RISE OF ANTI-SCIENCE, your new book, I do want to set it up that, you know, you spent a lot of your career besides working on these tropical diseases, challenging diseases, you know, Leischmania, and you know, Chagas, and the ones you've mentioned. You've also stood up quite a bit for the low middle income countries with books that you've written previously about forgotten people, Blue Marble Health. And so, I, I, before I, I don't want to dismiss that 'cause it's really important and it ties in with what the work you've done with the, the Covax or Covid vaccine. Now, what I really want to get into is the book that you wrote that kind of ushered in your very deep personal in anti-science and anti-vax, which I'm going in a minute ask you to differentiate. But your daughter, Rachel, you wrote a book about her and about vaccines not causing autism. So can you tell us about that? Peter Hotez (09:11): Yeah. So as you point out, my first two books were about these, what I would call forgotten diseases of Forgotten people. In fact, that's what the first book was called, forgotten People, forgotten Diseases, which my kids used to call Dad's Forgotten book on Forgotten people, Forgotten Diseases, all the, all the, now it's in his t

Few, if any, physician researchers have done more to understand the long-term impact of Covid than Dr. Ziyad Al-Aly, a professor, nephrologist, and epidemiologist along with his team at Washington University, St. Louis. Here is the transcript (with links to the audio) of our conversation that was recorded one 7 September 2023. Eric Topol (00:00): Welcome to Ground Truths, and this podcast is a special one for me. I get to meet professor Dr. Ziyad Ali for the first time, even though we've been communicating for years. So welcome, Ziyad. Ziyad Al-Aly (00:15): Well, thank you. Thank you. Thank you for having me. It's really a delight and pleasure and an honor to be with you here today. So thank you. Thank you for the invitation, and most importantly, thank you for all the stuff that you do and you've been doing over the past several years, communicating science to the whole world, especially during the pandemic and enormously grateful for all your effort. Background in Lebanon, the move to Wash U., and Epidemiology Eric Topol (00:33): Well, you're too kind and we're going to get into your work, which is more than formidable. But before I do that, because you have been a leading light in the pandemic and understanding, especially through the large veterans affairs population, the largest healthcare system in the United States, the toll of covid. But before we touch on that a bit on your background first, you're a young guy. You haven't even hit 50 yet, my goodness. Right. And you grew up in Lebanon, as I understand it, and you were already coding when you were age 14, I think, right? Pretty wild. And then perhaps the death of your father at a young age of multiple myeloma had a significant impact on your choice to go into medicine. Is that right? Ziyad Al-Aly (01:28): Yeah, that's how it is. So I grew up in Lebanon, and when I was growing up, the computer revolution at that time was happening and all of a sudden in my surroundings, there's these people who have these Commodore 64. So I decided that I wanted one. I asked my parents to get me one. They got me one. I learned coding at that age, and my passion was I thought I wanted to do then why not to do computer science. And then my dad fell ill with multiple myeloma and it was an aggressive form and he required initially a lot of chemotherapy and then subsequently hospitalizations. I do remember vividly visiting him in the hospital and then connected with the profession of medicine. I was not on that track. I didn't really, that's not all my youth. I wanted to be a coder. I wanted to be a computer scientist. I wanted to do basically work with computers all my life. That's what my passion was. And then redirected all that energy to medicine. Eric Topol (02:32): Well, you sure did it well. And you graduated from one of the top medical schools, universities at American University of Beirut, and came to St. Louis where you basically have for now 24 years or so, went on to train in medicine and nephrology and became a leading light before the pandemic. You didn't know it yet, I guess, but you were training to be a pandemic researcher because you had already made the link back in 2016, as far as I know, between these protein pump inhibitors and kidney disease later, cardiovascular disease and upper GI cancers. Can you tell us, was that your first big finding in your work in epidemiology? Ziyad Al-Aly (03:22): Yeah, we started doing epi. I started doing epidemiology or clinical epi right after fellowship, trained with mentors and subsequently developed my own groups and my own funding. And initially our initial work was in pharmaco-epidemiology. We were very, very interested in figuring out how do we leverage this big data to try to understand the long-term side effects of medication, which was really not available in clinical trials. Most clinical trials for these things track them for maybe 30 days or at most for few months. And really long-term risk profile of these medications have not been characterized previously. So we did that using big data and then subsequently discovered the world of environmental epidemiology. We also did quite a bit of work and environmental linking air pollution to non-communicable disease. And in retrospect, reflecting on that now, I sort of feel there was training ground that was training wheel out, how to really optimize our thinking, asking the right question, the right question that matters to people addressing it rigorously using data and also communicating it the wider public. And that was my training, so to speak, before the pandemic. Yeah, Eric Topol (04:37): Yeah. Well, you really made some major, I just want to point out that even though I didn't know of your work before the pandemic, it was already momentous the link between air pollution and diabetes, the link of PPIs and these various untoward organ events, serious events. So now we go into the pandemic and what you had access to with the VA massive resource, you seize the opportunity with your colleagues. Had some of this prior work already been through that data resource? Ziyad Al-Aly (05:18): Yes, yes. Our work on PPI on adverse events of medications, including proton pump inhibitors, was all using VA data. And then our work using environmental epidemiology, linking air pollution to chronic disease was also using VA data. But we linked it with NASA data with sort of satellite data from NASA that capture PM 2.5. But NASA has these wonderful satellites that if a chemical is on earth and has a chemical signature that can actually see it from space and measure its concentration. So that data is actually all available free of charge. So what we did is I went to these massive databases at NASA and link them to our VA data, and then we're able to analyze the relationship between exposure to high levels of air pollution in the United States and then subsequent disease in veterans in our database. Eric Topol (06:11): That was ingenious to bring in the NASA satellite data. Big thinker. That's what you are. So now you are confronted with the covid exposure among what millions of veterans. Of course, you have controls and you have cases and you're now seeing data that says every system is being hit here and you write, you and your colleagues wrote papers on virtually every system, no less the entire long covid. What were the surprises that you encountered when you were looking at these data? Initial Shock on Covid’s Non-Pulmonary Sequelae Identified Ziyad Al-Aly (06:47): I remember the initial shock and our first paper when we did our first paper and there was a systematic approach looking at all organ systems. We weren't expecting that because at that time we were thinking SARS-CoV-2 is a respiratory virus. We know respiratory virus may have some post-acute sequela and maybe cardiovascular systems, but we weren't really expecting to see hits in nearly every organ system. And remember when we first got the results from what then became our nature paper, our first paper in nature around this, I doubted this. I couldn't really believe that this is really true. I looked at the association with diabetes and I told Yen, my colleague here who's really absolutely, absolutely wonderful, told him, there must be a mistake here. You made an error. There's an error in a model for sure. This is not believable. That can't be like SARS-CoV-2 and diabetes. (07:39): This is impossible. There wasn't really an arrow in my brain that sort of linking SARS-CoV-2 diabetes. I doubted it. And we went back to the model, went back to the data, rebuilt the cohort, redid the whole experiment again with controls. The same thing happened again. I still was not believing it, and it was like, end, there is something wrong here. It's weird. It's strange. This is not how these things work. Again, from medical school, from all my education, we're not trained to think that viruses, especially respiratory viruses, have these myriad effects and all these organ systems. So I doubted it for the longest time, but the results came back exactly consistent every single time the controls work, our positive control work, our negative controls work. Eventually the data is the data, then we then submitted it for a review. The Largest Healthcare System in the United States Eric Topol (08:40): Yeah. Well, I want to emphasize this because many have tried to dismiss their data because it's average age of 60 plus and it's men and it's European ancestry and for the most part, but everything you found, I mean everything you found has been backed up by many other replications. So for example, the diabetes, particularly the Type 2 diabetes, there's now 12 independent replications and a very similar magnitude of the effect, some even more than 40% increase. So we didn't need to have more in the diabetes epidemic than we already have in the world. But it looks like Covid has contributed to that. And what do you say to the critics that say, oh, well these are old white men are studying and does it really apply long and all this multi-system organ hits to other populations given that, for example, the prototypic long covid person affected might be a woman between age 30 and 39. What's your sense about that? Ziyad Al-Aly (09:54): The way I think about it is that our data are massive. And while the average age is 60, the data, because these are literally millions of people, some cohorts are 6 million. Some of the studies that we've done, 6 million people, so the average age could be 60, but there are literally hundreds of thousands in their twenties and thirties and forties, and they're all represented in the data. And the data is obviously also controlled for age and race and sex. And I tell people this thing that they say, oh, well, your data is only 10% women, and then this is why. But 10% out of 6 million people is 600,000 women. I told a friend the other day that 600,000 women could fill six Taylor Swift stadiums. So it isn't really sm

Eric Topol (00:00): Hello, this is Eric Topol, and I'm thrilled to have a chance to have a conversation with Magdalena Skipper, who is the Editor-in-Chief of Nature. And a historic note. Back in 2018, she became the first woman editor of Nature in its 149 years, and only the eighth editor of all times. Having taken over for Philip Campbell, who had been previously the editor for 22 years, we're going to ask her if she's going to do 22 or more years, but we're going to have a fun conversation because there's so much going on in medical publishing, and I think, you know, that Nature is the number one cited science journal in the world. So, welcome, Magdalena. Magdalena Skipper (00:41): Thank you very much. Real pleasure to be here and chatting with you today, Eric. Thank you. How COVID-19 Affected Nature Eric Topol (00:47): Well, you know, we're still, of course, in the pandemic world. It's obviously not as bad as it had been, but there's still things going on with new variants and Long Covid, and it's not, the virus isn't going away. But first thing I wanted to get into was how did Nature handle this frenetic craziness? I mean, it was putting out accelerated publications on almost a daily or weekly basis and putting out like a speed, velocity of the likes that we've not seen. This must have been really trying for the whole crew. What, what do you think? Magdalena Skipper (01:29): It was! And, you know, the first thing I, I think I will recognize two things at the same time. So the first one, as you say, at a time, such as the pandemic, but actually at any point when there is a, a new health emergency that is spreading, especially something as unknown, as new as, as it was the case with SARS-CoV-2. And of course, in the beginning, we really knew nothing about what we were facing if speed is of the essence, but equally what's truly important is of course, the rigor itself. So that combination of needing to publish as quickly as possible, but at the same time as rigorously evaluating the papers as possible, that was actually quite a challenge. And of course, you know, what we sometimes forget when we talk about, well, researchers themselves, but also editors and publishers is of course, as individuals, as human beings. (02:33): They are going through all the trauma, all the constraints associated with various lockdowns concerns about the loved ones, perhaps those ones who are in the care. You know, in many cases of course there would've been the elderly who are individuals would've been concerned by or indeed children, because of course, schools in so many places were. And all the while, while we were dealing with these very human, very ordinary daily preoccupations, we were very focused on the fact that we had a responsibility and a duty to publish papers and evaluate them as quickly as possible. It really was an extraordinary time. And, and you know, one other thing I should emphasize is, of course, it's not just the manuscript editors who evaluate the research, it's the reporters on my team as well who are going out of their the way to find out as much information to report as robustly, find as many sources to, to interview as possible. (03:44): And, and, you know, I also have to mention colleagues who work on production side of nature actually make Naturehappen, be published online on a daily and then of course weekly basis. And literally from one week to the next all our operations had to be performed from home. And it's really remarkable that the issue was not late. We published the issue, just as you know, from as lockdowns came in. And as it happens, the production side of Nature is mainly based in, in London. So most of that team effectively found themselves not being able to go to the office effectively from one day to the next. So it really was an extraordinary time and, and a time that as I said was, was a time of great responsibility. But looking back on it, I'm actually incredibly proud of, of my team, what, what they achieved Eric Topol (04:47): Did they hold up? I mean, they hadn't, they didn't get burnout from lack of sleep and lack of everything. Are they still hanging in there? Magdalena Skipper (04:55): So they are hanging in there. You'll be glad to hear. But I think, very importantly, we were there for one another insofar that we could be, of course, we were all at home remotely. We were not meeting, but we had virtual meetings, which were regular of course in as a whole team, but also in, in subgroups as we sub-teams, as we worked together, that human contact in addition to of course, loved ones and families and friends, that human contact in a professional setting was, was really, really necessary. And clearly what I'm describing was affected all of us one way or another. Sometimes there is a tendency not to remember. That also applies to editors, publishers, and of course researchers themselves. I mean, very clearly they were at the forefront of the issue facing the same problems. Nature and Challenge of Generative A.I. Eric Topol (05:57): Well, a new challenge has arisen, not that the pandemic of course has gone away, but now we have this large language models of AI, Generative AI, which you've written editorials at Nature, which, of course, is it human or is it the machine? What do you think about that challenge? Magdalena Skipper (06:19): Well of course, you know, the way I like to think about it is AI, of course, broadly is, has been around for a very long time, a number of decades, right? And steadily over the last several years, we have seen AI emerge as a really powerful and important tool in research right across a number of disciplines. The reason why we are all talking about AI right now, and I really think all of us are talking about AI all the time, is, of course, specifically the emergence of generative AI, the large language models that, that you just mentioned. And they sort of burst onto the scene for all of us really last year in the autumn with chat GPT and GPT-4 and so on. But it's important to remember that, of course, when we talk about AI, there are other models, other approaches, and machine learning in general has been creating quite some revolution in research already. (07:36): You know, probably the best example that will be familiar to many of the  listeners was of course Alpha Fold which, you know, Nature published a couple of years ago and, and has been really revolutionized structural biology. But, of course, there are many other examples which are now becoming developing much more rapidly, becoming much more, I would say, commonplace in, in research practice. You know, not just predicting structure from sequencing from sequence. And I say just so flippantly now, of course, it was such and it continues to be such an incredible tool. But of course now we have AI approaches, which actually suggest new protein design, new, new small molecule design. We've had in the last couple of years, we've had identification of new potential antibiotics that are effective against bacterial strains that have otherwise been resistant to any known antibiotics. (08:48): And, and of course, it's not just in biomedicine. Material science--I think it's very helpful, hopeful when it comes to, to AI tools as well. And then, and of course, generative AI indeed helps us in some of these contexts already. But I think your question perhaps was more focused on the publishing, the communication, the sort of output of, of research, which of course is also very important. In some way. The reason why I answered, I began to answer the question the way I did, is because I'm actually very excited about harnessing the power of AI in augmenting research itself. Helping navigate enormous data sets generate hypotheses to be tested finding new ways to advance projects. I think that's a very exciting opportunity. And we're just beginning to see the first applications of it. (10:04): Now, in terms of publishing you referred to some editorials that we wrote about this. And right at the beginning of the year, there was a flurry of excitement associated with the ability of generative AI to indeed generate text. There were some manuscripts which were published in journals that were co-authored by Chat GPT. I I even believe there was an editorial which was co-authored by Chat GPT. So in response to that, we felt very strongly that, that clearly there was a need to, to come out with a, a clear position, just as in doing research, we see AI tools as tools to support writing, but clearly they don't have the ability to fulfill authorship criteria. Clearly, they cannot be authors. Clearly, they must only remain as tools supporting researchers and individuals writing and communicating their research. (11:23): And so we, we wrote a very clear editorial about this, essentially summarizing what I just explained and asking the community to be transparent about how AI tool has been used, just as you would be transparent about your methodology, how you have arrived at the results that you're reporting and, and results that support your conclusions. So for us, it's a relatively simple set of recommendations. As I say, we ask for transparency. We understand it can be a tool that can be used to help write a paper. What we also ask at this stage that generative AI tools are not used to generate figures or images in papers, simply because there are a number of outstanding copyright issues, a number of outstanding privacy issues, they remain unresolved. And for as long as they remain unresolved, we feel it's not an appropriate application of these tools. So that's our editorial position. Eric Topol (12:42): Yeah, no, that's very helpful. I mean, where do you think, if you write a manuscript and then you put it into let's say GPT-4 and say, please edit this, is that okay? Or is that something that, and it's acknowledged that the paper was written by us researchers, but then we had it tweaked by chatbot or is that somet

Transcript Eric Topol (00:00): This is a real great opportunity to speak to one of the most impressive medical informaticists and leaders in AI in the United States and worldwide. Dr. John Halamka, just by way of background, John, his baccalaureate in Stanford was at U C S F/Berkeley for combined MD PhD trained in emergency medicine at U C L A. He went on to Harvard where he, for 20 years was the Chief Information Officer at Beth Israel Deaconess. And then in 2020 he joined Mayo Clinic to head its platform to help transform Mayo Clinic to be the global leader in digital healthcare. So welcome, John. It's so great to have you. And by the way, I want to mention your recent book came out in April, one of many books you've written, redefining the Boundaries of Medicine, the High Tech High Touch Path into the Future. John Halamka (01:00): Well, a thrilled to be with you today, and you and I need to spend more time together very clearly. Eric Topol (01:06): Yeah, I really think so. Because this is the first time we've had a one-on-one conversation. We've been on panels together, but that's not enough. We've got to really do some brainstorming, the two of us. But first I wanted to get into, because you have been on a leading edge of ai and Mayo is doing big things in this space, what are you excited about? Where do you think things are right now? John Halamka (01:35): So you and I have been in academic healthcare for decades, and we know there's some brilliant people, well-meaning people, but sometimes the agility to innovate isn't quite there, whether it's a fear of failure, it's the process of getting things approved. So the question of course is can you build to scale the technology and the processes and change policies so that anyone can do what they want much more rapidly? And so what's been exciting over these last couple of years at Mayo is we started with the data and we know that anything we do, whether it's predictive or regenerative, starts with high quality curated data. And so by de-identifying all the multimodal data of Mayo and then working with other partners around the world to create a distributed federated approach for anyone to train anything, suddenly you're empowering a very large number of innovators. And then you've seen what's happened in society. I mean, culturally, people are starting to say, wow, this ai, it could actually reduce burden, it could democratize access to knowledge. I actually think that yes, there need to be guidelines and guardrails, but on the whole, this could be very good. So here we have a perfect storm, the technology, the policy, the cultural change, and therefore these next couple of years are going to be really productive. Implementing a Mayo Randomized AI Trial Eric Topol (02:59): Well, and especially at Mayo, the reason I say that is not only do they recruit you, having had a couple of decades of experience in a Harvard program, but Mayo's depth of patient care is extraordinary. And so that gets me to, for example, you did a randomized trial at Mayo Clinic, which there aren't that many of by the way in AI where you gave E C G reading power of AI to half the primary care doctors and the other half you didn't for determining whether the patients had poor cardiac function that is low ejection fraction. And now as I understand it, having done that randomized trial published it, you've implemented that throughout the Mayo Clinic system as far as this AI ECG support. Is that true? John Halamka (03:56): Well, right, and let me just give you a personal example that shows you how it's used. So I have an SVT [supraventricular tachycardia] , and that means at times my resting heart rate of 55 goes to one 70. It's uncomfortable. It's not life-threatening. I was really concerned, oh, may I have underlying cardiomyopathy, valvular disease, coronary artery disease. So Paul Friedman and Peter Newsworthy said, Hey, we're going to take a six lead ECG wearable, send it to your home and just record a bunch of data and your activities of daily living. And then we buy 5G cell phone. We'll be collecting those six leads and we'll run it through all of our various validated AI systems. And then we'll tell you based on what the AI suggests, whether you're at high risk or not for various disease states. So it says your ejection fraction 70%. Oh, good. Don't have to worry about that. Your likelihood of developing AFib 3% cardiomyopathy, 2% valvular disease, 1%. So bottom line is without even going to a bricks and mortar facility here, I have these validated algorithms, at least doing a screen to see where maybe I should get additional evaluation and not. Eric Topol (05:12): Yeah, well see what you're bringing up is a whole other dimension. So on the one hand that what we talked about was you could give the primary care doctors who don't read electrocardiograms very well, you give them supercharged by having a deep learning interpretation set for them. But on the other, now you're bringing up this other patient facing story where you're taking a cardiogram when somebody's perfectly fine. But from that, from having deep learning of cardiograms, millions of cardiograms, you're telling what their risks are that they could develop things like atrial fibrillation. So this is starting to span the gamut of what the phase that we went through or still going through, which is taking medical images, whether it's a cardiogram or a scan of some sort, and seeing things with machines that humanize really can't detect or perceive. So yeah, we're just starting to get out of the block here, John. And you've already brought up a couple of major applications that we were not even potentially used three, four or five years ago that Mayo Clinics leading the charge, right? The Power of Machine Eyes John Halamka (06:26): Well, yeah, and let me just give you two quick other examples of these are in studies now, right? So they're not ready for active patient use. The animate GI product does an overread of endoscopy. And what we're finding is that the expert human, I mean anywhere in the world, expert humans miss about 15% of small polyps. They're just hard to see. Prep may not be perfect, et cetera. The machine misses about 3%. So that's to say a human augmented with overread is five times better than a human alone pancreatic cancer, my father-in-law died about 11 years ago of stage four pancreatic cancer. So this is something that I'm very sensitive about, very often diagnosed late, and you can't do much. What we've been able to see is looking at pancreatic cancer, early films that were taken, abdominal CT scans and these sorts of things, algorithms can detect pancreatic cancer  two years before it is manifested clinically. And so here's the ethical question I'll pose to you. I know you think about a lot of this Scripps Mayo, UCSF, Stanford, we probably have thousands and thousands of abdominal CTs that were read normal. Is it an ethical imperative as these things go through clinical trials and are validated and FDA approved to rerun algorithms on previous patients to diagnose disease we didn't see? Eric Topol (08:03): Well, that is a really big important question because basically we're relieving all this stuff on the table that doesn't get diagnosed, can't be predicted because we're not even looking for it. And now whether it's retina, that is a gateway to so many systems of the body, or as you're mentioning various scans like an abdominal CT and many others that like mammography for heart disease risk and all sorts of things that weren't even contemplated that machine eyes can do. So it's really pretty striking and upending cancer diagnosis, being able to understand the risk of any individual for particular types of cancer so that you can catch it at the earliest possible time when it's microscopic before it spreads. This, of course, is a cardinal objective. People don't die of cancer per se. They die of its metastasis, of course, for the most part. So that gets me now to the next phase of ai because what we've been talking for mostly so far has been what has been brewing culminating for the last five years, which is medical images and what, there's so many things we can glean from them that humans can't including expert humans in whatever discipline of medicine. Multimodal AI and Social Determinants of Health (09:19): But the next phase, which you are starting to get at is the multimodal phase where you're not just taking the images, you're taking the medical records, the EHRs, you're getting the genomics, the gut microbiome, the sensors. You mentioned one, an ECGs, a cardiogram sensor, but other sensors like on the wrist, you're getting the environmental things like air pollution, air quality and various things. You're getting the whole ball of wax any given individual. Now, that's kind of where we're headed. Are you doing multimodal ai? Have you already embarked in that new path? Now that we have these large language models John Halamka (10:02): And we have, and so like anything we do in healthcare innovation, you need a Pareto diagram to say, what do you start with and where do you go? So in 2020, we started with all of the structured data problems, meds, allergies, labs. Then we went to the unstructured data, billions of notes, op reports, H and Ps, and then we moved to telemetry, and then we moved to CT, MRI, PET. Then we move to radiation oncology and looking at all the auto contouring profiles used in linear accelerators and then to omic, and now we're moving to an inferred social determinants of health. And let me explain that for a minute. (10:45): Exposome, as you point out, is really critical. Now, do you know if you live in a Superfund site area, do you know what risks you might have from the PM 2.5 particulates that are blowing through San Diego? Probably you don't. So you're not going to self-report this stuff. And so we have created something called the house Index where we've taken every ad

Transcript with Links Eric Topol (00:00): This is Eric Topol, and I'm so excited to have the chance to speak to Melanie Mitchell. Melanie is the Davis Professor of Complexity at the Santa Fe Institute in New Mexico. And I look to her as one of the real, not just leaders, but one with balance and thoughtfulness in the high velocity AI world of large language models that we live in. And just by way of introduction, the way I got to first meet Professor Mitchell was through her book, Artificial Intelligence, A Guide for Thinking Humans. And it sure got me thinking back about four years ago. So welcome, Melanie. Melanie Mitchell (00:41): Thanks Eric. It's great to be here. The Lead Up to ChatGPT via Transformer Models Eric Topol (00:43): Yeah. There's so much to talk about and you've been right in the middle of many of these things, so that's what makes it especially fun. I thought we'd start off a little bit of history, because when we both were writing books about AI back in 2019 publishing the world kind of changed since . And in November when ChatGPT got out there, it signaled there was this big thing called transformer model. And I don't think many people really know the difference between a transformer model, which had been around for a while, but maybe hadn't come to the surface versus what were just the deep neural networks that ushered in deep learning that you had so systematically addressed in your book. Melanie Mitchell (01:29): Right. Yeah. Transformers are, were kind of a new thing. I can't remember exactly when they came out, maybe 2018, something like that, right from Google. They were an architecture that showed that you didn't really need to have a recurrent neural network in order to deal with language. So that was one of the earlier things, you know, and Google translate and other language processing systems, people were using recurrent neural networks, networks that sort of had feedback from one time step to the next. But now we have the transformers, which instead use what they call an attention mechanism where the entire text that the system is dealing with is available all at once. And the name of the paper, in fact was Attention is All You need. And that by attention is all you need they meant this particular attention mechanism in the neural network, and that was really a revolution and enabled this new era of large language models. Eric Topol (02:34): Yeah. And as you aptly pointed out, that was in, that was five years ago. And then it took like, oh, five years for it to become in the public domain of Chat GPT. So what was going on in the background? Melanie Mitchell (02:49): Well, you know, the idea of language models (LLMs) that is neural network language models that learn by trying to predict the next word in a, in a text had been around for a long time. You know, we now have GPT-4, which is what's underlying at least some of ChatGPT, but there was GPT-1 and GPT-2, you probably remember that. And all of this was going on over those many years. And I think that those of us in the field have seen more of a progression with the increase in abilities of these increasingly large, large language models. that has really been an evolution. But I think the general public didn't have access to them and ChatGPT was the first one that like, was generally available, and that's why it sort of seemed to appear out of nothing. SPARKS OF ARTIFICIAL GENERAL INTELLIGENCE Sentience vs Intelligence Eric Topol (03:50): Alright. So it was kind of the, the inside world of the computer science kinda saw a more natural progression, but people were not knowing that LLMs were on the move. They  were kinda stunned that, oh, look at these conversations I can have and how, how humanoid it seemed. Yeah. And you'll recall there was a fairly well-publicized event where a Google employee back I think last fall was, put on suspension, ultimately left Google because he felt that the AI was sentient. Maybe you'd want to comment that because that's kind of a precursor to some of the other things we're going to discuss, Melanie Mitchell (04:35): Right? So yeah, so one of the engineers who was working with their version of ChatGPT, which I think at the time was called LaMDA was having conversations with it and came to the conclusion that it was sentient, whatever that means, , you know, that, that it was aware that it had feelings that it experienced emotions and all of that. He was so worried about this and he wanted, you know, I think he made it public by releasing some transcripts of his conversations with it. And I don't think he was allowed to do that under his Google contract, and that was the issue.  tThat made a lot of news and Google pushed back and said, no, no, of course it's not sentient. and then there was a lot of debate in the philosophy sphere of what sentient actually means, how you would know if something is sentient. And it Yeah. and it's kind of gone from there. Eric Topol (05:43): Yeah. And then what was interesting is then in March based upon GPT-4 the Microsoft Research Group published this sparks paper where they said, it seems like it has some artificial general intelligence, AGI qualities, kind of making the same claim to some extent. Right? Melanie Mitchell (06:05): Well, that's a good question. I mean, you know, intelligence is one thing, sentience is another. There's a question of whether, you know, how they're related, right? Or if they're related at all, you know, and what they all actually mean. And these terms, this is one of the problems. Of course, these terms are not well-defined, but most, I think most people in AI would say that intelligence and sentience are different. You know something can be intelligent or act intelligently without having any sort of awareness or sense of self or, you know, feelings or whatever sentience might mean. So I think that the sparks of AGI paper from Microsoft was more about this, that saying that they thought GPT-4 four, the system they were experimenting with, showed some kind of generality in its ability to deal with different kinds of tasks. You know, and this, this contrasts with the old, older fashioned ai, which typically was narrow only, could do one task, you know, could play chess, could play Go, could do speech recognition, or could, you know, generate translations. But it, they couldn't do all of those things. And now we have these language models, which seemed to have some degree of generality. The Persistent Gap Between Humans and LLMs Eric Topol (07:33): Now that gets us perfectly to an important Nature feature last week which was called the “Easy Intelligence Test that AI chatbots fail.” And it made reference to an important study you did. First, I guess the term ARC --Abstract and Reasoning Corpus, I guess that was introduced a few years back by Francois Chollet. And then you did a ConceptARC test. So maybe you can tell us about this, because that seemed to have a pretty substantial gap between humans and GPT-4. Melanie Mitchell (08:16): Right? So, so, so Francois Chollet is a researcher at Google who put together this set of sort of intelligence test like puzzles visual reasoning puzzles that tested for abstraction abilities or analogy abilities. And he put it out there as a challenge. A whole bunch of people participated in a competition to get AI programs to solve the problems, and none of them were very successful. And so what, what our group did was we thought that, that the original challenge was fantastic, but the prob one of the problems was it was too hard, it was even hard for people. And also it didn't really systematically explore concepts, whether a, a system understood a particular concept. So, as an example, think about, you know, the concept of two things being the same, or two things being different. Okay? (09:25): So I can show you two things and say, are these the same or are they different? Well, it turns out that's actually a very subtle question. 'cause when we, you know, when we say the same we, we can mean sort of the, the same the same size, the same shape, the same color, this, you know, and there's all kinds of attributes in which things can be the same. And so what our system did was it took concepts like same versus different. And it tried to create lots of different challenges, puzzles that had that required understanding of that concept. So these are very basic spatial and semantic concepts that were similar to the ones that Solei had proposed, but much more systematic. 'cause you know, this is one of the big issues in evaluating AI systems is that people evaluate them on particular problems. (10:24): For example, you know, I think a lot of people know that ChatGPT was able to answer many questions from the bar exam. But if you take like a single question from the bar exam and think about what concept it's testing, it may be that ChatGPT could answer that particular question, but it can't answer variations that has the same concept. So we tried to take inside of this arc domain abstraction and reasoning corpus domain, look at particular concepts and say, systematically can the system understand different variations of the same concept? And then we tested this, these problems on humans. We tested them on the programs that were designed to solve the ARC challenges, and we tested them on G P T four, and we found that humans way outperformed all the machines. But there's a caveat, though, is that these are visual puzzles, and we're giving them to GPT-4, which is a language model, a text, right? Right. System. Now, GPT four has been trained on images, but we're not using the system that can deal with images. 'cause that hasn't been released yet. So we're giving the system our problems in a text-based format rather than like, like giving it to humans who actually can see the pictures. So this, this can make a difference. I would say our, our our, our results are, are preliminary . Eric Topol (11:57): Wel

Transcript with some hyperlinks Eric Topol (00:00): Hello, Eric Topol here. And what a privilege to have as my guest Al Gore, as we discuss things that are considered existential threats. And that includes not just climate change but also recently the concern about A.I. No one has done more on the planet to bring to the fore the concerns about climate change. And many people think that the 2006 film, An Inconvenient Truth, was the beginning, but it goes way back into the 1980s. So, Al it's really great to have you put in perspective. Here we are with the what's going on in Canada with more than 12 million acres of forest fires that are obviously affecting us greatly, no less the surface temperature of the oceans. And so many other signs of this climate change that you had warned us about decades ago are now accelerating. So maybe we could start off out, where are we with climate change and the climate reality? The Good News on Climate Change Al Gore (01:00): Oh, well, first of all, thank you so much for inviting me to be on your podcast again, Eric. It's always a pleasure and especially because you're the host and we, we have very interesting conversations that aren't on the podcast. So, , I'm looking forward to this one. So, to start with climate you know, the old cliche, there's good news and bad news. Unfortunately, there's an abundance of bad news but there's also an awful lot of good news. Let me start with that first and then turn to the more worrying trends. We have seen the passage in the US last August of the largest and most effective best funded climate legislation passed by any nation in all of history. The so-called Inflation Reduction Act is an extraordinary piece of legislation. (01:55):  It's billed as allocating $369 billion to climate solutions. But actually, the heavy lifting in that legislation is done by tax credits, most of which are open-ended and uncapped, and a few without any time limits, most a 10-year duration. And the enthusiastic response to the legislation after President Biden signed it has now made it clear that that early estimate of 369 billion is a low-ball estimate, because Goldman Sachs, for example, is predicting that it will end up allocating 1.2 trillion to climate solutions. A lot of other investors and others using economic models are estimating more than a trillion. So, it's really a fantastic piece of legislation and other nations are beginning to react and respond and copy it. One month after that law was passed the voters of Australia threw out their climate denying government and replaced it with a climate-friendly government, which immediately then set about passing legislation that adopts the same goals as the US IRA and the Australian context. (03:19): And they stopped the biggest new coal mine there. And anyway, one month after that, in October, the voters of Brazil threw out their former president often called the “Trump of the Tropics” and replaced him with a new president, a former president who's a new president, who has pledged to protect the Amazon and the European Union in responding to the evil, evil and cruel invasion of Ukraine by Russia. And the attempted blackmail of nations in Europe, dependent on Russian gas and oil responded not by bending their knee to Vladimir Putin, but by saying, wait a minute, this makes renewable energy, freedom, energy. And so they accelerated their transition. And so these are all excellent signs and qualifies as good news. The other good news is not all that new, but it's still continuing to improve. (04:28): And that is the astonishing reductions in cost for electricity produced by solar and wind, and the reductions in cost for energy storage, principally in batteries and electric vehicles and a hundred other less well known technologies that are extremely important. We're in the midst of early stages of a sustainability revolution that has the magnitude of the industrial revolution, coupled with the speed of the digital revolution. And we're seeing it all over the place. It’s really quite heartening. One quick example last, the, the biggest single source of global warming pollution is the generation of electricity with gas and coal. Well, last year, if you look at all the new electricity generation capacity installed worldwide 90% of it was renewable. In India, 93% was solar and wind. And India's pledged not to give permits for any new coal burning plants for at least five years, which means never, probably because this cost reduction curve, as I mentioned, is still continuing downward electric vehicles, we're now seeing that the purchases have reached 15% of the market globally. (05:56): Norway's already at 50%. They've actually outlawed the sale of any new internal combustion engines. And indeed, many national and even municipal and state jurisdictions have prospectively served notice that they, you won't be able to buy them after a certain day, 2030,  in many cases and the auto companies and truck and bus companies have long since diverted their research money all their R & D is going into EVs now. And that's the second largest source of global warming pollution. I could go through the others, but I want, I'll just tell you that there is a lot of good news. And the Bad News Now, the bad news is we're still seeing the crisis get worse, faster than we're deploying all of these solutions. And, the inertia in our political and economic systems is partly a direct result of huge amounts of lobbying and campaign contributions and the century old net of political and economic influence built up by the fossil fuel industry. (07:18): And they're opposing every single solution at the state level, the local level, the national level, the international level. Now, this COP 28 [the 2023 United Nations Climate Change Conference] coming up at the end of the year in the United Arab Emirates is actually chaired by an oil and gas company CEO-- It's preposterous. And they already have in the last two COPS, more lobbyists registered as participants than all than the five or six largest national delegations combined. And we're seeing them really oppose this change. And meanwhile, the manifestations of the crisis are steadily worsening. You mentioned the fires in Canada that are predicted to burn all summer long. And I was in New York City last week, and you, you know, from the news stories it, it was horrific. I got there the day after the worst day, oh my God. (08:21): But I saw and heard from people just the tremendous problems that people have. It's also going on in Siberia, by the way, and these places that are typically beyond the reach of TV crews and networks that don't capture our attention unless something happens to blow the smoke to where we live. And that's what's happened here. But there are many other extremely worrying manifestations that aren't getting much attention. I do think we're going to solve this, Eric. I'm very optimistic, but the question is whether we will solve it in time. We are what's the right way to say this? We're tiptoeing through a minefield with tripwires and toward the edge of a cliff. I don't want to torture the metaphor, but actually there are several extremely dangerous threats to ecological systems that are in a state of balance now, and are being pushed out of their equilibrium state into a different format. (09:35): The ocean currents--we're already seeing it with the jet stream in the northern hemisphere. You may have seen on the weather maps. They're now using these a lot where it's getting loopier and more disorganized. That's what the last few winners has, has pulled these big loops, have pulled arctic air down into areas far south in the US and in other regions, by the way. And it’s making a lot of the extreme events worse. Now, we're entering an El Nino phase in the Pacific Ocean comes around every so often, and this one is predicted to be a strong one, and that's going to accentuate the temperature increase. You know, it was [recently] 110 degrees last week in Puerto Rico, 111 degrees in several countries in Southeast Asia. (10:31): Last summer, China had a heat wave that the historians say about, which the historians say there's nothing even minimally comparable in all prior known, and the length, the extent, the duration, the intensity. And we saw monsoons lead to much of Pakistan underwater for an extended period of time. I could go on, but the net and balance out the good news and the bad news we are gaining momentum. And soon we are going to be gaining on the crisis itself and start deploying solutions faster than it's getting worse. So I remain optimistic, and I always remind people, if you doubt we have the political will to see this through, remember that political will is itself a renewable resource. The Intersection of A.I. and Climate Change Eric Topol (11:27): Yeah, that's a great optimistic point, and we sure appreciate that, because it's pretty scary to see these trends that you reviewed. Now, as you know recently there was a large group of AI scientists this one led by Sam Altman of OpenAI, who put out a statement, a one-sentence statement, and it said, “Mitigating the risk of distinction from ai, which you and are enthusiastic about, should be a global priority alongside other societal scale risks, such as pandemics and nuclear war.” Well, obviously, also climate change. So how do you see the AI intersection of climate change? Because as you well know, GPT-4, having pre-trained with some 30,000 graphic processing units [GPUs], the issues about consumption of energy carbon emissions, the need for water cooling, is AI going to make this situation worse, or will it make it better? Al Gore (12:33): Well, yeah. You know, I understand. Well, both would be my answer. And we don't have enough data yet to really know for sure which way it will tip. Maybe we'll talk about the existential risks from generative AI. As this conversation continues, there are many wh

TRANSCRIPT Eric Topol (00:00): Hello, this is Eric Topol, and it's really a delight for me to welcome Hannah Davis who was the primary author of our recent review on Long Covid and is a co-founder of the Patient-Led Research Collaborative. And we're going to get into some really important topics about citizen science, Long Covid and related matters. So, Hannah, welcome. Hannah Davis (00:27): Thank you so much for having me. Eric Topol (00:29): Well, Hannah, before we get into it I thought because you had a very interesting background before you got into the patient led research collaborative organization with graphics and AI and data science. Maybe you could tell us a bit about that. Hannah Davis (00:45): Sure. Yeah. Before I got sick, I was working in machine learning with a particular focus on generative models for art and music. so I did some projects like translating data sets of landscapes into emotional landscapes. I did a project called The Laughing Room, where there was a room and you went in and the room would listen to you and laugh if it thought you said something funny, . and then I did a lot of generative music based on sentiment. So I, I did a big project where I was generating music from the sentiment of novels and a lot of kind of like critical projects, looking at biases in data sets, and also curating data sets to create desired outcomes in these generative models. Eric Topol (01:30): So, I mean, in a way again, you were ahead of your time because that was before ChatGPT in November last year, and you were ahead of the generative AI curve. And here again, you're way ahead in in the citizen science era as it particularly relates to the pandemic. So, I, I wonder if you could just tell us a bit I think it was back, we go back to March, 2020. Is that when you were hit with Covid? Hannah Davis (01:59): Yes. Eric Topol (02:00): And when did you realize that it wasn't just an acute phase illness? Hannah Davis (02:06):  for me, honestly, I was not worried at all. I, my first symptom was that I couldn't parse a text message. I just couldn't read it, thought I was tired. an hour later, took my temperature, realized I had a fever, so that's when I kind of knew I was sick. but I really just truly believed the narrative I was going to get better. I was 32 at the time. I had no pre-existing conditions. I just was, you know, laying around doing music stuff, not concerned at all. And I put a calendar note to donate plasma two weeks out, and I was like, you know, I'm going to hit that mark. I'm going to donate plasma, contribute, it'll be fine. And that day came and went. I was still, you know, pretty sick with a mild case. You know, I didn't have to be hospitalized. (02:49): I didn't have severe respiratory symptoms. but my neurological symptoms were substantial and did increase kind of over time. And so I, I was getting concerned. Three weeks went by, still wasn't better. And then I read Fiona Lowenstein’s op-ed in the New York Times. They were also very young. They were 26 at the time, they had been hospitalized, and they had this prolonged recovery, which we now know as Long Covid. and they started the Body Politic Support Group joined that saw thousands of people with the same kind of debilitating brain fog, the same complete executive functioning loss, inability to drive, forgetting your family members' names who were all extremely young, who all had mild cases. and that's kind of when I got concerned because I realized, you know, this was not just happening to me. This was happening to so many people, and no one understood what was happening. Eric Topol (03:49): Right. extraordinary. And, and was a precursor, foreshadowing of what was to come. Now, here it is, well over three years later. And you're still affected by all this, right? Hannah Davis (04:02): Yes. Pretty severely. Eric Topol (04:04): Yeah. And I learned about that when I had the chance to work with you on the review. You were the main driver of this review, and I remember asking you, because I, I didn't know anyone in the world that was tracking Long Covid like you and to be the primary author. And then you sent this outline, and I had never seen an outline in all my years in academic medicine. I never saw an outline like this of the review. I said, oh my God, this is incredible. So I know that during that time when we worked on the review together, along with Lisa McCorkell and Julia Moore Vogel, that, you know, there, there were times when you couldn't work on it right there, there were just absolutely, you would have some good days or bad days. And, and that's the kind of, is that kind of the way is, how it goes in any given unit time? Hannah Davis (04:55): I think generally, I, I communicated as like 40% of my function is gone. So, like, I used to be able to have very, very full days, 12 hour days would work, would socialize, would do music, whatever. you know, I, I have solidly four functional hours a day. on a good day, maybe that will be six. On a bad day, that's zero. And when I push myself by accident, I can get into a crash that can be three to seven days easily. Hmm. and then I'm, then I'm just not, you know, able to be present. I don't feel here. I don't feel cognitively able, I can't drive. And then I'm just completely out of the world for a bit of time. Eric Topol (05:35): Yeah. Wow. So back in the early days of when you were first got sick and realized that this was not going to just go away, you worked with others to form this Patient -Led Research Collaborative organization, and here you are, you didn't have a medical background. You certainly had a data science and computing backgrounds. But what were your thoughts? I mean, citizen science has taken on more of a life in recent years, certainly in the last decade. And here there's a group of you that are kind of been leading the charge. we'll get to, you know, working with RECOVER and NIH in just a moment. But what were your thoughts as to whether this could have an impact at working with these, the other co-founders? Hannah Davis (06:27): I think at first we really didn't realize how much of an impact we were going to have. The reason we started collecting data in the first place really was to get answers for ourselves as patients. You know, we saw all these kind of anecdotes happening in the support group. We wanted to get a sense of which were happening the most at what frequency, et cetera. and it really wasn't until after that when like the CDC and WHO started reaching out, asking for that data, which was gray literature at the time that we kind of realized we needed to formalize this and, and put out an official paper which was what ended up being the second paper. But the group that we formed really is magical, I think like, because the primary motivator to join the group was being sick and wanting to understand what was happening. And because everyone in the group only has the kind of shared experience of, of living with Long Covid, we ended up with a very, very diverse group. Many, many different and I think that really contributed to our success in both creating this data, but also communicating and, and doing actionable policy and advocacy work with it. Eric Topol (07:42): Did you know the folks before? Or did you all come together because of digital synapses? Hannah Davis (07:47): Digital synapses? I love that. Absolutely. No, we didn't know each other at all. they're now all, you know, they're my best friends by far. you know, we've been through this, this huge thing together. but no, we didn't meet in person until just last September, actually. And many of them we still haven't even met in person. which makes it even more magical to me. Eric Topol (08:13): Well, that's actually pretty extraordinary. So together you've built a formidable force to stand up for the millions and millions of people. As you wrote in the review, 65 million people around the world who are suffering in one way or another from Long Covid. So just to comment about the review --you know, I've been working in writing papers for too long, 35 years. I've never, in my entire career, over 1300 peer reviewed papers on varied topics, ever had one that's already had 900,000 downloads, is the fourth most cited paper and Altmetric since published the same timeframe in January of all 500,000 peer-reviewed papers. Did you ever think that the, the work that, that you did and our, you know, along with Lisa and doing, and I would ever have this type of level of interest? Hannah Davis (09:16): No, and honestly, it's so encouraging. Our, our second paper to me did very well. and, you know, was, was widely viewed and widely cited, and this one just surpassed that by miles. And I think that it's encouraging because it communicates that, that people are interested, right? People, even if they don't understand what long covid is, there is a huge desire to know. And I think that putting this out in this form, focusing on the biomedical side of things really gives people a, a tool to start to understand it. And from the patient side of things, more than any other paper I've heard we, we get so many comments that are like, oh, I brought this to my doctor and, you know, the course of my care change. Like he believed me and he started X treatment. and that, that's the kind of stuff that just makes us so, so meaningful. and I'm so, so grateful that, that we were able to do this. Eric Topol (10:16): Yeah. And as you aptly put it, you know, a work of love, and it was not easy because the reviewers were not not all of them were supportive about the real impact, the profound impact of long covid. So when you now every day you're keeping track of what's going on in this field, and there's something every single day. one of the things, of course is that we haven't really seen a validated treatment all this time, and you've put together a list of candidates, of course, it was in the review, and it constantly gets

Link to the book: The AI Revolution in Medicine Link to my review of the book Link to the Sparks of Artificial General Intelligence preprint we discussed Link to Peter’s paper on GPT-4 in NEJM Transcript (with a few highlights in bold of many parts that could be bolded!) Eric Topol (00:00): Hello, I'm Eric Topol, and I'm really delighted to have with me Peter Lee, who's the director of Microsoft Research and who is the author, along with a couple of colleagues for an incredible book called The AI Revolution in Medicine, GPT-4 and Beyond. Welcome, Peter. Peter Lee (00:20): Hello Eric. And thanks so much for having me on. This is a real honor to be here. Eric Topol (00:24): Well, I think you are in the enviable position of having spent now more than seven months looking at GPT-4’s S capability, particularly in the health and medicine space. And it was great that you recorded that in a book for everyone else to learn because you had such a nice head start.  I guess what I wanted to start with is, I mean, it's, it's a phenomenal book. I [holding the book up], this prop. I can't resist Peter Lee (00:52): Eric Topol (00:53): When, when I got it, I, I couldn't, I stayed up most of the night because I couldn't put it down. It was, it is so engrossing. But when you, when you first got your hands on this and started testing it, what were, what were your initial thoughts? Peter Lee (01:09): Yeah. I, let me first start by saying thank you for the nice words about the book, but really, so much of the credit goes to the co-authors, Carey Goldberg and Zach Kohane and Corey in particular took my overly academic writing. I suspect you have the same kind of writing style as well as Zach's pretty academic writing and helped turn it into something that would be approachable to non-computer scientists and as she put it, as much as possible as a page turner. So I'm glad that her work helped make the, the book an easy read. I, Eric Topol (01:54): I want to just say you're very humble because the first three chapters that you wrote yourself were clearly the, the best ones for me. Anyway. I don't mean to interrupt, but it, it, it is an exceptional book, really. Peter Lee (02:06): Oh thank you very much. It means a lot. Hearing that from you. You know, my own view is that the, the best writing and the best analyses and the best ideas for applications or not of this type of technology in medicine are yet to come. But you're right that I did benefit from this seven-month head start. And so, you know, I think the timing is, is very good. but I'm hoping that much better books and much better writings and ideas will come, you know, when you start with something like this, I, I suspect, Eric, you had the same thing. you start off with a lot of skepticism and I, in fact, I sort of now made light with this. I talk about the nine stages of grief that you have to go through. (02:55):  I was extremely skeptical. Of course, I was very aware of GPT 2, GPT 3 and GPT 3.5. I understand, you know, what goes into those models really deeply. and so some of the claims, when I was exposed to the early development, GPT-4 just seemed outlandish and impossible. So I, I was, you know, skeptical, somewhat quietly skeptical. We've all been around the block before and, you know, we've heard lots of AI claims and I was in that state for maybe more than two weeks. And then I started to become in that two weeks annoyed, because I know that some of my colleagues like falling into what I felt was the trap of getting fooled by this technology. And then that turned into frustration and fear. I actually got angry. And one colleague who I won't name I've since had to apologize because then I into the phase of amazement because you start to encounter things that you can't explain that this thing seems to be doing that turns into joy. (04:04):  I remember the exhilaration of thinking, wow, I did not think I would live long enough to see a technology like this. and then intensity, There was a period of about three days when I didn't sleep, I was just experimenting. Then you run into some limits and some areas of puzzlement and that's a phase of chagrin. And then real dangerous missteps and mistakes that this system can make that you realize might end up really hurting people. and then, you know, ChatGPT gets released and to our surprise it catches fire with people. And we learn directly through communications that some clinicians are using it in clinical settings. And that heightens the concern. And I, I can't say I'm in the ninth stage of enlightenment yet, but you do become very committed to wanting to help the medical community get up to speed and to be in a position to take ownership of the question of whether, when, and how a technology like this should be used. and that was really the motivating force behind the book. And it, it was really that journey. And that journey also has given me patience with everyone else in the world, because I realize everyone else in the world has to go through those same nine, nine stages. Eric Topol (05:35): Well, those stages that you went through are actually a great way to articulate this pluripotent technology. I mean, I think you, you touched on that chat. ChatGPT was released November 30th and within 90 days had a billion distinct users, which is beyond anything in history. And then of course, this transcended that quite a bit as you showed in the book coming out in you know, just a very short time in March. right. And I think a lot of people want access to GPT-4 because they know that there is this jump in its capabilities. But the book starts off after Sam Altman's forward, which was also nice because he said, you know, this is just an early, as you pointed out there, there's a lot more to come in the large language model space. (06:30): But the grabber to me was this futuristic, this second year medical resident who's using an app on the phone to get to the latest GPT to help manage her patient, and then all the other things that it's doing to check on her patients and do all the things that are the tasks that clinicians don't really want to do, that they need help with. And that just grabs you as to the futuristic potential, which may not be so far away. And I think then you get into the nuts and bolts, but one of the things that I think is a misnomer that you really nailed is how you say it isn't just that it generates, but it really is great at editing and analyzing. And here it's, it's called generative AI. Can you, can you expound on that? And it's unbelievable conversationalist capability. Peter Lee (07:23): Yeah. you know, the term Generative AI, I tried for a while to push back on this, but I think it's just caught on and I've given up on that. And I get it. You know, I, I think especially with ChatGPT it's of course reasonable for the public to be, you know infatuated with a thing that can write love letters, write poetry and that generative capability. and of course, you know school children writing their essays and so on this way. But as you say one thing we have discovered through a lot of experimentation is it's actually somewhat of a marginal generator of text. I would not say at all. That is, it is not as good a poet as good human poets. It's not the, you know, people have programmed GPT-4 to try to write whole novels and it can do that, (08:24): they aren't great. and it's a challenge, you know within Microsoft, our Nuance division has been integrating GPT-4 to help write clinical and encounter notes. and you can tell it's just hitting at the very limits of the capabilities in and of the intelligence of GPT-4 to be able to do that well. But one area where it really excels is in evaluating or judging or reviewing things. And we've seen that over and over again. in chapter three. You know, I have this example of its analysis of some contemporary poetry which is just stunning in its kind of insights and its use of metaphor and allegory. And but then in other situations in interactions with the New England Journal Journal of Medicine experimentations with the use of GPT-4 as an adjunct to the review process for papers it is just incredibly insightful in spotting inconsistencies missing citations to precursor studies to understanding lack of inclusivity and diversity, you know, in approach or in terminology. (09:49): And these sorts of review things end up being especially intriguing for me when we think about the whole problem of medical errors and the possibility of using GPT-4 to look over the work of doctors, of nurses of insurance, adjudicators and others, just as a second set of eyes to check for errors check for kind of missing possibilities if there's a differential diagnosis. Is there a possibility that's been something that's been missed? If there's a calculation for an IV medication administration, well, it's a calculation done correctly or not. And it's in those types of applications of GPT-4 as a reviewer, as a second set of eyes that I think I've been especially impressed with. And we try to highlight that in the book. Eric Topol (10:43): Yeah. That's one of the very illuminating things about going well beyond what are the assumed utilities in a little bit, we'll talk about the liabilities, but certainly these are functions part of that flurry potent spectrum that I think a lot of people are not aware of. One, particularly of interest in the medical space is something I had not anticipated as, you know, when I wrote a Deep Medicine chapter, “Deep Empathy,” I said, well, we got to rely totally on humans for that. But here you had examples that were quite stunning of coaching physicians by going through their communication, their note and saying, you know, you could have been more sensitive with this. You could have done this, but you, you could be more empathic. And as you know, since the book was published, there was an interesting study that compared a couple hundred questions direct

Transcript Eric (00:00): Okay. Hello, this is Eric Topol and this is a rare privilege for me to interview my favorite epidemiologist, Dr. Michael Osterholm. He is the Regents Professor of the University of Minnesota. He's director of CIDRAP, which is certainly one of the leading entities around the world for public health. And, we've been friends for the last few years, which we'll we'll talk about. So, welcome Michael. Such a great privilege to have you today. Michael (00:31): Well, thank you, the honor, really is mine. As I have shared with you and others know very well--you have been a real mentor to me and many others during this pandemic. And, I could never repay you adequately for all that you've helped teach me throughout these last three years. It's been immeasurable. Eric (00:49): No, if you're too kind, I think it's much different. The opposite way. I've learned so much from you because this isn't my area, as you well know. I thought we'd start with, of course, right now things are relatively good for the pandemic in the United States and mostly around the world, with relatively less cases, less hospitalizations and deaths. But obviously still people are getting infected. And maybe you can tell us about the recent case that you went through that would be enlightening. End of the Pandemic? Michael (01:28): Yeah, I think we're all trying to understand when the pandemic ends. And, as we've discussed many times before, we'll probably know that about a year after it ends, then we'll say, yep, that was the end of it. Don’t for a moment think that at the end means that there won't be cases. You know, for every infectious agent that we think of when causing a pandemic, they still come back, whether it be influenza, or potentially coronaviruses. They will, they will continue to circulate. It's a matter of how many cases occur, how many people die. And I think that's an important point. There isn't really a definition for when a pandemic ends. It's, I guess it's just when you feel like it's over. And clearly the world has come to that conclusion already. You don't need a, an epidemiologist or a politician to tell 'em that the pandemic's over that they feel that we're still seeing about 165 deaths a day in this country from Covid. (02:24): So it's hardly gone away completely. But we do have to acknowledge it. Most of those deaths are older individuals, people who have not been vaccinated recently with bivalent boosters. And in that regard, we could surely even reduce the illnesses further. I don't have any faith right now in the surveillance systems that have been set up to look at cases around the world. We've pretty much dismantled that. We are not testing people that we results in reports being made to public health agencies, whether in this country or anywhere else in the world. So I really look at two other things. One is deaths. And even they're realizing that still is a challenge in terms of how complete death reporting is due to covid. But then the other thing we're looking at, which has been really, you might say, public health revolution during the pandemic, and I say revolution cause it's really changed things. (03:19): And that is the issue of wastewater surveillance. And we've been able to ascertain in many areas of the world, in fact, with using wastewater surveillance, a much better sense of how much virus is in the community. And so, just in following with your very thoughtful comment about case numbers dropping, that's exactly what we're seeing in most locations in this country too. We, for example, here in the Minneapolis St. Paul area, have seen a dramatic decrease in wastewater activity in the last two months. So I think we're in a place right now where I can hope it'll only get better. On the other hand, you know, I have a lot of respect for this virus, and frankly, we all ought to have a lot of humility. We don't know if another variant will emerge that with, given how much immunity we have in our population will somehow break through that and cause increase in surgeon cases or whether this will become kind of the norm and we'll see less and less. On Getting Covid (04:16): Now, you asked me about my case. Yeah. I have to say that, I speak about this with, with really some trepidation in the sense that I was not gonna get this. I had and very faithful throughout the course of the pandemic, where in my N 95 respirator when I went out and about, I had been fit tested. In addition, when we finally did socialize in our home, we had a, what became affectionately known as the Osterholm Home Rule. You could not have had known contact with someone of the, with Covid in the five previous days. You could have no symptoms yourself on the day of, and you had to test negative bilateral flow test within three to four hours of coming. And we would entertain small four, the six party, parties, and it was going wonderful. (05:07): And then on March 10th, the night of March 10th, a colleague from work came over with Fern and myself. Three of us had dinner. We went down our elevator in our building here, which were 31 stories up. No one else is in the elevator. And then we proceeded to go to a very small music venue where we wore N95s. We were some distance from any other people, and we were there for an hour and 45 minutes. And, literally two days later, almost 48 hours later, all three of us developed symptoms. None of us converted for another 24 hours. And then at that point, we all three tested lateral flow positive.. We all three took Paxlovid. I took it and was starting to feel better after that fifth day. (05:59): And then I kind of crashed and at that point, I got a second, , five day course of Paxlovid and started to feel better. And, I'm you know, was very happy to have this behind me. However, over the course of the last 10 days, I have really had significant fatigue. You know I'm not one that sleeps a lot But, I can tell you there are multiple times in a day where I'm doing something like even doing what I'm doing right now where I just feel like I just need to fall asleep. It’s been really a challenge. The other thing that happened, which was in retrospect a little bit more concerning than I realized at the time, there was a period at about day 10 to 14 into my illness, I started losing my memory on many, many things of, you know, importance. (06:53): I couldn't, for example, tell you what was that drink that is: a champagne, orange juice combination. I couldn't find the word mimosa if my life depended on it. If somebody asked me who was in sleepless in Seattle, I had to think about now the movie who was in it. I couldn't remember. And I mean, in retrospect, I wasn't that concerned thinking, ah, it's not that bad. And it was actually quite remarkable. This lasted about two and a half, three weeks. And now I think, I think at least according to those around me, I have gained most of my memory back. But now I have the fatigue picture. So, as much as I don't know where I picked up the virus, all three of us picked it up. And as much asI feel like I have survivor's guilt right now in the sense that, you know, I'm not that concerned about getting infected in a public exposure given I probably have some pretty good protection, at least for a few more weeks. But nonetheless,  I think this potential fatigue issue is really a challenge. Eric (07:52): Yeah. The things that you're bringing up with this, like for example, I know you had had, the initial series and three boosters including the bivalent. Was that sometime in September last year, or, Michael (08:04): Yeah, it was seven and a half months before, Eric (08:07): Yeah. So, Michael (08:07): So, so that was, and I tried to get it at six months in the second. But in Minnesota we actually have a registry. And so it's not just your white card that, you know, you could do it. And it wasn't, I was trying to do something illegal, but you know, this vaccine's just sitting there. So I tried to get another bivalent at six months post my first one, and of course I was turned down. And then, five weeks after that I got covid. Eric (08:33): Yeah. And, and then of course, just recently the FDA and CDC finally came to the conclusion that for people of our age group and immunocompromised, they certainly have the option that you've advocated for. And unfortunately, you weren't able to get at that time. Although I suspect the protection, you might comment on that, Mike, that there is some protection infection for the first few months after a booster. Michael (09:00): Yeah. Yeah, absolutely. I mean, I think the studies that we've seen so far, at least, and particularly from those from other countries where they have remarkable follow up on databases, there is some initial evidence of protection in those first weeks against getting infected and even potential transmission. But that wanes unfortunately, quickly, and it's likely B-cell related immunity. And then I think as we all, at least believe the T-cell immunity, which we're still all trying to understand and characterize, probably kicks in and gives us protection against serious illness, hospitalizations and deaths. But as you and I have looked at even then, at six months out, you start to see some potential waning of that. And I think that's why we have a real challenge right now. I've said many, many times, we can't boost our way out of this pandemic. And I meant that not because some of us wouldn't be willing to get a vaccine every six months, but the vast majority of the population would not. And we've even seen here with the first bivalent booster dose, which we know has provided good protection against serial serious illness, hospitalizations, and deaths. Look at the very small proportion of the [age 65+] population that have taken that less than 40%. So  it's a challenge that how do we get people to keep getting vaccinated? A lot of people say, I'm done. I'm, I'm done with it. Eric (10:22): Right,