Family of large biological molecules
POPULARITY
Categories
112 episodes with RNA
67 episodes with RNA
76 episodes with RNA
31 episodes with RNA
44 episodes with RNA
34 episodes with RNA
33 episodes with RNA
142 episodes with RNA
27 episodes with RNA
47 episodes with RNA
21 episodes with RNA
16 episodes with RNA
15 episodes with RNA
20 episodes with RNA
25 episodes with RNA
12 episodes with RNA
12 episodes with RNA
10 episodes with RNA
11 episodes with RNA
36 episodes with RNA
13 episodes with RNA
9 episodes with RNA
7 episodes with RNA
16 episodes with RNA
11 episodes with RNA
6 episodes with RNA
13 episodes with RNA
7 episodes with RNA
16 episodes with RNA
8 episodes with RNA
7 episodes with RNA
9 episodes with RNA
11 episodes with RNA
15 episodes with RNA
11 episodes with RNA
7 episodes with RNA
10 episodes with RNA
13 episodes with RNA
6 episodes with RNA
28 episodes with RNA
8 episodes with RNA
12 episodes with RNA
12 episodes with RNA
8 episodes with RNA
16 episodes with RNA
6 episodes with RNA
14 episodes with RNA
18 episodes with RNA
9 episodes with RNA
13 episodes with RNA
12 episodes with RNA
162 episodes with RNA
7 episodes with RNA
5 episodes with RNA
4 episodes with RNA
7 episodes with RNA
12 episodes with RNA
150 episodes with RNA
11 episodes with RNA
5 episodes with RNA
5 episodes with RNA
14 episodes with RNA
7 episodes with RNA
8 episodes with RNA
16 episodes with RNA
4 episodes with RNA
6 episodes with RNA
7 episodes with RNA
6 episodes with RNA
4 episodes with RNA
7 episodes with RNA
9 episodes with RNA
8 episodes with RNA
5 episodes with RNA
12 episodes with RNA
3 episodes with RNA
4 episodes with RNA
3 episodes with RNA
10 episodes with RNA
15 episodes with RNA
7 episodes with RNA
5 episodes with RNA
4 episodes with RNA
7 episodes with RNA
6 episodes with RNA
3 episodes with RNA
7 episodes with RNA
6 episodes with RNA
3 episodes with RNA
12 episodes with RNA
11 episodes with RNA
8 episodes with RNA
10 episodes with RNA
10 episodes with RNA
9 episodes with RNA
6 episodes with RNA
5 episodes with RNA
6 episodes with RNA
19 episodes with RNA
4 episodes with RNA
4 episodes with RNA
14 episodes with RNA
3 episodes with RNA
5 episodes with RNA
7 episodes with RNA
4 episodes with RNA
6 episodes with RNA
3 episodes with RNA
5 episodes with RNA
4 episodes with RNA
4 episodes with RNA
5 episodes with RNA
3 episodes with RNA
5 episodes with RNA
5 episodes with RNA
5 episodes with RNA
4 episodes with RNA
6 episodes with RNA
5 episodes with RNA
5 episodes with RNA
5 episodes with RNA
3 episodes with RNA
5 episodes with RNA
7 episodes with RNA
4 episodes with RNA
3 episodes with RNA
12 episodes with RNA
8 episodes with RNA
6 episodes with RNA
3 episodes with RNA
5 episodes with RNA
4 episodes with RNA
3 episodes with RNA
5 episodes with RNA
7 episodes with RNA
2 episodes with RNA
5 episodes with RNA
4 episodes with RNA
3 episodes with RNA
4 episodes with RNA
4 episodes with RNA
4 episodes with RNA
4 episodes with RNA
3 episodes with RNA
7 episodes with RNA
7 episodes with RNA
4 episodes with RNA
4 episodes with RNA
3 episodes with RNA
3 episodes with RNA
3 episodes with RNA
3 episodes with RNA
3 episodes with RNA
4 episodes with RNA
8 episodes with RNA
3 episodes with RNA
3 episodes with RNA
4 episodes with RNA
2 episodes with RNA
3 episodes with RNA
2 episodes with RNA
4 episodes with RNA
5 episodes with RNA
3 episodes with RNA
5 episodes with RNA
4 episodes with RNA
3 episodes with RNA
3 episodes with RNA
2 episodes with RNA
3 episodes with RNA
3 episodes with RNA
5 episodes with RNA
5 episodes with RNA
3 episodes with RNA
2 episodes with RNA
2 episodes with RNA
3 episodes with RNA
2 episodes with RNA
9 episodes with RNA
4 episodes with RNA
4 episodes with RNA
4 episodes with RNA
2 episodes with RNA
6 episodes with RNA
3 episodes with RNA
6 episodes with RNA
3 episodes with RNA
2 episodes with RNA
2 episodes with RNA
Most doctors treat the average patient. But you are not average, and this episode gives you the precision medicine blueprint to treat yourself like the individual you are, using multi-omic testing, biohacking technology, and longevity science to optimize every layer of your biology. -Watch this episode on YouTube for the full video experience: https://www.youtube.com/@DaveAspreyBPR Host Dave Asprey sits down with Dr. Anil Bajnath, a Board-Certified Family Physician, author of The Longevity Equation, and President and Founder of the American Board of Precision Medicine. He serves as Adjunct Professor at the George Washington University School of Medicine and CEO of the Institute for Human Optimization. Dr. Bajnath is certified through the Institute for Functional Medicine, board certified in anti-aging and regenerative medicine, and is one of the few clinicians actively applying genomics, transcriptomics, proteomics, and epigenetics together in a real clinical practice. Together, Dave and Dr. Bajnath break down why population-based medicine fails individuals, how functional medicine and precision science combine to unlock real human performance, and why your mitochondria sit at the foundation of every longevity strategy worth pursuing. They dig into how AI can help you decode your own inflammasome biology, why biohackers are using “sex drugs” to extend longevity, why vagal nerve stimulation directly suppresses the NLRP3 inflammasome, and which biomarkers like MMP9 and homocysteine mainstream medicine keeps ignoring. They also cover peptides, supplements, the dark side of metformin, microdosing for anti-aging, and why biohacking works best when it's personalized and precise. This is essential listening for anyone serious about longevity, smarter not harder health strategies, metabolism, sleep optimization, brain optimization, functional medicine, and taking full control of their biology. You'll Learn: Why precision medicine outperforms population-based health strategies for human performance How to layer genomics, transcriptomics, and proteomics into one complete biological picture Which longevity biomarkers your doctor is likely ignoring, including MMP9 and homocysteine How vagal nerve stimulation suppresses the NLRP3 inflammasome and drives anti-aging benefits The real story on metformin, peptides, and which supplements actually move the needle How AI can help you understand your own biology and act on it faster Why biohacking precision beats random stacking every time Thank you to our sponsors! • Igniton | Head over to Igniton.com and use code DAVE for an exclusive 15% off your first order. • BEYOND Biohacking Conference 2026 | Register with code DAVE300 for $300 off https://beyondconference.com • Caldera + Lab | Go to https://calderalab.com/DAVE and use code DAVE at checkout for 20% off your first order. • Screenfit | Get your at-home eye training program for 40% off using code DAVE at https://www.screenfit.com/dave. Dave Asprey is a four-time New York Times bestselling author, founder of Bulletproof Coffee, and the father of biohacking. With over 1,000 interviews and 1 million monthly listeners, The Human Upgrade brings you the knowledge to take control of your biology, extend your longevity, and optimize every system in your body and mind. Each episode delivers cutting-edge insights in health, performance, neuroscience, supplements, nutrition, biohacking, emotional intelligence, and conscious living. New episodes are released every Tuesday, Thursday, Friday, and Sunday (BONUS). Dave asks the questions no one else will and gives you real tools to become stronger, smarter, and more resilient. Keywords: precision medicine, biohacking, Dave Asprey Cialis, Anil Bajnath, American Board of Precision Medicine, multi-omics, genomics, transcriptomics, proteomics, epigenetics, NLRP3 inflammasome, vagal nerve stimulation, MMP9, homocysteine, mitochondria, longevity, anti-aging, peptides, BPC-157, metformin, rapamycin, functional medicine, human performance, supplements, EGCG, exposome, nitric oxide, vascular health, metabolism, brain optimization, AI health, biohacking technology, Dave Asprey Sex Drugs Resources: • Learn More About Anil's Work And the Institute For Human Optimization At: https://ifho.org/ • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15 • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 00:00 – Trailer 00:53 – Intro to Precision Medicine 01:58 – Dr. Bajnath's Holistic Health Journey 05:03 – Pharmaceuticals vs. Supplements 07:58 – Peptides and Longevity Molecules 10:34 – Sexual Health and Vitality 13:56 – Vascular Health and Blood Flow 15:14 – Multi-Omics Approach 19:03 – DNA and Genomics 22:17 – Transcriptomics and RNA 24:24 – Proteomics and Inflammation Markers 32:00 – The Human Exposome 34:55 – Key Health Biomarkers 36:58 – Cell Membrane Dynamics 40:28 – Biological Investment Strategy 41:53 – Life Extension Possibilities 48:52 – Bioenergetics and Mitochondria 49:47 – Quantum Medicine and the Future 51:33 – Vagal Nerve Stimulation See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
On Earth living things are everywhere from the deepest ocean depths to the highest mountain tops. On our home planet RNA (Ribonucleic Acid) is a complex essential molecule involved in the process of translating genetic information into the working components of living cells. In a recent paper in the peer reviewed scientific journal Proceedings of the National Academy of Sciences , Dr. Yuta Hirakawa and his team of two coauthors report on their experiments to produce RNA under conditions similar to those which may have occurred in the early history of Earth and Mars.
Can the aging brain still make new neurons? A landmark 2026 Nature study analyzed 355,997 human hippocampal nuclei using single-nucleus RNA sequencing and ATAC sequencing. Neurogenesis persists into adulthood—but chromatin accessibility collapses early in Alzheimer's disease. SuperAgers showed a 2.5-fold increase in immature neurons and a preserved resilience signature. Epigenetics may be the earliest battlefield in cognitive decline.
Most of the questions I've been asked lately have been about my current hormone replacement regimen. So I have dedicated this episode to unpacking what I use transdermally, orally, and topically for hormone replacement therapy. As you might expect, my approach includes the basics, along with a few additional strategies. I also share a topical option for facial skin that Dr. Felice Gersh recently recommended. Stay tuned to find out what I do for hormone replacement therapy. IN THIS EPISODE, YOU WILL LEARN: What you need to optimize first, before even considering adding any additional items Why I might need to increase the dosage of my Dotti transdermal estrogen patch Why I consider estradiol the most potent estrogen our bodies make before menopause Factors that influenced my decision to prioritize estradiol therapy The role of testosterone, beyond libido Why I use progesterone The value of intra-vaginal products Why I use a compounded intra-vaginal product What copper peptides, estriol, DMAE, and hyaluronic acid may do for aging skin Connect with Cynthia Thurlow Follow on X, Instagram & LinkedIn Check out Cynthia's website Submit your questions to support@cynthiathurlow.com Join other like-minded women in a supportive, nurturing community: The Midlife Pause/Cynthia Thurlow Cynthia's Menopause Gut Book is on presale now! Cynthia's Intermittent Fasting Transformation Book The Midlife Pause Supplement Line Research Links Efficacy of Transdermal Estradiol and Micronized Progesterone in the Prevention of Depressive Symptoms in the Menopause Transition: A Randomized Clinical Trial Hormone Replacement Therapy Effects of Ultra–Low-Dose Transdermal Estradiol on Cognition and Health-Related Quality of Life Regenerative and Protective Actions of the GHK-Cu Peptide in the Light of the New Gene Data Treatment of skin aging with topical estrogens SCF-induced airway hyperreactivity is dependent on leukotriene production The role of dimethylaminoethanol in cosmetic dermatology The Missing lnc(RNA) between the pancreatic β-cell and diabetes
Perhaps it's the biggest question science has left to answer, how did life begin? Now, molecular biologists in Cambridge university have discovered tiny molecules of RNA which they say might provide some clues. Science journalist and author Philip Ball explains what we know and whether we'll ever find the origins of life on earth.Professor Michael Wooldridge has given this year's Royal Society's Michael Faraday Prize lecture. He speaks to Tom Whipple about why the AI we have is not what he wanted it to be; rational. And science columnist at the Financial Times Anj Ahuja brings her favourite new science to discuss.To discover more fascinating science content, head to bbc.co.uk, search for BBC Inside Science and follow the links to The Open University. Presenter: Tom Whipple Producer: Kate White, Katie Tomsett, Clare Salisbury and Alex Mansfield Editor: Martin Smith Production Co-ordinator: Jana Bennett-Holesworth
Guest: Dr. Ido Amit is a Principal Investigator and the Eden and Steven Romick Professorial Chair at the Weizmann Institute of Science. His lab is at the forefront of developing and applying cutting-edge single-cell genomics technologies alongside advanced computational approaches. By integrating these innovative tools in both animal models and human studies, his team uncovers the immune regulatory mechanisms and pathways that shape health and disease. Featured Products and Resources: Stay up-to-date with the latest in human immunology news. Download a free wallchart on the production of CAR T cells. The Immunology Science Round Up Modified RNA Prevents Autoimmunity – Researchers show that modified RNA from our own cells naturally blocks TLR7 and TLR8, preventing harmful immune activation. Oncolytic Virus Boosts T Cells – In glioblastoma patients, a single virus treatment helped the immune system attack the tumor. Rewiring the Immune System During Food Scarcity – When food is scarce, stress hormones rebalance the immune system to fight infection while conserving glucose and preserving immune memory. Regulating Bystander T Cells – IL-4 can dial down how strongly memory CD8+ T cells respond to infection without direct antigen stimulation. Image courtesy of Dr. Ido Amit Subscribe to our newsletter! Never miss updates about new episodes. Subscribe
BUFFALO, NY — February 24, 2026 — A new #research paper was #published in Volume 18 of Aging-US on February 10, 2026, titled “Aging-associated mitochondrial circular RNAs.” Led by first author Hyejin Mun from the University of Oklahoma — with corresponding authors Je-Hyun Yoon from the University of Oklahoma and Young-Kook Kim from Chonnam National University Medical School — the study profiles mitochondrial circular RNAs in Peripheral Blood Mononuclear Cells (PBMCs) from young and old human cohorts and probes how mitochondrial circRNAs and the mitochondrial RNA-binding protein GRSF1 relate to mitochondrial metabolism and cellular senescence. Using total RNA sequencing of PBMCs from young and old donors and complementary cell-based experiments, the authors report that a large fraction of circular RNA junctions originates from the mitochondrial genome, with MT-RNR2 producing the most abundant circular junctions. They show that circMT-RNR2 levels are depleted in older cohorts and in replicative senescence of human fibroblasts, and that the mitochondria-localized RNA-binding protein GRSF1 interacts with both linear and circular MT-RNR2. Loss of GRSF1 reduced circMT-RNR2 levels, decreased mitochondrial TCA intermediates (fumarate and succinate), and accelerated cellular senescence and mitochondrial dysfunction — findings that link mitochondrial circRNAs to mitochondrial energetics and proliferative status in younger cells. “Taken together, our findings demonstrate the existence and possible function of circular MT-RNR2 during human aging and senescence, implicating its role in promoting the TCA cycle.” The authors note key limitations and outline next steps: clarifying the biogenesis mechanism of mitochondrial circular RNAs (including whether trans-splicing contributes), mapping direct interactions between mitochondrial transcripts and metabolic enzymes, and performing mechanistic studies (in vivo and in additional human cohorts) to test how circMT-RNR2 and GRSF1 influence mitochondrial energetics and organismal aging. These follow-ups will determine whether mitochondrial circular RNAs are actionable targets for modulating mitochondrial metabolism or delaying aspects of cellular aging. DOI - https://doi.org/10.18632/aging.206354 Corresponding authors - Je-Hyun Yoon - jehyun-yoon@ou.edu, and Young-Kook Kim - ykk@jnu.ac.kr Abstract video - https://www.youtube.com/watch?v=f8uZ6_tcOHw Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206354 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, circular RNA, MT-RNR2, GRSF1, TCA cycle To learn more about the journal, please visit https://www.Aging-US.com and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 X - https://twitter.com/AgingJrnl Facebook - https://www.facebook.com/AgingUS/ Instagram - https://www.instagram.com/agingjrnl/ LinkedIn - https://www.linkedin.com/company/aging/ Reddit - https://www.reddit.com/user/AgingUS/ Pinterest - https://www.pinterest.com/AgingUS/ YouTube - https://www.youtube.com/@Aging-US Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM
生命の起源、「RNAワールド仮説」が現実味 自己複製できる45塩基のRNA「QT45」発見 Science誌に掲載。 英ケンブリッジ大学などに所属する研究者らがScience誌に発表した論文「A small polymerase ribozyme that can synthesize itself and its complementary strand」は、たった45塩基の短いRNAが自己複製能力を持つことを実証した研究報告だ。
In this episode of the Pediatric and Developmental Pathology, our hosts Dr. Mike Arnold (@MArnold_PedPath) and Dr. Jason Wang speak with Dr. Aida Glembocki, a Pediatric Pathologist and Masters Degree candidate at the University of Toronto, and Dr. Robert Siddaway, an Oncology Investigator at The Hospital for Sick Children in Toronto. Hear about how The Hospital for Sick Children applies RNA sequencing in pediatric cancer diagnosis to reduce costs and identify key information for diagnostic classification. We also hear about their article in Pediatric and Developmental Pathology: Fusion-Negative Rhabdomyosarcoma: Clinical Application of Targeted RNA Sequencing Related article: Siddaway R, Glembocki AI, Arnoldo A, Staunton J, Liu APY, Yuki KE, Yu M, Cohen-Gogo S, Shlien A, Villani A, Whitlock JA, Hitzler J, Tabori U, Levine AB, Lafrenière A, Nagy A, Chen H, Ngan BY, Somers GR, Abdelhaleem M, Chami R, Hawkins C. Clinical utility of targeted RNA sequencing in cancer molecular diagnostics. Nat Med. 2025 Oct;31(10):3524-3533. doi: 10.1038/s41591-025-03848-8. Epub 2025 Jul 17. PMID: 40676318. Featured public domain music: Summer Pride by Loyalty Freak
RNA episode 149 includes news for 24 February 2026 featuring the latest on Australia's most controversial recruiter, Luke Hemmings, the likely final instalment in the Employment Hero–Seek saga. Company updates from PeopleIN and The Next Group, along with the latest unemployment data and earnings results from Ignite Limited and Ashley Services Group.
Gregory Zuckerman recounts the dramatic mapping and sharing of the COVID-19 genetic sequence, which launched global efforts to develop messenger RNA and adenovirus-based vaccines against the pandemic. 1
Today, we're joined by Professor Matthew Wood, a leading figure in neuroscience and RNA-based therapeutics. He is Professor of Neuroscience at the University of Oxford, Deputy Head of the Medical Sciences Division, and Director of both the MDUK Oxford Neuromuscular Centre and the Oxford-Harrington Rare Disease Centre, a groundbreaking partnership between the University of Oxford and Harrington Discovery Institute dedicated to accelerating therapies for rare genetic diseases affecting millions worldwide.In today's episode we discuss his vision for making antisense oligonucleotides (or ASOs) and gene editing more modular, more scalable, and faster by collaborating with regulators, scientists, and patient groups to bring hope to those with rare neuromuscular and genetic conditions.With rare disease day coming up just next week, I hope you enjoy the insights that Professor Wood shares on the future of the fight against rare disease.01:23 – Meet Matthew Wood07:26 – The Oxford-Harrington Rare Disease Centre10:33 – Collaborations, philanthropy, and industry partnerships13:55 – Key challenges in rare disease therapy development20:00 – Modular and scalable platforms for ASOs28:08 – Scaling gene editing like CRISPR for rare diseases32:38 – Role of AI and computational tools in acceleration37:28 – Future breakthroughs in rare disease treatments44:07 – Advice for new researchers in the fieldInterested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletterTo dive deeper into the topic: Prader Willi syndrome: five much-anticipated therapies poised for approval First-ever approval for Barth Syndrome treatment: what does this mean for ultra-rare disease therapeutics? When rare diseases are not so rare after all: A closer look at where and why this happens
As traditional pesticides lose their effectiveness due to regulatory changes and increasing pest resistance, growers are searching for a new way forward. GreenLight Biosciences’ Jonathan Adamson tells Stephanie Hoff that RNA technology can control the pests that potato growers and other specialty crop farmers deal with regularly. Using RNA-based foliar pesticides, Greenlight Biosciences' products "send the wrong message" to invasive pests. In turn, these pests, like the Colorado potato beetle, will starve themselves.See omnystudio.com/listener for privacy information.
BUFFALO, NY — February 19, 2026 — A new #research paper was #published in Volume 18 of Aging-US on February 8, 2026, titled “Single-cell transcriptomics reveal intrinsic and systemic T cell aging in COVID-19 and HIV.” In this study, co-first authors Alan Tomusiak from the Buck Institute for Research on Aging and the University of Southern California, and Sierra Lore from the Buck Institute for Research on Aging and the University of Copenhagen, together with corresponding author Eric Verdin from the Buck Institute for Research on Aging, developed a new single-cell transcriptomic clock called T immune cell transcriptomic clock (Tictock) to measure aging in specific immune cells. Immune aging increases susceptibility to infection, cancer, and chronic inflammatory disease. Most aging clocks, used to measure it, rely on bulk measurements from mixed cell populations. As a result, they cannot determine whether age-related signals reflect shifts in cell proportions or true molecular aging within defined immune cells. To address this limitation, the research team used single-cell RNA sequencing, a method that measures gene expression in individual cells. They analyzed nearly two million immune cells from the blood of healthy adults to develop Tictock. This tool integrates automated classification of six canonical T cell subsets with cell-type specific age prediction models. This design enables the separation of systemic aging, reflected by changes in cell proportions, from intrinsic aging, which occurs within individual cells. When the team applied Tictock to patients with acute COVID-19, they found two clear effects. First, COVID-19 altered T cell composition, including significant reductions in naïve CD8 and naïve CD4 T cells. Second, the infection increased the biological age of naïve CD8 T cells. In people living with HIV who were receiving long-term antiretroviral therapy, T cell proportions remained largely stable. However, naïve CD8 T cells still showed signs of accelerated aging. The study also uncovered shared biological pathways linked to immune aging. Many of the genes that predicted age were involved in ribosomes, the structures that help cells produce proteins. The researchers also observed that older immune cells often had shorter average transcript lengths, a feature previously linked to aging. These findings suggest that changes in protein production and gene regulation play an important role in immune decline. “Gene Ontology enrichment of 209 genes shared across six clock models identified common pathways including the cytosolic small ribosomal subunit, TNF receptor binding, and cytosolic ribosome components.” Overall, Tictock was designed to measure relative aging within defined T cell populations rather than overall biological aging. By distinguishing systemic from cell-intrinsic immune aging, it provides a clearer understanding of how viral infections such as COVID-19 and HIV reshape immune function. This approach enables the study of immune aging at single-cell resolution and may support improved immune risk assessment in clinical and research settings. DOI - https://doi.org/10.18632/aging.206353 Corresponding author - Eric Verdin - EVerdin@buckinstitute.org Abstract video - https://www.youtube.com/watch?v=_r3AF7OrgKY Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts To learn more about the journal, please visit https://www.Aging-US.com. MEDIA@IMPACTJOURNALS.COM
Send a textWhy do some scientific breakthroughs begin with ridicule?In this episode, we explore cancer research, serendipity, and the philosophy behind unconventional biomedical innovation.Dr. Alexander Shneider, Ph.D. is a biotech entrepreneur and scientist with more than three decades of experience spanning oncology, immunology, vaccines, and translational medicine. He is the Founder and CEO of CureLab Oncology ( https://www.curelaboncology.com/ ), where he is leading the development of a novel biological agent designed to fight cancer through a dual approach—stimulating anti-tumor immunity while also addressing the chronic inflammation that often underlies disease progression.CureLab's lead therapeutic, Elenagen, has demonstrated a strong safety profile and encouraging signals of clinical benefit in international Phase II studies, and is being explored both as an adjunct to standard cancer treatments and for its broader potential in inflammation-driven and age-associated diseases. The platform's reach may extend beyond human medicine, with ongoing interest in applications for oncology in companion animals.Over the course of his career, Dr. Shneider has advised biotechnology, pharmaceutical, and investment organizations across areas including R&D strategy, licensing, technology transfer, product development, and intellectual property. He previously founded CureLab, Inc., where he worked on tools for the rational design of DNA vaccines and the development of broad-spectrum influenza vaccine concepts.In academia, Dr. Shneider has held professor-level appointments and led the Genetic Vaccine Laboratory at Sechenov University, where he helped establish multicenter clinical research infrastructure focused on diseases involving chronic inflammation. His research has also explored evolutionarily conserved RNA structures as targets for antiviral therapies and next-generation vaccine design.In addition to his scientific and entrepreneurial work, he serves on the editorial boards of several peer-reviewed journals in immunology and aging research, reflecting his long-standing engagement at the intersection of basic science, clinical translation, and biotechnology innovation.#CancerResearch #BiomedicalScience #CancerInnovation#ScientificDiscovery #PhilosophyOfScience #Biotech #MedicalInnovation#Oncology #BreakthroughScience #StartupScience #SciencePodcast #DrugDevelopment #Serendipity #Innovation #FutureOfMedicineSupport the show
a16z general partner Jorge Conde talks with Vasant Narasimhan, CEO of Novartis International, about transforming a 250-year-old conglomerate into a pure play medicines company and unlocking $180 billion of value in the process. They cover Novartis's platform technologies: cell and gene therapies, RNA medicines, and radioligand therapies. They also discuss AI in drug discovery, the rise of China as a biotech competitor, and what Vasant looks for when evaluating startup partnerships, including his advice on the killer experiments and CMC work that can make or break a deal. Resources: Follow Vasant Narasimhan on X: https://twitter.com/VasNarasimhanFollow Jorge Conde on X: https://x.com/JorgeCondeBio Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
RNA episode 148 includes news for 17 February 2026 featuring SEEK's sudden share price drop, Robert Half report that just may show why an increase in overqualified candidates is so significant to our market. Company results Randstad, Recruit, and Will Group and the overlooked consequences of the PALM scheme. Question of the Week is, " What is the hidden cost of advertising a job in 2026?"
Både och istället för antingen eller - en podd om integrativ medicin och hälsa
Welcome to a new episode, where we explore the forefront of longevity medicine and regenerative health. Our guests are Dr. Metin Kurtoglu, M.D., Ph.D., and Estelle Nordenfalk, CEO of AgeBack.co. Dr. Kurtoglu is a third-generation physician whose journey spans from early medical studies to advanced research in the U.S. After training in internal medicine at Jackson Memorial Health and the University of Miami, and pursuing oncology research at the National Institutes of Health, he went on to lead cutting-edge developments at Cartesian - including the world's first RNA-transfected CAR-T cell therapy for autoimmune disorders, now in an international phase 3 trial. At AgeBack.co in Stockholm, Dr. Metin provides a comprehensive overview of modern longevity protocols and how new technologies and regenerative treatments are being used to optimize recovery, performance, and long-term vitality. A special focus is placed on Hyperbaric Oxygen Therapy (HBOT) - a central component of the AgeBack model - alongside advanced regenerative therapies such as stem cells, exosomes, and plasma exchange. Estelle Nordenfalk, CEO of AgeBack.co, leads this innovative longevity clinic offering personalized programs rooted in diagnostics, precision medicine, and cellular science - bridging expertise between Stockholm and their advanced partner clinic in Istanbul. Together, we'll dive into how modern science is transforming the way we approach aging, healing, and human potential. ♥ Facebook: https://www.facebook.com/integrativmedicin ♥ Instagram: https://www.instagram.com/both_instead_of_either_or ♥ Youtube: https://www.youtube.com/user/integrativMedicin
This week, we discuss a new antibody-based nasal spray that protects against the flu: how does it work? Plus, the tiny self-replicating molecule that may give clues to the origins of life on Earth, whether we should regulate "mirror life" research, and how bacteria protect oak trees from drought and other stresses... Like this podcast? Please help us by supporting the Naked Scientists
Episode 2747 - In this critical nutrition and health policy episode, Austin Broer examines how common dietary choices accelerate brain aging while addressing RNA vaccine regulatory developments, liver health threats, and children's alarming food additive exposure burdens.
This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w
Autoimmune diseases like myasthenia gravis have long forced patients to trade daily function for chronic immunosuppression, but Cartesian Therapeutics is betting that its experimental RNA‑engineered CAR T cells can rewrite that equation. The company's lead experimental therapy, Descartes‑08, is designed to deliver deep, durable remissions through a short course of outpatient infusions that selectively eliminate the plasma cells driving disease, while sidestepping the toxicity and logistical hurdles of conventional DNA‑based CAR T therapies. We spoke to Carsten Brunn, CEO of Cartesian Therapeutics, about how the company's RNA‑engineered CAR T cells target the root cause of autoimmune diseases, data from its phase 2 study in myasthenia gravis, and the potential to expand the approach into myositis and other rare autoimmune indications.
Genes. Glands. Vessels.
Komórki nowotworowe to nie byty obce, ale nasze własne, tyle że zbuntowane. Zamiast współpracować, odmawiają „honorowej” samodestrukcji (apoptozy), ignorują sygnały z otoczenia i dzielą się wtedy, kiedy nie powinny. – Komórka rakowa jest w sobie zakochana do tego stopnia, że interesuje ją tylko to, żeby siebie powielać – porównuje prof. Kinga Kamieniarz-Gdula z Centrum Zaawansowanych Technologii i Wydziału Biologii UAM w Poznaniu, z którą rozmawiam w najnowszym odcinku. Zdrowe komórki mają fizjologiczny limit podziałów. Po jego przekroczeniu przestają to robić lub umierają. – Natomiast komórki nowotworowe potrafią wyzerować ten licznik i stać się nieśmiertelne. Robią to często przez aktywację enzymu, który nazywa się telomeraza – wyjaśnia uczona. Takie komórki potrafią tworzyć własną sieć naczyń krwionośnych, omijają mechanizmy naprawy DNA i obronę immunologiczną.Słowem: są niezwykle trudnym przeciwnikiem. Mało kto jednak wie, że nowotwory powstają w nas bardzo często – organizm codziennie produkuje tysiące komórek z potencjalnie groźnymi mutacjami – ale zazwyczaj sobie z nimi radzi. Wchodzą do akcji „policjanci” układu odpornościowego, mechanizmy naprawy DNA. W rozmowie porządkujemy współczesne metody leczenia: od wciąż niezwykle skutecznej chirurgii, przez klasyczną chemioterapię i radioterapię, po nowsze terapie celowane oraz immunoterapię – w tym „żywy lek” CAR-T. Niedawno odkryto, że piętą Achillesową komórek rakowych jest końcowy etap przepisywania informacji genetycznej z genu (cząsteczki DNA) na RNA. Większość ludzkich genów ma kilka alternatywnych końców, a wybór tego właściwego może wpływać na końcowy produkt, czyli białko. Aby wykorzystać tę wiedzę w potencjalnej terapii przeciwnowotworowej, prof. Kinga Kamieniarz-Gdula wraz z dr Martyną Plens-Gałąską opracowały innowacyjną metodę do poszukiwań nowych leków, które kierują wyborem, gdzie kończy się gen. Uczone będą kontynuowały nowatorskie badania, m.in. dzięki kolejnemu grantowi ERC uzyskanemu przez prof. Kamieniarz-Gdulę, tym razem Proof of Concept, pozyskanemu na projekt “Biologia molekularna w terapii przeciwnowotworowej – poszukiwania nowych leków, które kierują wyborem, gdzie kończy się gen”. W zespole pracują wspólnie z dr Agatą Stępień.W odcinku usłyszycie też dlaczego sen, ruch, unikanie kancerogenów oraz ogólnie zdrowy styl życia naprawdę mają znaczenie – bo wspierają właśnie te ciche, codzienne interwencje naszego organizmu. Poznacie barwne metafory mechanizmów stojących za genetyką, opowieść o tym, jak to jest wrócić z Oxfrodu nad Wisłę, a także pochwałę badań podstawowych. Polecamy!W opisie wykorzystaliśmy fragmenty informacji prasowej Uniwersytetu im. Adama Mickiewicza.
Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of transformative events shaping the landscape of drug development, regulatory scrutiny, and corporate strategies.At the forefront is Madrigal Pharmaceuticals' strategic acquisition of Ribo Therapeutics' preclinical siRNA programs, valued at $4.4 billion. This move aims to fortify Madrigal's liver disease drug portfolio alongside its promising candidate, resmetirom. By expanding into RNA-based therapies, Madrigal highlights an industry trend focused on gene silencing techniques to target genetic diseases more precisely.Turning to Moderna, it faces a regulatory hurdle as the FDA issued a refusal-to-file letter for its mRNA-based flu vaccine. The regulator's concerns about the trial design, specifically the use of a licensed standard-dose seasonal influenza vaccine as a control arm, emphasize the complexities of advancing mRNA technologies beyond COVID-19 applications. This situation underscores the necessity for meticulous trial designs that align with evolving regulatory standards.In cell therapy, allogeneic CAR-T treatments are gaining attention as companies strive to make these therapies more accessible by using T cells from healthy donors, rather than modifying a patient's cells. Despite technical challenges like graft-versus-host disease and immune rejection, these treatments promise streamlined manufacturing and reduced costs, marking a significant evolution from the pioneering autologous CAR-T success with Emily Whitehead in 2012. Eli Lilly's entry into CAR T-cell therapy through a $2.4 billion acquisition of Orna represents an ambitious expansion into autoimmune therapies. This strategic move exemplifies a broader trend where companies diversify portfolios to include emerging therapeutic modalities promising transformative impacts on patient care.In respiratory medicine, Upstream Bio's phase 2 trial of its TSLP receptor agonist showed encouraging results in reducing asthma exacerbations, comparable to Tezspire. However, falling short of best-case scenarios leaves room for competitors to present more compelling data. This illustrates the competitive nature of asthma treatment development and the ongoing quest for superior therapeutic options.A critical regulatory update arises from the NIH's decision to halt the Xarelto arm of a stroke prevention trial due to safety concerns. This decision highlights the indispensable role of independent monitoring committees in ensuring patient safety and meaningful clinical trial outcomes.On the corporate front, AstraZeneca has articulated an ambitious goal to achieve over 25 blockbuster drugs by 2030 as part of its strategy to reach $80 billion in revenue. This vision underscores the importance of innovation and strategic planning in sustaining growth within an increasingly competitive market.Fujifilm Biotechnologies' completion of its £400 million expansion project in the UK is another notable development, signaling robust investment in antibody production capabilities. This expansion positions Fujifilm as a key player in biopharmaceutical contract manufacturing and underscores the growing demand for flexible production technologies.The biotech sector is also witnessing significant activity with Pelage making strides in addressing hair loss through promising candidate developments. The market's enthusiasm for innovative solutions beyond traditional treatments reflects a broader demand for cutting-edge approaches to longstanding medical challenges.In obesity treatment, Novo Nordisk and Eli Lilly continue to lead with notable advancements. Novo Nordisk's recent developments with its Wegovy pill have been positively received, yet analysts question if this will suffice in maintaining their competitive edge given the dynamic nature of this therapeutic areSupport the show
Continua con questa puntata il viaggio di Smart City nel PNRR per la ricerca. A pochi mesi dalla chiusura del piano, quale eredità lascia il PNRR al sistema dell'innovazione e della ricerca, e come viene gestita? Oggi parliamo del Centro Nazionale RNA & Gene therapy, uno dei cinque centri nazionali per la ricerca costituiti nel 2022 dal MIUR con fondi PNRR pari a 320 milioni di euro. Il centro lavora su due filoni terapeutici su cui ci sono enormi aspettative; una medicina di frontiera con una lunga storia di ricerca scientifica ma un approdo solo recente alla pratica clinica/medica, con moltissime diramazioni. Per citare le più importanti: malattie genetiche e metaboliche, vaccini, lotta al cancro. In tutti questi ambiti, le terapie geniche e a RNA hanno da offrire un ventaglio di approcci terapeutici semplicemente inesistente fino a pochi anni fa. Ne parliamo con Rosario Rizzuto, Presidente del Centro Nazionale per la Terapia Genica e i Farmaci con Tecnologia a RNA; medico e professore di Patologia Generale; già Rettore dell'Università di Padova.
Boosting a Natural Molecule (NAD+) Reverses Alzheimer's Brain Damage in New Study University of Oslo & Ullevaal University Hospital (Norway), February 8, 2026 One of the key drivers of brain dysfunction in Alzheimer's disease (AD) is the protein tau. Under normal conditions, tau helps maintain the internal structure of neurons, supporting the transport systems that allow nerve cells to function properly. In Alzheimer's disease, however, tau becomes abnormally modified and begins to clump together. These aggregates interfere with normal cellular transport, damage neurons, and ultimately contribute to memory impairment. Now, an international team of scientists has identified a previously unrecognized way to protect the brain from this degeneration. Their research shows that increasing levels of the naturally occurring molecule NAD⁺ can counteract neurological damage linked to Alzheimer's disease. Previous research has suggested that boosting NAD⁺ using precursor compounds such as nicotinamide riboside (NR) or nicotinamide mononucleotide (NMN) can produce beneficial effects in animal models of AD and in early-stage clinical studies. However, the biological processes responsible for these effects have remained poorly understood,” explains first author Alice Ruixue Ai. The new study reveals that NAD⁺ works through a previously unidentified RNA-splicing pathway. This pathway is regulated by a protein called EVA1C, which plays an essential role in the process of RNA splicing. RNA splicing allows a single gene to produce multiple isoforms of a protein, and one isoform may show distinctive effects on the other isoforms. Its dysregulation is one of the most recently acknowledged risk factors for AD.
Contributor: Alec Coston, MD Educational Pearls: Disclaimer: this has nothing to do with the ER but is too cool to not talk about. Condition: Carbamoyl phosphate synthetase 1 (CPS1) deficiency Rare inborn error of metabolism Inability to properly break down ammonia Leads to severe hyperammonemia and hepatic encephalopathy Natural history: Without treatment, typically fatal within the first few weeks of life Even with current standard treatments, life expectancy is often limited to ~5–6 years Breakthrough treatment: A team of researchers at the Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania developed the CRISPR-based targeted gene therapy for this patient. First-of-its-kind precision approach tailored to the patient's specific mutation Key components of the therapy: Whole-genome sequencing to identify the exact CPS1 mutation Creation of a custom base-editing enzyme designed to correct that specific mutation Design of a guide RNA to direct the base editor to the precise genomic location Delivery method: Lipid nanoparticles used to deliver the gene-editing machinery Nanoparticles can be targeted to specific tissues Why the liver works well: CPS1 is primarily expressed in hepatocytes The liver is relatively easy to target with lipid nanoparticles Hepatocytes divide frequently, allowing edited genes to be passed on as cells replicate Long-term impact: Once edited, cells continue producing functional CPS1 enzyme Potential for durable, possibly lifelong correction from a single treatment References https://www.nih.gov/news-events/news-releases/infant-rare-incurable-disease-first-successfully-receive-personalized-gene-therapy-treatment Choi Y, Oh A, Lee Y, Kim GH, Choi JH, Yoo HW, Lee BH. Unfavorable clinical outcomes in patients with carbamoyl phosphate synthetase 1 deficiency. Clin Chim Acta. 2022 Feb 1;526:55-61. doi: 10.1016/j.cca.2021.11.029. Epub 2021 Dec 29. PMID: 34973183. Bharti N, Modi U, Bhatia D, Solanki R. Engineering delivery platforms for CRISPR-Cas and their applications in healthcare, agriculture and beyond. Nanoscale Adv. 2026 Jan 5. doi: 10.1039/d5na00535c. Epub ahead of print. PMID: 41640466; PMCID: PMC12865601. Summarized and edited by Jeffrey Olson MS4 Donate: https://emergencymedicalminute.org/donate/ Join our mailing list: http://eepurl.com/c9ouHf
Dr. Pedro Barata and Dr. Ugwuji Maduekwe discuss the evolving treatment landscape in gastroesophageal junction and gastric cancers, including the emergence of organ preservation as a selective therapeutic goal, as well as strategies to mitigate disparities in care. Dr. Maduekwe is the senior author of the article, "Organ Preservation for Gastroesophageal Junction and Gastric Cancers: Ready for Primetime?" in the 2026 ASCO Educational Book. TRANSCRIPT Dr. Pedro Barata: Hello, and welcome to By the Book, a podcast series from ASCO that features compelling perspectives from authors and editors of the ASCO Educational Book. I'm Dr. Pedro Barata. I'm a medical oncologist at University Hospitals Seidman Cancer Center and an associate professor of medicine at Case Western Reserve University in Cleveland, Ohio. I'm also the deputy editor of the ASCO Educational Book. Gastric and gastroesophageal cancers are the fifth most common cancer worldwide and the fourth leading cause of cancer-related mortality. Over the last decade, the treatment landscape has evolved tremendously, and today, organ preservation is emerging as an attainable but still selective therapeutic goal. Today, I'm delighted to be speaking with Dr. Ugwuji Maduekwe, an associate professor of surgery and the director of regional therapies in the Division of Surgical Oncology at the Medical College of Wisconsin. Dr. Maduekwe is also the last author of a fantastic paper in the 2026 ASCO Educational Book titled "Organ Preservation for Gastroesophageal Junction and Gastric Cancers: Ready for Prime Time?" We explore these questions in our conversations today. Our full disclosures are available in the transcript of this episode as well. Welcome. Thank you for joining us today. Dr. Ugwuji Maduekwe: Thank you, Dr. Barata. I'm really, really glad to be here. Dr. Pedro Barata: There's been a lot of progress in the treatment of gastric and gastroesophageal cancers. But before we actually dive into some of the key take-home points from your paper, can you just walk us through how systemic therapy has emerged and actually allowed you to start thinking about a curative framework and really informing surgery decision-making? Dr. Ugwuji Maduekwe: Great, thank you. I'm really excited to be here and I love this topic because, I'm terrified to think of how long ago it was, but I remember in medical school, one of my formative experiences and why I got so interested in oncology was when the very first trials about imatinib were coming through, right? Looking at the effect, I remember so vividly having a lecture as a first-year or second-year medical student, and the professor saying, "This data about this particular kind of cancer is no longer accurate. They don't need bone marrow transplants anymore, they can just take a pill." And that just sounded insane. And we don't have that yet for GI malignancies. But part of what is the promise of precision oncology has always been to me that framework. That framework we have for people with CML who don't have a bone marrow transplant, they take a pill. For people with GIST. And so when we talk about gastric cancers and gastroesophageal cancers, I think the short answer is that systemic therapy has forced surgeons to rethink what "necessary" really means, right? We have the old age saying, "a chance to cut is a chance to cure." And when I started out, the conversation was simple. We diagnose the cancer, we take it out. Surgery's the default. But what's changed really over the last decade and really over the last five years is that systemic therapy has gotten good enough to do what is probably real curative work before we ever enter the operating room. So now when you see a patient whose tumor has essentially melted away on restaging, the question has to shift, right? It's no longer just, "Can I take this out?" It's "Has the biology already done the heavy lifting? Have we already given them systemic therapy, and can we prove it safely so that maybe we don't have to do what is a relatively morbid procedure?" And that shift is what has opened the door to organ preservation. Surgery doesn't disappear, but it becomes more discretionary. Necessary for the patients who need it, and within systems that can allow us to make sure that we're giving it to the right patients. Dr. Pedro Barata: Right, no, that makes total sense. And going back to the outcomes that you get with these systemic therapies, I mean, big efforts to find effective regimens or cocktails of therapies that allow us to go to what we call "complete response," right? Pathologic complete response, or clinical complete response, or even molecular complete response. We're having these conversations across different tumors, hematologic malignancies as well as solid tumors, right? I certainly have those conversations in the GU arena as well. So, when we think of pathologic CRs for GI malignancies, right? If I were to summarize the data, and please correct me if I'm wrong, because I'm not an expert in this area, the traditional perioperative chemo gives you pCRs, pathologic complete response, in the single digits. But then when you start getting smarter at identifying biologically distinct tumors such as microsatellite instability, for instance, now you start talking about pCRs over 50%. In other words, half of the patients' cancer goes away, it melts down by offering, in this case, immunotherapy as a backbone of that neoadjuvant. But first of all, this shift, right, from going from these traditional, "not smart" chemotherapy approaches to kind of biologically-driven approaches, and how important is pCR in the context of "Do I really need surgery afterwards?" Dr. Ugwuji Maduekwe: That's really the crux of the entire conversation, right? We can't proceed and we wouldn't be able to have the conversation about whether organ preservation is even plausible if we hadn't been seeing these rates of pathologic complete response. If there's no viable tumor left at resection, did surgery add something? Are we sure? The challenge before this was how frequently that happened. And then the next one is, as you've already raised, "Can we figure that out without operating?" In the traditional perioperative chemo era, pathologic complete response was relatively rare, like maybe one in twenty patients. When we go to more modern regimens like FLOT, it got closer to one in six. When you add immunotherapy in recent trials like MATTERHORN, it's nearly triple that rate. And it's worth noting here, I'm a health services-health disparities researcher, so we'll just pause here and note that those all sound great, but these landmark trials have significant representation gaps that limit and should inform how confidently we generalize these findings. But back to what you just said, right, the real inflection point is MSI-high disease where, with neoadjuvant dual-checkpoint blockade, trials like NEONIPIGAS and INFINITY show pCR rates that are approaching 50% to 60%. That's not incremental progress, that's a whole new different biological reality. What does that mean? If we're saying that 50% to 60% of the people we take to the OR at the time of surgery will end up having no viable tumor, man, did we need to do a really big surgery? But the problem right now is the gold standard, I think we would mostly agree, the gold standard is pathologic complete response, and we only know that after surgery. I currently tell my patients, right, because I don't want them to be like, "Wait, we did this whole thing." I'm like, "We're going to do this surgery, and my hope is that we're going to do the surgery and there will be no cancer left in your stomach after we take out your stomach." And they're like, "But we took out my stomach and you're saying it's a good thing that there's no cancer." And yes, right now that is true because it's a measure of the efficacy of their systemic therapy. It's a measure of the biology of the disease. But should we be acting on this non-operatively? To do that, we have to find a surrogate. And the surrogate that we have to figure out is complete clinical response. And that's where we have issues with the stomach. In esophageal cancer, the preSANO protocol, which we'll talk about a little bit, validated a structured clinical response evaluation. People got really high-quality endoscopies with bite-on biopsies. They got endoscopic ultrasounds. They got fine-needle aspirations and PET-CT, and adding all of those things together, the miss rate for substantial residual disease was about 10% to 15%. That's a number we can work with. In the stomach, it's a lot more difficult anatomically just given the shape of people's stomachs. There's fibrosis, there's ulceration. A fair number of stomach and GEJ cancers have diffuse histology which makes it difficult to localize and they also have submucosal spread. Those all conceal residual disease. I had a recent case where I scoped the patient during the case, and this person had had a 4 cm ulcer prior to surgery, and I scoped and there was nothing visible. And I was elated. And on the final pathology they had a 7 cm tumor still in place. It was just all submucosal. That's the problem. I'm not a gastroenterologist, but I would have said this was a great clinical response, but because it's gastric, there was a fair amount of submucosal disease that was still there. And our imaging loses accuracy after treatment. So the gap between what looks clean clinically and what's actually there pathologically remains very wide. So I think that's why we're trying to figure it out and make it cleaner. And outside of biomarker-selected settings like MSI-high disease, in general, I'm going to skip to the end and our upshot for the paper, which is that organ preservation, I would say for gastric cancer particularly, should remain investigational. I think we're at the point where the biology is increasingly favorable, but our means of measurement is not there yet. Dr. Pedro Barata: Gotcha. So, this is a perfect segue because you did mention the SANO, just to spell it out, "Surgery As Needed for Oesophageal" trial, so SANO, perfect, I love the abbreviation. It's really catchy. It's fantastic, it's actually a well-put-together perspective effort or program applying to patients. And can you tell us how was that put together and how does that work out for patients? Dr. Ugwuji Maduekwe: Yeah, I think for those of us in the GI space, we have SANO and then we also have the OPRA for rectum. SANO for the upper GI is what takes organ preservation from theory to something that's clinically credible. The trial asked a very simple question. If a patient with a GEJ adenocarcinoma or esophageal adenocarcinoma achieved what was felt to be a clinical complete response after chemoradiation, would they actually benefit from immediate surgery? And the question was, "Can you safely observe?" And the answer was 'yes'. You could safely observe, but only if you do it right. And what does that mean? At two years, survival with active surveillance was not inferior to those who received an immediate esophagectomy. And those patients had a better early quality of life. Makes sense, right? Your quality of life with an esophagectomy versus not is going to be different. That matters a lot when you consider what the long-term metabolic and functional consequences of an esophagectomy are. The weight loss, nutritional deficiencies that can persist for years. But SANO worked because it was very, very disciplined and not permissive. You mentioned rigor. They were very elegant in their approach and there was a fair amount of rigor. So there were two main principles. The first was that surveillance was front-loaded and intentional. So they had endoscopies with biopsies and imaging every three to four months in the first year and then they progressively spaced it out with explicit criteria for what constituted failure. And then salvage surgery was pre-planned. So, the return-to-surgery pathway was already rehearsed ahead of time. If disease reappeared, take the patient to the OR within weeks. Not sit, figure out what that means, think about it a little bit and debate next steps. They were very clear about what the plan was going to be. So they've given us this blueprint for, like, watching people safely. I think what's remarkable is that if you don't do that, if you don't have that infrastructure, then organ preservation isn't really careful. It's really hopeful. And that's what I really liked about the SANO trial, aside from, I agree, the name is pretty cool. Dr. Pedro Barata: Yeah, no, that's a fantastic point. And that description is spot on. I am thinking as we go through this, where can this be adopted, right? Because, not surprisingly, patients are telling you they're doing a lot better, right, when you don't get the esophagus out or the stomach out. I mean, that makes total sense. So the question is, you know, how do you see those issues related to the logistics, right? Getting the multi-disciplinary team, getting the different assessments of CR. I guess PETs, a lot of people are getting access to imaging these days. How close do you think this is, this kind of program, to be implemented? And maybe I would assume it might need to be validated in different settings, right, including the community. How close or how far do you think you see that being applied out there versus continuing to be a niche program, watch and wait program, in dedicated academic centers? Dr. Ugwuji Maduekwe: I love this question. So I said at the top of this, I'm a health equity/health disparities researcher, and this is where I worry the most. I love the science of this. I'm really excited about the science. I'm very optimistic. I don't think this is a question of "if," I think it's a question of "when." We are going to get to a point where these conversations will be very, very reasonable and will be options. One of the things I worry about is: who is it going to be an option for? Organ preservation is not just a treatment choice, and I think what you're pointing out very rightly is it's a systems-level intervention. Look at what we just said for SANO. Someone needs to be able to do advanced endoscopy, get the patients back. We have to have the time and space to come back every three to four months. We have to do molecular testing. There needs to be multi-disciplinary review. There needs to be intensive surveillance, and you need to have rapid access to salvage surgery. Where is that infrastructure? In this country, it's mostly in academic centers. I think about the panel we had at ASCO GI, which was fantastic. And as we were having the conversation, you know, we set it up as a debate. So folks were debating either pro-surveillance or pro-surgery. But both groups, both people, were presenting outcomes based on their centers. And it was folks who were fantastic. Dr. Molena, for example, from Memorial Sloan Kettering was talking about their outcomes in esophagectomies [during our session at GI26], but they do hundreds of these cases there per year. What's the reality in this country? 70% to 80% to 90%, depending on which data you look at, of the gastrectomies in the United States occur at low-volume hospitals. Most of the patients at those hospitals are disproportionately uninsured or on government insurance, have lower income and from racial and ethnic minority groups. So if we diffuse organ preservations without the system to support it, we're going to create a two-tiered system of care where whether you have the ability to preserve your organs, to preserve bodily integrity, depends on where you live and where you're treated. The other piece of this is the biomarker testing gap. One of the things that, as you pointed out at the beginning, that's really exciting is for MSI-high tumors. Those are the patients that are most likely to benefit from immunotherapy-based organ preservation. But here's the problem. If the patient isn't tested at time of initial diagnosis before they ever see me as a surgeon, the door to organ preservation is closed before it's ever open. And testing access remains very inconsistent across academic networks. And then there's the financial toxicity piece where, for gastrectomy, pancreatectomy, I do peritoneal malignancies, more than half of those patients experience significant financial toxicity related to their cancer treatment. We're now proposing adding at least two years, that's the preliminary information, right? It's probably going to be longer. At least a couple of years of surveillance visits, repeated endoscopies, immunotherapy costs. How are we going to support patients through that? We're going to have to think about setting up navigation support, geographic solutions, what financial counseling looks like. My patient for clinic yesterday was driving to see me, and they were talking about how they were sliding because it was snowing. And they were sliding for the entire three-hour drive down here. Are we going to tell people like that that they need to drive down to, right, I work at a high-volume center, they're going to need to come here every three months, come rain or snow, to get scoped as opposed to the one-time having a surgery and not needing to have the scopes as frequently? My concern, like I said, I'm an optimist, I think it is going to work. I think we're going to figure out how to make it work. I'm worried about whether when we deploy it, we widen the already existing disparities. Dr. Pedro Barata: Gotcha, and that's a fantastic summary. And as I'm thinking also of what we've been talking in other solid tumors, which one of the following do you think is going to evolve first? So we are starting to use more MRD-based assays, which are based on blood test, whether it's a tumor-informed ctDNA or non-informed. We are also trying to get around or trying to get more information response to systemic therapies out of RNA-seq through gene expression signatures, or development of novel therapeutics which also can help you there. Which one of these areas you think you're going to help this SANO-like approach move forward, or you actually think it's actually all of the above, which makes it even more complicated perhaps? Dr. Ugwuji Maduekwe: I think it's going to be all of the above for a couple of reasons. I would say if I had to pick just one right now, I think ctDNA is probably the most promising and potentially the missing piece that can help us close the gap between clinical and pathologic response. If you achieve clinical complete response and your ctDNA is negative, so you have clinical and molecular evidence of clearance, maybe that's a low-risk patient for surveillance. If you have clinical complete response but your ctDNA remains positive, I would say you have occult molecular disease and we probably need intensified therapy, closer monitoring, not observation. I think the INFINITY trial is already incorporating ctDNA into its algorithm, so we'll know. I don't think we're at the point where it alone can drive surgical decisions. I think it's going to be a good complement to clinical response evaluation, not a replacement. The issue of where I think it's probably going to be multi-dimensional is the evidence base: who are we testing? Like, what is the diversity, what is the ancestral diversity of these databases that we're using for all of these tests? How do we know that ctDNA levels and RNA-seq expression arrays are the same across different ancestral groups, across different disease types? So I think it's probably going to be an amalgam and we're going to have to figure out some sort of algorithm to help us define it based on the patient characteristics. Like, I think it's probably different, some of this stuff is going to be a little bit different depending on where in the stomach the cancer is. And it's going to be a little bit more difficult to figure out if you have a complete clinical response in the antrum and closer to the pylorus, for example. That might be a little bit more difficult. So maybe the threshold for defining what a clinical complete response needs to be is higher because the therapeutic approach there is not quite as onerous as for something at the GE-junction. Dr. Pedro Barata: Wonderful. And I'm sure AI, whether it's digitization of the pathology from the biopsies and putting all this together, probably might play a role as well in the future. Dr. Maduekwe, it's been fantastic. Thank you so much for sharing your insights with us and also congrats again for the really well-done review published. For our listeners, thank you for staying with us. Thank you for your time. We will post a link to this fantastic article we discussed today in the transcript of this episode. And of course, please join us again next month on the By the Book Podcast for more insights on key advances and innovations that are shaping modern oncology. Thank you, everyone. Dr. Ugwuji Maduekwe: Thank you. Thank you for having me. Watch the ASCO GI26 session: Organ Preservation for Gastroesophageal and Gastric Cancers: Ready for Primetime? Disclaimer: The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement. Follow today's speakers: Dr. Pedro Barata @PBarataMD Dr. Ugwuji Maduekwe @umaduekwemd Follow ASCO on social media: @ASCO on X (formerly Twitter) ASCO on Bluesky ASCO on Facebook ASCO on LinkedIn Disclosures: Dr. Pedro Barata: Stock and Other Ownership Interests: Luminate Medical Honoraria: UroToday Consulting or Advisory Role: Bayer, BMS, Pfizer, EMD Serono, Eisai, Caris Life Sciences, AstraZeneca, Exelixis, AVEO, Merck, Ipson, Astellas Medivation, Novartis, Dendreon Speakers' Bureau: AstraZeneca, Merck, Caris Life Sciences, Bayer, Pfizer/Astellas Research Funding (Inst.): Exelixis, Blue Earth, AVEO, Pfizer, Merck Dr. Ugwuji Maduekwe: Leadership: Medica Health Research Funding: Cigna
Send us a textThis session is a practical walkthrough of where digital pathology and AI truly stand in early 2026—based on five recent PubMed papers and real-world implementation experience.In this episode, I review new clinical adoption guidelines, AI applications in liver cancer imaging and pathology, AI-ready metadata for whole slide images, non-destructive tissue quality control from H&E slides, and machine learning–assisted IHC scoring in precision oncology.This conversation is not about hype. It's about standards, validation, data integrity, and clinical translation—the factors that decide whether AI tools stay in research or reach patient care.Episode Highlights01:21 – Practical digital pathology adoption guidelines (Polish Society of Pathologists)08:05 – AI in liver cancer imaging & pathology, and why framework alignment matters18:10 – AI-generated tissue maps as metadata for WSI archives23:01 – PathQC: predicting RNA integrity and autolysis from H&E slides32:14 – ML-assisted IHC scoring in genitourinary cancers29:42 – Digital Pathology 101 book + community updatesKey TakeawaysDigital pathology adoption still requires clear standards and validation workflowsAI performs best when aligned with existing diagnostic frameworks (e.g., LI-RADS)Metadata extraction is a low-effort, high-impact AI use caseSlide-based quality control can support biobanking and biomarker researchAutomated IHC scoring improves consistency—but adoption remains uneven globallyResources Mentioned Digital Pathology 101 (free PDF & audiobook)Publication Links: a. https://pubmed.ncbi.nlm.nih.gov/41618426/ b. https://pubmed.ncbi.nlm.nih.gov/41616271/ c. https://pubmed.ncbi.nlm.nih.gov/41610818/ d. https://pubmed.ncbi.nlm.nih.gov/41595938/ e. https://pubmed.ncbi.nlm.nih.gov/41590351/ Support the showGet the "Digital Pathology 101" FREE E-book and join us!
Nuestra comprensión del universo depende de la precisión con que podamos medirlo. Desde los relojes de sol hasta los atómicos, la evolución de la tecnología no solo busca puntualidad, sino descifrar por qué el tiempo parece fluir constantemente del pasado al futuro y que ocurre cuando intentamos medirlo a intervalos cuánticos, diminutos, o si pudiéramos hacerlo en escenarios extremos como un agujero negro o el Big Bang. Hemos entrevistado a Miguel Ángel Martín Delgado, catedrático de Física Teórica en la universidad Complutense y autor del libro “¿Qué es el tiempo y como se mide?” (Catarata).Con Carlos Briones hemos analizado un estudio que apoya la hipótesis del mundo RNA, que propone que la vida en la Tierra primitiva comenzó con estas moléculas y no con ADN. En concreto, la investigación muestra la forma en la que pudo generarse el ARN de transferencia, esencial para la síntesis de proteínas. José Luís Trejo nos ha contado una interesante investigación sobre el sistema de orientación de los pájaros carboneros que demuestra que el cerebro puede "descargar" actualizaciones físicas según las necesidades del entorno. Amanda López nos ha informado de un trabajo coliderado por el Instituto de Astrofísica de Andalucía que revela el papel de las tormentas de polvo en la desaparición del agua en Marte. Con testimonios de Adrián Brines, del IAA (CSIC). Hemos informado de la campaña de recaudación de fondos iniciada por la Fundación CRIS Contra el Cáncer para financiar al Grupo de Mariano Barbacid en el CNIO después de los excelentes resultados obtenidos en ratones de un tratamiento contra el cáncer de páncreas; del aplazamiento a marzo de la misión Artemis II de la NASA por fugas de combustible y problemas de comunicación; y de la recuperación parcial de la visión de un paciente con ceguera total a partir de un ensayo clínico de microestimulación eléctrica cerebral desarrollado por investigadores de la Universidad Miguel Hernández de Elche y del CIBER en Bioingenería, Biomateriales y Nanomedicina del Instituto de Salud Carlos III (ISCIII).Escuchar audio
On sème FORT ! Le podcast du jardinage bio et de la permaculture
Au programme de cette émission :Nous parlerons des travaux à faire au jardin (mais c'est très humide...)Que faire au jardin ? On vous dit tout sur notre blog : https://www.monjardinbio.com/blogs/infos/que-faire-au-jardin-en-hiverPuis nous répondrons aux questions que vous nous avez envoyé sur onsemefort@monjardinbio.comCette semaine, Eric nous parle de la régénération naturelle assistée (RNA). C'est une technique de restauration des écosystèmes qui consiste à protéger, sélectionner et gérer les rejets naturels d'arbres, d'arbustes ou d'arbrisseaux qui repoussent spontanément dans les champs...Un sujet à étudier dans nos communes ?
In this episode of Denatured, Jennifer C. Smith-Parker speaks to Erik Digman Wiklud, CEO of Circio and Jacob Becraft, CO-founder and CEO of Strand Therapeutics. Since the mRNA vaccine breakthroughs of the COVID-19 era, attention has turned to what's next for programmable medicines. While first- generation mRNA prove the power of transient genetic instruction, its instability, immune reactivity, and short-lived expression have limited its use mainly to vaccines. Emerging platforms like circular and logic circuit RNA are expanding the field's therapeutic horizons.HostJennifer Smith-Parker, Director of Insights, BioSpaceGuestsErik Digman Wiklund, CEO, CircioJacob Becraft, Co-founder and CEO, Strand TherapeuticsDisclaimer: The views expressed in this discussion by guests are their own and do not represent those of their organizations.
Dr. Sonam Puri discusses the full update to the living guideline on stage IV NSCLC with driver alterations. She shares a new overarching recommendation on biomarking testing and explains the new recommendations and the supporting evidence for first-line and subsequent therapies for patients with stage IV NSCLC and driver alterations including EGFR, MET, ROS1, and HER2. Dr. Puri talks about the importance of this guideline and rapidly evolving areas of research that will impact future updates. Read the full living guideline update "Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2026.3.0" at www.asco.org/thoracic-cancer-guidelines TRANSCRIPT This guideline, clinical tools and resources are available at www.asco.org/thoracic-cancer-guidelines. Read the full text of the guideline and review authors' disclosures of potential conflicts of interest in the Journal of Clinical Oncology, https://ascopubs.org/doi/10.1200/JCO-25-02822 Brittany Harvey: Hello and welcome to the ASCO Guidelines podcast, one of ASCO's podcasts delivering timely information to keep you up to date on the latest changes, challenges, and advances in oncology. You can find all the shows, including this one, at asco.org/podcasts. My name is Brittany Harvey, and today I'm interviewing Dr. Sonam Puri from Moffitt Cancer Center, co-chair on "Therapy for Stage IV Non-Small Cell Lung Cancer with Driver Alterations: ASCO Living Guideline, Version 2026.3.0." It's great to have you here today, Dr. Puri. Dr. Sonam Puri: Thanks, Brittany. Brittany Harvey: And then just before we discuss this guideline, I'd like to note that ASCO takes great care in the development of its guidelines and ensuring that the ASCO Conflict of Interest Policy is followed for each guideline. The disclosures of potential conflicts of interest for the guideline panel, including Dr. Puri, who has joined us here today, are available online with the publication of the guideline in the Journal of Clinical Oncology, which is linked in the show notes. So then, to dive into the content that we're here today to talk about, Dr. Puri, this living clinical practice guideline for systemic therapy for patients with stage IV non-small cell lung cancer with driver alterations is updated on an ongoing basis. So, what data prompted this latest update to the recommendations? Dr. Sonam Puri: So Brittany, non-small cell lung cancer is one of the fastest-moving areas in oncology right now, particularly when it comes to targeted therapy for driver alterations. New data are emerging continuously from clinical trials, regulatory approvals, real-world experience, which is exactly why these are living guidelines. The goal is to rapidly integrate important advances as they happen, rather than waiting for years for a traditional update. Since the last full update of the ASCO Stage IV Non-small Cell Lung Cancer Guideline with Driver Alterations published in 2024, there have been seven new regulatory approvals and changes in first-line therapy for some driver alterations. [This version] of the "Stage IV Non-small Cell Lung Cancer Guidelines with Driver Alterations" represents a full update, which means that the panel reviewed and refreshed every applicable section of the guideline to reflect the most current evidence across therapies including sequencing and clinical decision-making. This is to ensure that clinicians have up-to-date practical guidelines that keep pace with how quickly the field is evolving. Brittany Harvey: Absolutely. As you mentioned, this is a very fast-moving space and this full update helps condense all of those versions that the panel reviewed before into one document, along with additional approvals and new trials that you reviewed during this time period. So then, the first aspect of the guideline is there's a new overarching recommendation on biomarker testing. Could you speak a little bit to that updated recommendation? Dr. Sonam Puri: Yeah, definitely. So the panel has discussed and provided recommendations on comprehensive biomarker testing and its importance in all patients diagnosed with non-small cell lung cancer. Ideally, biomarker testing should include a broad-based next-generation sequencing panel, rather than single-gene tests, along with immunohistochemistry for important markers such as PD-L1, HER2, and MET. These results really drive treatment decisions, both in frontline settings for all patients diagnosed with non-small cell lung cancer and in subsequent line settings for patients with non-small cell lung cancer harboring certain targetable alterations. Specifically in the frontline setting, it helps determine whether a patient should receive upfront targeted therapy or immunotherapy-based approach. We now have strong data that shows that complete molecular profiling results before starting first-line therapy is associated with better overall survival and actually more cost-effective care. Using both tissue and blood-based testing can improve likelihood of getting actionable results in a timely way, and we've also provided guidance on platforms that include RNA sequencing, which are specifically helpful for identifying gene fusions that might be otherwise missed with other platforms. On the flip side, outside of a truly resource-limited setting, single-gene PCR testing really should not be routine anymore. This is what the panel recommends. It's less sensitive and inefficient and increases the risk of missing important actionable alterations. Brittany Harvey: Understood. I appreciate you reviewing that recommendation. It really helps identify critical individual factors to match the best treatment option to each individual patient. So then, following that recommendation, what are the updated recommendations on first-line therapy for patients with stage IV non-small cell lung cancer with a driver alteration? Dr. Sonam Puri: Since the last full update in 2024, there have been four additional interim updates which were published across 2024 and 2025. Compared to the last version, there have been several updates which have been included in this full update. One of the most important shifts has been in first-line treatment of patients with non-small cell lung cancer harboring the classical, or what we call as typical, EGFR mutation. The current version of the recommendation is based on the updated survival data from the phase III FLAURA2 and MARIPOSA studies, based on which the panel recommended to offer either osimertinib combined with platinum-pemetrexed chemotherapy or the combination of amivantamab plus lazertinib in the first-line treatment of classical EGFR mutations. And these recommendations, as I mentioned, are grounded in the results of the FLAURA2 and MARIPOSA trials, both of which demonstrated improvement in progression-free survival and overall survival compared to osimertinib alone in patients with common EGFR mutations. That being said, the panel actually spent significant time discussing the toxicities associated with these treatments as well. These combination approaches come with higher toxicity, longer infusion time, increased treatment frequency. So while combination therapy is now recommended as preferred, the panel has recommended that osimertinib monotherapy remains a reasonable option, particularly for patients with poor performance status and for those who are not interested in treatment intensification after knowing the risks and benefits. Brittany Harvey: Absolutely. It's important to consider both those benefits and risks of those adverse events that you mentioned to match appropriately individualized patient care. So then, beyond those recommendations for first-line therapy, what is new for second-line and subsequent therapies? Dr. Sonam Puri: So this is a section that saw several major updates, particularly again in the EGFR space. The first was an update on treatment after progression on osimertinib for patients with classical EGFR mutation. Here the panel recommends the combination of amivantamab plus chemotherapy, and this recommendation was based on the phase III MARIPOSA-2 trial, which compared amivantamab plus chemotherapy with chemotherapy alone with progression-free survival as the primary endpoint. The study met its primary endpoint, showing an improvement in median PFS with the combination of amivantamab plus chemotherapy compared to chemotherapy alone. And as expected, the combination was associated with higher toxicity. So, although the panel recommends this regimen, the panel emphasizes that patients should be counseled on the side effects which may be moderate to severe with the combination therapy approach. In addition, a new recommendation was added for patients who are not candidates for amivantamab plus chemotherapy. In those cases, platinum-based chemotherapy with or without continuation of osimertinib may be offered, and the option of continuing osimertinib with chemotherapy was recommended and supported by data from a recently presented phase III COMPEL study, which randomized 98 patients with EGFR exon 19 deletion or L858R-mutated advanced non-small cell lung cancer who had experienced no CNS progression on first-line osimertinib, and these patients were randomized to receive platinum-pemetrexed chemotherapy with osimertinib or placebo. Although this study was small, it demonstrated a PFS benefit with continuation of osimertinib with chemotherapy, and this approach may be appropriate for patients without CNS progression who prefer or require alternatives to more intensive treatment strategies. Next was an update on options for patients with EGFR-mutated lung cancer after progression on osimertinib and platinum-based chemotherapy. Here the panel recommended that for patients whose disease has progressed after both osimertinib and platinum-based chemotherapy, a new drug known as datopotamab deruxtecan can be offered as a treatment option. And this treatment recommendation was based on evaluation of pooled data from the TROPION-Lung01 and TROPION-Lung05 study, in which in the pooled analysis about 114 patients with EGFR-mutant non-small cell lung cancer were treated with Dato-DXd, 57% of whom had received three or more prior lines of treatment, and what was observed was an overall response rate of 45% with a median duration of response of 6.5 months. So definitely promising results. Next, we focused on updates to subsequent therapy options for patients with another type of EGFR mutation known as EGFR exon 20 insertion mutations. In this section, the panel added sunvozertinib as a subsequent line option after progression on platinum-based chemotherapy with or without amivantamab. Sunvozertinib is an oral, irreversible, and selective EGFR tyrosine kinase inhibitor with efficacy demonstrated in the phase II WU-KONG6 study conducted in Chinese patient population. In this study, amongst 104 patients with platinum-pretreated EGFR exon 20 mutated non-small cell lung cancer, the observed response rate was 61%. Staying in the EGFR space, the panel added a recommendation for patients with acquired MET amplification following progression on EGFR TKI therapy. In these situations, the panel recommended that treatment may be offered with osimertinib in combination with either tepotinib or savolitinib. As our listeners may know, MET amplification occurs in approximately 10% to 15% of patients with EGFR-mutated non-small cell lung cancer when they progress on third-generation EGFR TKIs, and detection of MET amplification is done with various methods, such as tissue-based methods like FISH, NGS, and IHC, as well as ctDNA-based NGS with variable cut-offs. Over the last few years, several studies have informed this recommendation. I'm going to be discussing some of them. In the phase II ORCHARD trial, 32 patients with MET-amplified non-small cell lung cancer after progression on first-line osimertinib were evaluated, where the combination of osimertinib plus savolitinib achieved an overall response rate of 47% with a duration of response of 14.5 months. More recently, the phase II SAVANNAH trial reported outcomes in 80 patients with MET-amplified tumors after progression on osimertinib, and in this patient population, the combination of savolitinib and osimertinib achieved an overall response rate of 56% with a median PFS of 7.4 months. And lastly, the phase II single-arm INSIGHT 2 trial assessed the efficacy of osimertinib plus tepotinib in patients with advanced EGFR-mutant non-small cell lung cancer who had disease progression following first-line osimertinib therapy. And in this study, in a cohort of 98 patients with MET-amplified tumors confirmed by central testing, the overall response rate with the combination was 50% with a duration of response of 8.5 months. So definitely informing this guideline recommendation. Next, we had an update on recommendation in patients with ROS1-rearranged non-small cell lung cancer. For patients with ROS1-rearranged non-small cell lung cancer, the panel recommended specifically for patients who progressed after first-line ROS1 TKIs, the addition of taletrectinib as a new option alongside repotrectinib. And this recommendation was based on analysis of the results of the TRUST-I and TRUST-II studies, which showed that amongst 113 tyrosine kinase inhibitor-pretreated patients, taletrectinib achieved a confirmed overall response rate of 55.8% with a median duration of response of 16.6 months and a median PFS of 9.7 months, a very promising agent. Finally, for patients with HER2 exon 20 mutated non-small cell lung cancer, the panel added two new oral HER2 tyrosine kinase inhibitors, zongertinib and sevabertinib, as options in addition to T-DXd and after exposure to T-DXd. These recommendations are based on early phase data from two trials: the phase I Beamion LUNG-01 study, which evaluated zongertinib, and the phase I/II SOHO-01 study that evaluated sevabertinib. In this study, zongertinib demonstrated an overall response rate of 71% in previously treated patients, with an overall response rate of 48% amongst patients who had received prior HER2-directed ADCs including T-DXd. Sevabertinib in its early phase study showed an overall response rate of 64% in previously treated but HER2 therapy-naive patients, and an overall response rate of 38% in patients previously exposed to HER2-directed therapy. The panel believes that both agents had manageable toxicity profile and represent meaningful new options for this patient population. Brittany Harvey: Certainly, it's an active space of research, and I appreciate you reviewing the evidence underpinning all of these recommendations for our listeners. So, it's great to have these new options for patients in the later-line settings. And given all of these updates in both the first and the later-line settings, what should clinicians know as they implement this latest living guideline update, and how do these changes impact patients with non-small cell lung cancer? Dr. Sonam Puri: Some great questions, Brittany. I think for clinicians when implementing this update, I think about two practical steps. First is reiterating the importance of comprehensive biomarker testing. That is the only way to identify key drivers and resistance mechanisms that we are now targeting. And second, picking a first-line strategy that balances efficacy and toxicity and patient preference for your specific patient. I think informed decision-making, shared decision-making is more important than any time right now. It has always been important, but definitely very important now. For patients, this guideline brings recommendations on more personalized treatment options for both first-line and post-progression settings, which potentially means better outcomes. But it is also very important for our patients to continue to have informed conversations about side effects, time commitment, and what matters most to them with their providers. The panel in this version of the guideline specifically acknowledges the real-world barriers that prevent patients from receiving guideline-concordant therapy, including challenges with access to comprehensive molecular testing and treatment availability, and the panel emphasizes on the importance of shared decision-making, and we provide practical discussion points to help clinicians navigate these conversations with the patient. In addition, the panel has also addressed common real-world clinical complexities, such as treating elderly or frail patients, managing multiple chronic conditions, considerations around pregnancy and fertility, and certain disease scenarios such as oligoprogression or oligometastatic disease. And where available, the guideline summarizes this existing data to support informed individual decision-making in these complex situations. Brittany Harvey: Shared decision-making is really paramount, especially with all of the options and weighing the risks and benefits and considering the individual circumstances of each patient that comes before a clinician. We've talked a lot about all of the new studies that the panel has reviewed, but what other studies or areas of research is the panel examining for future updates to this living guideline as it continues to be updated on an ongoing basis? Dr. Sonam Puri: Yes, definitely, so much to look forward to, right? Looking ahead, the panel is closely monitoring several rapidly evolving areas that are likely to shape future updates of the guideline. This includes emerging data from ongoing later-phase studies, particularly the studies that are evaluating these new targeted agents moving to earlier lines of therapy, alongside studies evaluating additional combination strategies or more refined approaches to treatment sequencing. We're also closely watching advances in biomarker testing, the evolving understanding of resistance mechanisms, development of new targets, and promising therapeutic agents. I think ultimately the living guideline exists to help clinicians and patients navigate this rapidly evolving field, and we would like to ensure that scientific advances are rapidly translated into better, more personalized patient care. Brittany Harvey: Definitely. We'll look forward to those updates from those ongoing trials and future areas of research that you mentioned to provide better options for patients with non-small cell lung cancer and a driver alteration. So I want to thank you so much for your work to rapidly and continuously update this guideline, and thank you for your time today, Dr. Puri. Dr. Sonam Puri: Thanks so much. Thanks so much for the opportunity. Brittany Harvey: And finally, thank you to all of our listeners for tuning in to the ASCO Guidelines podcast. To read the full guideline, go to www.asco.org/thoracic-cancer-guidelines. You can also find many of our guidelines and interactive resources in the free ASCO Guidelines app available in the Apple App Store or the Google Play Store. There's also a companion episode with Dr. Reuss on the related living guideline on stage IV non-small cell lung cancer without driver alterations that listeners can find in their feeds as well. And if you've enjoyed what you've heard today, please rate and review the podcast and be sure to subscribe so you never miss an episode. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.
When we cling to judgments, identities and fixed beliefs, atoms and subatomic particles organize around that tension, accelerating wear and triggering the very patterns we fear. Shift the story, and the system recalibrates. That's the heart of our exploration as we map the marriage of higher and lower self, move beyond good-versus-bad thinking, and learn to listen to the body's clear signals without moralizing them.Midway through, we guide a focused practice with a direct invitation to reactivate DNA, RNA, and the youth vitality of telomeres. Consider it a structured meditation, a somatic reset, and a clear conversation with your body. We close by grounding that energy and returning to a simple commitment: treat the body like the dear friend it is, choose neutrality over narrative, and let coherence lead the way.If this sparks a shift, share it with someone who needs more ease, subscribe for future explorations, and leave a review telling us the belief you're ready to release. Your story shapes your cells—what story do you want them to follow next?
Preeclampsia is one of those pregnancy conditions that gets mentioned often, explained poorly, and frequently dismissed until it suddenly isn't. In this episode, HeHe sits down with Dr. Dallas Reed to pull back the curtain on what preeclampsia actually is, how it shows up, and what expectant parents deserve to understand long before things feel urgent. Together, they break down the basics in a way that's clear and human, including how common preeclampsia really is, what symptoms to take seriously, and how to make sense of blood pressure readings and pregnancy-related hypertension diagnoses. Dr. Reed explains the differences between severe and non-severe preeclampsia, what monitoring can look like before and after 37 weeks, and why postpartum preeclampsia deserves more attention than it often gets. The conversation also explores prevention and management, including lifestyle considerations, aspirin use, and how care plans may differ depending on risk level, gestational age, and whether someone is being monitored inpatient or outpatient. A major focus of the episode is the future of personalized maternal care, including a deep dive into the Encompass test. This new RNA-based blood test, available between 18 and 22 weeks, helps identify pregnancies at higher risk for preeclampsia and pairs that insight with an evidence-based action plan and virtual support. Dr. Reed shares how this type of testing may change the way providers and families approach monitoring, communication, and early intervention, including potential benefits for out-of-hospital birth settings. This episode is grounded, evidence-based, and empowering, offering expectant parents tools, language, and understanding so they can participate confidently in their care rather than feeling blindsided by it. TIMESTAMPS 00:00 Introduction to Preeclampsia 00:56 Welcome to The Birth Lounge Podcast 01:39 Features of The Birth Lounge App 03:00 Pregnancy and Postpartum Articles 04:54 Introduction to Today's Episode 07:47 Discussion with Dr. Dallas Reed 08:35 Understanding Preeclampsia 10:46 Symptoms and Diagnosis 18:56 Managing Blood Pressure During Pregnancy 22:37 Risk Factors and Prevention 31:59 Strategies to Prevent Preeclampsia 32:29 Healthy Lifestyle Recommendations 33:37 Monitoring and Follow-Up 35:05 Risks and Complications of Preeclampsia 37:05 Postpartum Preeclampsia 39:20 Managing Preeclampsia Before 37 Weeks 41:20 Inpatient Care and Medications 46:22 Understanding the Encompass Test 53:06 Benefits of the Encompass Test for Home Births 58:19 Final Thoughts and Resources Guest Bio: Dr. Dallas Reed, practicing OBGYN, medical geneticist and advisor to Mirvie, a company delivering data-driven solutions for predictive and preventive care in pregnancy. Mirvie recently launched Encompass, which is the first RNA-based blood test to predict preeclampsia risk, combined with an evidence-based preventive action plan and virtual assistant to guide individualized support and care. SOCIAL MEDIA: Connect with HeHe on Instagram Connect with Mirvie on IG BIRTH EDUCATION: Join The Birth Lounge for judgment-free, evidence-based childbirth education that shows you exactly how to navigate hospital policies, avoid unnecessary interventions, and have a trauma-free labor experience, all while feeling wildly supported every step of the way Want prep delivered straight to your phone? Download The Birth Lounge App for bite-sized birth and postpartum tools you can use anytime, anywhere. And if you haven't grabbed it yet… Snag my free Pitocin Guide to understand the risks, benefits, and red flags your provider may not be telling you about, so you can make informed, powerful decisions in labor.
On Chen, MD, and Tahmid Rahman, MD discuss how plozasiran—an RNA-based APOC3-targeting therapy for patients with familial chylomicronemia syndrome with infrequent injections and a favorable safety profile—can transform care when integrated through coordinated multidisciplinary workflows with pharmacists leading patient identification, access, and monitoring.
We discussed a few things including: 1. Your career journeys 2. Gitte's biotech venture 3. Garnet's venture capital firm 4. Discuss effects of federal policies on innovation ecosystem 5. Discuss outlook for 2026 Garnet Heraman is a serial entrepreneur and investor with 25 years experience at the intersection of innovation + technology. Originally from the island nation of Trinidad & Tobago, he was educated at Columbia University (BA), NYU (MBA) and The London School of Economics. As a dotcom entrepreneur Garnet had 3 exits, 1 of which was to a publicly traded company. As an investor, he is co-founder and managing partner of Aperture® Venture Capital, a seed stage fintech fund backed by 7 different Fortune 500 corporations. He is also an LP in other VC funds such as NY InsurTech Fund II and the Berkeley Skydeck Fund, as well as a prolific angel investor. Garnet is highly sought after as a startup technology expert, appearing in over 30 business publications and at events on 5 continents. ------ Gitte Pedersen is a scientist, CEO, company builder, and investor with a mission to improve health and sustainability. RNA enthusiast. Focused on helping cancer patients survive through better diagnostics and treatment navigation tools. Serial entrepreneur. Advised several small and medium-sized biotech companies and the Danish Ministry of Foreign Affairs, bringing in +$1B deals to Danish Biotech companies. Advised the European Commission on evidence-based innovation and investment policies. Won numerous prizes and awards and raised $8M+ in grants. Worked at Novo Nordisk in several management positions, inventing, developing and bringing multiple products to market worldwide. #podcast #AFewThingsPodcast
Editor's note: Welcome to our new AI for Science pod, with your new hosts RJ and Brandon! See the writeup on Latent.Space (https://Latent.Space) for more details on why we're launching 2 new pods this year. RJ Honicky is a co-founder and CTO at MiraOmics (https://miraomics.bio/), building AI models and services for single cell, spatial transcriptomics and pathology slide analysis. Brandon Anderson builds AI systems for RNA drug discovery at Atomic AI (https://atomic.ai). Anything said on this podcast is his personal take — not Atomic's.—From building molecular dynamics simulations at the University of Washington to red-teaming GPT-4 for chemistry applications and co-founding Future House (a focused research organization) and Edison Scientific (a venture-backed startup automating science at scale)—Andrew White has spent the last five years living through the full arc of AI's transformation of scientific discovery, from ChemCrow (the first Chemistry LLM agent) triggering White House briefings and three-letter agency meetings, to shipping Kosmos, an end-to-end autonomous research system that generates hypotheses, runs experiments, analyzes data, and updates its world model to accelerate the scientific method itself.* The ChemCrow story: GPT-4 + React + cloud lab automation, released March 2023, set off a storm of anxiety about AI-accelerated bioweapons/chemical weapons, led to a White House briefing (Jake Sullivan presented the paper to the president in a 30-minute block), and meetings with three-letter agencies asking “how does this change breakout time for nuclear weapons research?”* Why scientific taste is the frontier: RLHF on hypotheses didn't work (humans pay attention to tone, actionability, and specific facts, not “if this hypothesis is true/false, how does it change the world?”), so they shifted to end-to-end feedback loops where humans click/download discoveries and that signal rolls up to hypothesis quality* Cosmos: the full scientific agent with a world model (distilled memory system, like a Git repo for scientific knowledge) that iterates on hypotheses via literature search, data analysis, and experiment design—built by Ludo after weeks of failed attempts, the breakthrough was putting data analysis in the loop (literature alone didn't work)* Why molecular dynamics and DFT are overrated: “MD and DFT have consumed an enormous number of PhDs at the altar of beautiful simulation, but they don't model the world correctly—you simulate water at 330 Kelvin to get room temperature, you overfit to validation data with GGA/B3LYP functionals, and real catalysts (grain boundaries, dopants) are too complicated for DFT”* The AlphaFold vs. DE Shaw Research counterfactual: DE Shaw built custom silicon, taped out chips with MD algorithms burned in, ran MD at massive scale in a special room in Times Square, and David Shaw flew in by helicopter to present—Andrew thought protein folding would require special machines to fold one protein per day, then AlphaFold solved it in Google Colab on a desktop GPU* The E3 Zero reward hacking saga: trained a model to generate molecules with specific atom counts (verifiable reward), but it kept exploiting loopholes, then a Nature paper came out that year proving six-nitrogen compounds are possible under extreme conditions, then it started adding nitrogen gas (purchasable, doesn't participate in reactions), then acid-base chemistry to move one atom, and Andrew ended up “building a ridiculous catalog of purchasable compounds in a Bloom filter” to close the loopAndrew White* FutureHouse: http://futurehouse.org/* Edison Scientific: http://edisonscientific.com/* X: https://x.com/andrewwhite01* Cosmos paper: https://futurediscovery.org/cosmosFull Video EpisodeTimestamps00:00:00 Introduction: Andrew White on Automating Science with Future House and Edison Scientific00:02:22 The Academic to Startup Journey: Red Teaming GPT-4 and the ChemCrow Paper00:11:35 Future House Origins: The FRO Model and Mission to Automate Science00:12:32 Resigning Tenure: Why Leave Academia for AI Science00:15:54 What Does ‘Automating Science' Actually Mean?00:17:30 The Lab-in-the-Loop Bottleneck: Why Intelligence Isn't Enough00:18:39 Scientific Taste and Human Preferences: The 52% Agreement Problem00:20:05 Paper QA, Robin, and the Road to Cosmos00:21:57 World Models as Scientific Memory: The GitHub Analogy00:40:20 The Bitter Lesson for Biology: Why Molecular Dynamics and DFT Are Overrated00:43:22 AlphaFold's Shock: When First Principles Lost to Machine Learning00:46:25 Enumeration and Filtration: How AI Scientists Generate Hypotheses00:48:15 CBRN Safety and Dual-Use AI: Lessons from Red Teaming01:00:40 The Future of Chemistry is Language: Multimodal Debate01:08:15 Ether Zero: The Hilarious Reward Hacking Adventures01:10:12 Will Scientists Be Displaced? Jevons Paradox and Infinite Discovery01:13:46 Cosmos in Practice: Open Access and Enterprise Partnerships Get full access to Latent.Space at www.latent.space/subscribe
Some dogs are more adept at learning language than others. Researchers studying these special dogs discovered that, much like toddlers, these smart furry canine companions can pick up words just by eavesdropping on their owners' conversations.PLUSTracking space debris using seismometersUsing nitrogen to boost treesHow Mars shapes our climateExtracting ice age mammoth RNA and using lichens to find dino bones
Epigenetic regulation of gene expression is an important mechanism in development and disease. N6-methyladenosine (m6A) is one of the most prevalent epigenetic modifications for RNA and has been shown to play critical roles in processes such as embryo development, cancer, and stress responses. Our guests today investigate how m6A regulates X chromosome dosage compensation to ensure proper balance of gene expression from X chromosomes between sexes. X-chromosome dosage compensation is accomplished through two complementary mechanisms. First, X-chromosome inactivation (XCI) silences one of the two X chromosomes in female cells. Second, the remaining active X chromosome is transcriptionally upregulated so that its gene expression levels are balanced with those of the autosomes, a process known as X-to-autosome (X-to-A) compensation. The authors dissect the distinct contributions of m6A RNA methylation to XCI versus X-to-A compensation across multiple embryonic lineages, providing deeper insights into the epigenetic regulation of early development.GuestsSrimonta Gayen, PhD, Department of Developmental Biology and Genetics, Indian Institute of Science, IndiaHostJanet Rossant, Editor-in-Chief, Stem Cell Reports and The Gairdner FoundationSupporting ContentPaper link: "The role of m6A RNA methylation in the maintenance of X chromosome inactivation and X-to-autosome dosage compensation in early embryonic lineages," Stem Cell ReportsAbout Stem Cell ReportsStem Cell Reports is the open access, peer-reviewed journal of the International Society for Stem Cell Research (ISSCR) for communicating basic discoveries in stem cell research, in addition to translational and clinical studies. Stem Cell Reports focuses on original research with conceptual or practical advances that are of broad interest to stem cell biologists and clinicians. X: @StemCellReportsAbout ISSCR Across more than 80 countries, the International Society for Stem Cell Research (@ISSCR) is the preeminent global, cross-disciplinary, science-based organization dedicated to advancing stem cell research and its translation to medicine. ISSCR StaffKeith Alm, Chief Executive OfficerShuangshuang Du, Scientific Programs ManagerYvonne Fisher, Managing Editor, Stem Cell ReportsKym Kilbourne, Director of Media and Strategic CommunicationsMegan Koch, Senior Marketing ManagerJack Mosher, Scientific DirectorHunter Reed, Senior Marketing Coordinator
De Darwin a Santa: física sin cuentos (dos soles, origen de la vida y 727 km/s)Capítulos (YouTube)00:00 – Intro: ¿hay algo después de la muerte? Ciencia vs fe10:48 – Darwin: viaje, fósiles, Galápagos y selección natural16:39 – Copérnico y Galileo: del geocentrismo al Sol (lunas de Júpiter)20:14 – Kepler, elipses y la ciencia bajo la Inquisición + miedo a lo desconocido29:20 – ¿Cómo pudo surgir la vida? Experimento Miller-Urey y el rol del RNA35:21 – ¿Santa es físicamente posible? La cuenta: ~727 km/s y velocidad de escape42:34 – Dos soles como Tatooine: estrellas binarias, noches “infinitas” y multiversos mal entendidos50:22 – Cierre y dónde seguir a Daniel (Notas Astronómicas)DescripciónVolvió Daniel Isaac (Notas Astronómicas) para aterrizar temas que suelen asustar… con física. Hablamos de la idea de “nada” después de la muerte, por qué la ciencia no compite con la espiritualidad (pero sí exige evidencias), y nos vamos de tour histórico con Darwin, Copérnico, Galileo y Kepler: cómo pasamos del “todo gira a la Tierra” a entender órbitas elípticas y la evolución por selección natural.Luego bajamos a lo práctico:Origen de la vida: qué demostró realmente Miller-Urey y por qué el RNA importa.Santa Claus con física: si tuviera que repartir en ~36 h a ~100M de casas, ¡necesitaría ~727 km/s! (sí, más allá de la velocidad de escape
On today's ID the Future, host Casey Luskin continues a deep dive into the mounting hurdles facing origin of life (OOL) research with prebiotic synthesis expert Dr. Edward Peltzer. Peltzer, a seasoned ocean chemist and researcher, breaks down the critical flaws in the RNA world hypothesis, revealing that many successful lab experiments actually rely on investigator interference—intelligently designed interventions that researchers must make in experiments in order to yield results. But that's not how the prebiotic atmosphere would have worked, notes Peltzer: "Unless you've got graduate students and post-docs working on the early Earth to set up these conditions that were used in the experiments, it's not gonna happen." Peltzer also discusses how the goalposts of origin-of-life theory keep moving as our understanding of cellular complexity expands. And he shares a personal story of censorship as the discussion ends by exploring the risks faced by scientists who question the standard evolutionary paradigm. This is Part 2 of a two-part conversation. Look for Part 1 in a separate episode. Source
Molecular Therapy Editor-in-Chief Dr. Joseph Glorioso joins researchers Kathy Steece-Collier (Michigan State University), Jeffrey Kordower (Arizona State University), and Fredric Manfredsson (Barrow Neurological Institute) to discuss their groundbreaking work on RNA interference for Parkinson’s disease. This episode focuses on their recent article titled "Disease-modifying, multidimensional efficacy of putaminal CaV1.3-shRNA gene therapy in aged parkinsonism male and female macaques." Music: 'Electric Dreams' by Scott Buckley - released under CC-BY 4.0. www.scottbuckley.com.auShow your support for ASGCT!: https://asgct.org/membership/donateSee omnystudio.com/listener for privacy information.
Kimchi, a traditional Korean fermented vegetable dish, is rich in diverse lactic acid bacteria, bioactive compounds, and fibers that support gut integrity, microbial balance, immune signaling, and overall metabolic resilience A recent study published in npj Science of Food used single-cell RNA sequencing to map how daily kimchi intake influences immune cells, offering insight into food-driven changes in human immune regulation Their findings showed that kimchi strengthened key immune functions by helping cells recognize threats more effectively and supporting balanced T cell activity, without triggering overactive immune responses Beyond immune modulation, kimchi intake supports metabolic health, reduces body fat, improves cholesterol markers, nourishes the gut microbiome, strengthens the gut barrier, enhances digestion, and influences mood through gut-brain signaling Choosing raw, unpasteurized kimchi made with simple ingredients ensures you get the full benefit of its live cultures; homemade versions offer more control and better microbial diversity
We love to hear from our listeners. Send us a message. This week's special holiday episode of the Business of Biotech brings seven chief editors from the Life Science Connect family together to talk about the life sciences industry topics, trips, and reporting that mattered most in 2025, and what each editor has planned for 2026. From the RNA, cell, and gene therapy space to small molecule manufacturing, bioprocessing, drug discovery, and outsourcing, the editors weigh in on key industry trends, new developments, and policy surprises from their respective coverage areas. Topics include biotech funding dynamics, FDA leadership, China's growing role, favorite holiday movies, and much, much more. Special thanks to Tyler Menichiello and the Better Biopharma podcast for hosting this roundtable discussion. Happy New Year! Access this and hundreds of episodes of the Business of Biotech videocast under the Business of Biotech tab at lifescienceleader.com. Subscribe to our monthly Business of Biotech newsletter. Get in touch with guest and topic suggestions: ben.comer@lifescienceleader.comFind Ben Comer on LinkedIn: https://www.linkedin.com/in/bencomer/
Welcome to The Times of Israel's Daily Briefing, your 20-minute audio update on what's happening in Israel, the Middle East and the Jewish world. Diaspora affairs reporter Zev Stub and reporter Diana Bletter join host Jessica Steinberg for today's episode. As Spain implements the largest state-level boycott of Israel, Stub reviews elements of the embargo and whether it could set a precedent of similar gestures from other countries. Bletter reports on a recent visit to the northern city of Kiryat Shmona, badly damaged during the year of Hezbollah strikes, and still struggling to revitalize itself. A look at Israel's population numbers shows that more people exited the country in 2024 than entered it, reports Stub. This came even as statistics paint a nuanced picture of rising immigration to the country amid skyrocketing antisemitism globally. Finally, Bletter reports on the resilience and diversity of Israeli science and medical research, including research on coral reefs, and how cancer can help heal ailing hearts. Check out The Times of Israel's ongoing liveblog for more updates. For further reading: With new trade restrictions, Spain looks to trigger EU cascade against Israel Spanish Jews warn map of local Jewish and ‘Zionist’ businesses will lead to violence Half-empty and scarred by war, Kiryat Shmona sees protests – and grassroots rejuvenation More than 69,000 Israelis left Israel in 2025, as population reached 10.18 million In surprising breakthrough, scientists in Israel find cancer may help heal the failing heart Israeli scientists say tiny organisms can revamp their own RNA to survive extreme heat New Israeli research shows coral reefs shape the ebb and flow of local microbial life Subscribe to The Times of Israel Daily Briefing on Apple Podcasts, Spotify, YouTube, or wherever you get your podcasts. This episode was produced by Podwaves. IMAGE: A pro-Palestinian demonstrator holds a banner reading: 'Boycott Israeli apartheid' during a protest in Madrid, Spain, Saturday, Oct. 4, 2025. (Bernat Armangue/AP Photo)See omnystudio.com/listener for privacy information.
Are you tired of praying for healing while your body, mind, and diagnosis stay the same? In this Prophetic Spiritual Warfare message, Kathy DeGraw calls you out of passivity and into kingdom dominion, speaking directly to your DNA in the power of Jesus. Purchase Kathy's book Healed at Last – Overcome Sickness to Receive your Physical Healing on Amazon https://a.co/d/6a6mt8w or at: https://www.kathydegrawministries.org/healed-at-last/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, ecourses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# So many believers are begging God for healing while tolerating sickness, settling for prescriptions, and forgetting the dominion Jesus already gave them. In this fiery message, Kathy DeGraw exposes passivity, calls you higher, and teaches you how to speak to your DNA, and cells in the name of Jesus. From Pentecostal altar-fire to practical teaching, Kathy shows you how to stop chasing everyone else's prayers and start using your own God-given authority. You'll learn why dominion is more than occasionally "taking authority" over a symptom—it's a lifestyle that shifts spiritual atmospheres, silences demonic assignments, and commands sickness to bow. Kathy shares testimonies of being healed 17 times without medical intervention, how she prays over DNA, RNA, proteins, and prions, and why anointing oil is a powerful point of contact when it's used by faith. If you're battling chronic illness, neurological issues, or fear of disease, this episode will provoke you to get hungry, get aggressive, and go to the throne room yourself until you walk in divine health and fulfill your destiny. #divinehealing #spiritualwarfare #deliveranceministry #anointingoil #walkindominion **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Warfare Prayer Declarations at https://kathydegrawministries.org/declarations-download - Kathy's training, mentoring and ecourses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KDMGLORY - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
America Out Loud PULSE with Dr. Peter McCullough and Malcolm Out Loud – Is Dr McCullough aware of increased levels of Alpha-Gal Syndrome? I have one concern regarding curcumin. I heard from a doctor that curcumin inhibits natural RNase L, an enzyme that degrades RNA, including exogenous mRNA. Could you enlighten me on that, as surely we'd want to speed that process?
Nature: Asteroids, antibiotics and ants: a year of remarkable scienceIn this episode:1:58 Evidence of ancient brine on an asteroidSamples taken from the asteroid Bennu by NASA's OSIRIS-REx spacecraft suggest the parent body it originated from is likely to have contained salty, subsurface water. This finding provides insights into the chemistry of the early Solar System, and suggests that brines might have been an important place where pre-biotic molecules were formed.News & Views: Asteroid Bennu contains salts from ancient brineNature Podcast: Asteroid Bennu contains building blocks of life08:01 How gene expression doesn't always reflect a cell's functionCells are often grouped into categories according to the RNA molecules they produce. However a study of zebrafish (Danio rerio) brains revealed that cells can be functionally diverse even if they appear molecularly similar. This finding adds more nuance to how a cell's ‘type' is ultimately defined.News & Views: Does a cell's gene expression always reflect its function?12:01 The disproportionate mortality risks of extreme rainfallAn assessment of death rates in India's coastal megacity of Mumbai revealed that the impact of extreme rainfall events will be highest for women, young children and residents of informal settlements. This situation is likely to become more pronounced as a result of climate change.News & Views: Extreme rainfall poses the biggest risk to Mumbai's most vulnerable people14:46 An AI-designed underwater glueInspired by animals like barnacles and aided by machine learning, researchers have developed a super-sticky compound that works as an underwater adhesive. To demonstrate its properties, researchers applied it to a rubber duck, which stuck firmly to a rock on a beach despite being battered by the sea.News & Views: AI learns from nature to design super-adhesive gels that work underwaterNature Podcast: Underwater glue shows its sticking power in rubber duck test Hosted on Acast. See acast.com/privacy for more information.
© 2020-2026 Ivy Podcast Discovery LLC
