Podcasts about Broad Institute

Doctors in the US have become the first to treat a baby with a customised gene-editing therapy after diagnosing the child with a severe genetic disorder that kills about half of those affected in early infancy. Ian Sample explains to Madeleine Finlay how this new therapy works and how it paves the way for even more complex gene editing techniques. David Liu, a professor at the Broad Institute of MIT and Harvard and the inventor of these therapies, also describes the barriers that could prevent them reaching patients, and how he thinks they can be overcome. Help support our independent journalism at theguardian.com/sciencepod

Send us a textIn this episode of Navigating the Customer Experience, we're joined by Eric Karofsky, an award-winning expert in customer experience (CX), user experience (UX), and employee engagement, and the founder of VectorHX, a human experience agency. Eric shares his professional journey—from decades in agency and consultancy work with major brands like Michelin and Royal Caribbean, to leading UX at the Broad Institute of MIT and Harvard, and now building his own company focused on creating seamless customer interactions across digital and physical touchpoints.Eric discusses how AI is rapidly reshaping the customer experience landscape, emphasizing that it's a powerful tool—not a solution on its own. He shares both the promise and the current limitations of AI, particularly in customer support scenarios, likening poorly designed chatbots to frustrating call center loops from the 1980s.A major theme in the episode is understanding customer behavior through cultural, situational, and demographic lenses. Eric cautions against forcing users into preferred communication channels and instead advises companies to map the ideal journey for different personas and optimize each channel for a frictionless experience.He offers a powerful case study from the pharmaceutical industry, where AI is being used to transform labor-intensive literature reviews—cutting timelines from six months to potentially two weeks. This not only boosts business efficiency but also accelerates drug development, delivering life-saving treatments to patients faster.Eric also touches on:AI leadership and how it should drive business strategy by identifying areas for efficiency and innovation.Tools he can't live without, like Claude AI and Notion, which he uses to manage his business and ideas.His excitement about no-code tools like Bolt.new and Lovable, which allow rapid prototyping of full-stack apps without technical skills.The enduring value of classic books like Getting to Yes and The Design of Everyday Things, which shaped his thinking around negotiation and customer-centric design.The importance of motivation and resilience, fueled by the exciting pace of innovation and meaningful human connections with clients and team members.He closes with a favorite quote by Benjamin Franklin:"Tell me and I forget. Teach me and I remember. Involve me and I learn." A reminder of the value of active learning and mentorship in building strong, collaborative teams.You'll leave this episode with fresh insights on CX, AI strategy, and how to build human-centered experiences in a rapidly evolving digital world.

Join us for a fascinating conversation with Dr. Livnat Jerby, an Assistant Professor of Genetics at Stanford University, Chan Zuckerberg Biohub Investigator, and Paul Allen Distinguished Investigator. In this episode, Dr. Jerby shares how her lab is decoding and engineering immune circuits to create next-generation cell therapies, drawing on high-throughput technologies and computational modeling. We explore how multicellular programs shape disease and immunity, revisit her work on immune evasion during her time at the Broad Institute, and discuss the future of synthetic immune systems and interdisciplinary science. Hosted by Emma Chen.

With the help of cat owners, a new project investigates cats' biology and aims to link some of their behaviors to their genes.Calling all cat people: This one's for you. Despite humans' long history of welcoming felines into their homes and delis, research on cats lags far behind research on dogs. Now, scientists behind the project Darwin's Ark are working to close the cat gap by enlisting cat caretakers from across the country to submit a tuft of fur and answer a few questions about their feline's appearance, personality, and behaviors.Host Flora Lichtman talks about the project, as well as what we do and don't know about cat genetics, with Dr. Elinor Karlsson, chief scientific officer at Darwin's Ark, and director of the Vertebrate Genomics Group at the Broad Institute of MIT and Harvard Universities.Transcript for this segment will be available after the show airs on sciencefriday.com. Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

Genetic testing in our patients with Inborn Errors of Immunity is becoming more common and this episode will help listeners better understand how to incorporate this into their practice. Sarah Hendrickson, MD, PhD, walks us through who should consider testing, considerations in ordering the testing, the types of testing available and how to think through the results.Helpful LinksClinVar - https://www.clinicalgenome.org/This is a website by the NIH where you can type in any gene and it will list the variants that have been identified and refer you to the papers where they have been published.Franklin Genoox - https://franklin.genoox.com/clinical-db/homeThis is a website where you can look at the gene or protein of interest and look at how the variant might change the protein structure and function and what has been published.  GnomAD - https://gnomad.broadinstitute.org/This is another site where you can look at a gene of interest and how the variant might affect protein structure and function.  It is run by the Broad Institute in Cambridge, MA.MARRVEL - https://marrvel.org/This is a website that searches multiple databases for a gene or variant and helps to determine the likelihood that a variant might cause disease.

Let's shift our focus from just the people behind the science to the technologies they're using.In this special episode, we dive deep into spectral flow cytometry with a panel of leading experts, featuring:• Tamar Tak, Coordinator Flow Cytometry Facility, Leiden University Medical Center• Domenico Lo Tartaro, University of Modena and Reggio Emilia• Andrew Patentreger, Senior Team Lead, Broad Institute of MIT and Harvard• Kelly Lundsten, Biotechnology Market and Technical Bioassay Consultant, Luminous Bioanalytical Consulting• John Bianchi Colangeli, Resource Technologist I, Broad Institute of MIT and HarvardThey introduce the fundamentals of spectral flow and explain the chief benefits it can bring to your research.Our panel also chats about the latest instrument innovations, such as the CytoFLEX mosaic Spectral Detection Module which can switch between conventional and spectral modes, enabling them to answer new research questions, optimize panel designs, and gain deeper insights.Watch or listen to all episodes of Flow Stars: flowstars.bitesizebio.com

Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH.A clip from our conversationThe audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.Transcript with audio and external linksEric Topol (00:06):Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute's Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you.Anna Greka (00:48):Oh, thank you. It's a great honor to be here and glad to chat with you, Eric.Eric Topol (00:54):Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion.A Mysterious Kidney Disease Unraveled Anna Greka (01:59):Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising.Anna Greka (02:51):After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially results in a neoprotein like a toxic, what I call a mangled protein that sort of accumulates inside the kidney cells.Anna Greka (03:55):And that's where my sort of adventure began. It was Eric Lander's group, who is the founding director of the Broad who discovered the mutation. And then through a conversation we had here in Boston, we sort of discovered that there was an opportunity to collaborate and so that's how I came to the Broad, and that's the beginnings of this story. I think what's fascinating about this story though, that starts in a remote Mediterranean island and then turns out to be a disease that you can find in every continent all over the world. There are probably millions of patients with kidney disease in whom we haven't recognized the existence of this mutation. What's really interesting about it though is that what we discovered is that the mangled protein that's a result of this misspelling of this mutation is ultimately captured by a family of cargo receptors, they're called the TMED cargo receptors and they end up sort of grabbing these misfolded proteins and holding onto them so tight that it's impossible for the cell to get rid of them.Anna Greka (04:55):And they become this growing heap of molecular trash, if you will, that becomes really hard to manage, and the cells ultimately die. So in the process of doing this molecular sleuthing, as I call it, we actually also identified a small molecule that actually disrupts these cargo receptors. And as I described in my TED Talk, it's a little bit like having these cargo trucks that ultimately need to go into the lysosome, the cells recycling facility. And this is exactly what this small molecule can do. And so, it was just like a remarkable story of discovery. And then I think the most exciting of all is that these cargo receptors turn out to be not only relevant to this one mangled misshapen protein, but they actually handle a completely different misshapen protein caused by a different genetic mutation in the eye, causing retinitis pigmentosa, a form of blindness, familial blindness. We're now studying familial Alzheimer's disease that's also involving these cargo receptors, and there are other mangled misshapen proteins in the liver, in the lung that we're now studying. So this becomes what I call a node, like a nodal mechanism that can be targeted for the benefit of many more patients than we had previously thought possible, which has been I think, the most satisfying part about this story of molecular sleuthing.Eric Topol (06:20):Yeah, and it's pretty extraordinary. We'll put the figure from your classic Cell paper in 2019, where you have a small molecule that targets the cargo receptor called TMED9.Anna Greka (06:34):Correct.Expanding the MissionEric Topol (06:34):And what's amazing about this, of course, is the potential to reverse this toxic protein disease. And as you say, it may have applicability well beyond this MUC1 kidney story, but rather eye disease with retinitis pigmentosa and the familial Alzheimer's and who knows what else. And what's also fascinating about this is how, as you said, there were these limited number of families with the kidney disease and then you found another one, uromodulin. So there's now, as you say, thousands of families, and that gets me to part of your sleuth work is not just hardcore science. You started an entity called the Ladders to Cures (L2C) Scientific Accelerator.Eric Topol (07:27):Maybe you can tell us about that because this is really pulling together all the forces, which includes the patient advocacy groups, and how are we going to move forward like this?Anna Greka (07:39):Absolutely. I think the goal of the Ladders to Cures Accelerator, which is a new initiative that we started at the Broad, but it really encompasses many colleagues across Boston. And now increasingly it's becoming sort of a national, we even have some international collaborations, and it's only two years that it's been in existence, so we're certainly in a growth mode. But the inspiration was really some of this molecular sleuthing work where I basically thought, well, for starters, it cannot be that there's only one molecular node, these TMED cargo receptors that we discovered there's got to be more, right? And so, there's a need to systematically go and find more nodes because obviously as anyone who works in rare genetic diseases will tell you, the problem for all of us is that we do what I call hand to hand combat. We start with the disease with one mutation, and we try to uncover the mechanism and then try to develop therapies, and that's wonderful.Anna Greka (08:33):But of course, it's slow, right? And if we consider the fact that there are 30 million patients in the United States in every state, everywhere in the country who suffer from a rare genetic disease, most of them, more than half of them are children, then we can appreciate the magnitude of the problem. Out of more than 8,000 genes that are involved in rare genetic diseases, we barely have something that looks like a therapy for maybe 500 of them. So there's a huge mismatch in the unmet need and magnitude of the problem. So the Ladders to Cures Accelerator is here to address this and to do this with the most modern tools available. And to your point, Eric, to bring patients along, not just as the recipients of whatever we discover, but also as partners in the research enterprise because it's really important to bring their perspectives and of course their partnerships in things like developing appropriate biomarkers, for example, for what we do down the road.Anna Greka (09:35):But from a fundamental scientific perspective, this is basically a project that aims to identify every opportunity for nodes, underlying all rare genetic diseases as quickly as possible. And this was one of the reasons I was there at the AI for Science Forum, because of course when one undertakes a project in which you're basically, this is what we're trying to do in the Ladders to Cures Accelerator, introduce dozens of thousands of missense and nonsense human mutations that cause genetic diseases, simultaneously introduce them into multiple human cells and then use modern scalable technology tools. Things like CRISPR screens, massively parallel CRISPR screens to try to interrogate all of these diseases in parallel, identify the nodes, and then develop of course therapeutic programs based on the discovery of these nodes. This is a massive data generation project that is much needed and in addition to the fact that it will help hopefully accelerate our approach to all rare diseases, genetic diseases. It is also a highly controlled cell perturbation dataset that will require the most modern tools in AI, not only to extract the data and understand the data of this dataset, but also because this, again, an extremely controlled, well controlled cell perturbation dataset can be used to train models, train AI models, so that in the future, and I hope this doesn't sound too futuristic, but I think that we're all aiming for that cell biologists for sure dream of this moment, I think when we can actually have in silico the opportunity to make predictions about what cell behaviors are going to look like based on a new perturbation that was not in the training set. So an experiment that hasn't yet been done on a cell, a perturbation that has not been made on a human cell, what if like a new drug, for example, or a new kind of perturbation, a new chemical perturbation, how would it affect the behavior of the cell? Can we make a predictive model for that? This doesn't exist today, but I think this is something, the cell prediction model is a big question for biology for the future. And so, I'm very energized by the opportunity to both address this problem of rare monogenic diseases that remains an unmet need and help as many patients as possible while at the same time advancing biology as much as we possibly can. So it's kind of like a win-win lifting all boats type of enterprise, hopefully.Eric Topol (12:11):Yeah. Well, there's many things to get to unpack what you've just been reviewing. So one thing for sure is that of these 8,000 monogenic diseases, they have relevance to the polygenic common diseases, of course. And then also the fact that the patient family advocates, they are great at scouring the world internet, finding more people, bringing together communities for each of these, as you point out aptly, these rare diseases cumulatively are high, very high proportion, 10% of Americans or more. So they're not so rare when you think about the overall.Anna Greka (12:52):Collectively.Help From the Virtual Cell?Eric Topol (12:53):Yeah. Now, and of course is this toxic proteinopathies, there's at least 50 of these and the point that people have been thinking until now that, oh, we found a mangled protein, but what you've zeroed in on is that, hey, you know what, it's not just a mangled protein, it's how it gets stuck in the cell and that it can't get to the lysosome to get rid of it, there's no waste system. And so, this is such fundamental work. Now that gets me to the virtual cell story, kind of what you're getting into. I just had a conversation with Charlotte Bunne and Steve Quake who published a paper in December on the virtual cell, and of course that's many years off, but of course it's a big, bold, ambitious project to be able to say, as you just summarized, if you had cells in silico and you could do perturbations in silico, and of course they were validated by actual experiments or bidirectionally the experiments, the real ones helped to validate the virtual cell, but then you could get a true acceleration of your understanding of cell biology, your field of course.Anna Greka (14:09):Exactly.Eric Topol (14:12):So what you described, is it the same as a virtual cell? Is it kind of a precursor to it? How do you conceive this because this is such a complex, I mean it's a fundamental unit of life, but it's also so much more complex than a protein or an RNA because not only all the things inside the cell, inside all these organelles and nucleus, but then there's all the outside interactions. So this is a bold challenge, right?Anna Greka (14:41):Oh my god, it's absolutely from a biologist perspective, it's the challenge of a generation for sure. We think taking humans to Mars, I mean that's an aspirational sort of big ambitious goal. I think this is the, if you will, the Mars shot for biology, being able to, whether the terminology, whether you call it a virtual cell. I like the idea of saying that to state it as a problem, the way that people who think about it from a mathematics perspective for example, would think about it. I think stating it as the cell prediction problem appeals to me because it actually forces us biologists to think about setting up the way that we would do these cell perturbation data sets, the way we would generate them to set them up to serve predictions. So for example, the way that I would think about this would be can I in the future have so much information about how cell perturbations work that I can train a model so that it can predict when I show it a picture of another cell under different conditions that it hasn't seen before, that it can still tell me, ah, this is a neuron in which you perturbed the mitochondria, for example, and now this is sort of the outcome that you would expect to see.Anna Greka (16:08):And so, to be able to have this ability to have a model that can have the ability to predict in silico what cells would look like after perturbation, I think that's sort of the way that I think about this problem. It is very far away from anything that exists today. But I think that the beginning starts, and this is one of the unique things about my institute, if I can say, we have a place where cell biologists, geneticists, mathematicians, machine learning experts, we all come together in the same place to really think and grapple with these problems. And of course we're very outward facing, interacting with scientists all across the world as well. But there's this sort of idea of bringing people into one institute where we can just think creatively about these big aspirational problems that we want to solve. I think this is one of the unique things about the ecosystem at the Broad Institute, which I'm proud to be a part of, and it is this kind of out of the box thinking that will hopefully get us to generate the kinds of data sets that will serve the needs of building these kinds of models with predictive capabilities down the road.Anna Greka (17:19):But as you astutely said, AlphaFold of course was based on the protein database existing, right? And that was a wealth of available information in which one could train models that would ultimately be predictive, as we have seen this miracle that Demi Hassabis and John Jumper have given to humanity, if you will.Anna Greka (17:42):But as Demis and John would also say, I believe is as I have discussed with them, in fact, the cell prediction problem is really a bigger problem because we do not have a protein data bank to go to right now, but we need to create it to generate these data. And so, my Ladders to Cures Accelerator is here to basically provide some part of the answer to that problem, create this kind of well-controlled database that we need for cell perturbations, while at the same time maximizing our learnings about these fully penetrant coding mutations and what their downstream sequelae would be in many different human cells. And so, in this way, I think we can both advance our knowledge about these monogenic diseases, build models, hopefully with predictive capabilities. And to your point, a lot of what we will learn about this biology, if we think that it involves 8,000 or more out of the 20,000 genes in our genome, it will of course serve our understanding of polygenic diseases ultimately as well as we go deeper into this biology and we look at the combinatorial aspects of what different mutations do to human cells. And so, it's a huge aspirational problem for a whole generation, but it's a good one to work on, I would say.Learning the Language of Life with A.I. Eric Topol (19:01):Oh, absolutely. Now I think you already mentioned something that's quite, well, two things from what you just touched on. One of course, how vital it is to have this inner or transdisciplinary capability because you do need expertise across these vital areas. But the convergence, I mean, I love your term nodal biology and the fact that there's all these diseases like you were talking about, they do converge and nodal is a good term to highlight that, but it's not. Of course, as you mentioned, we have genome editing which allows to look at lots of different genome perturbations, like the single letter change that you found in MUC1 pathogenic critical mutation. There's also the AI world which is blossoming like I've never seen. In fact, I had in Science this week about learning the language of life with AI and how there's been like 15 new foundation models, DNA, proteins, RNA, ligands, all their interactions and the beginning of the cell story too with the human cell.Eric Topol (20:14):So this is exploding. As you said, the expertise in computer science and then this whole idea that you could take these powerful tools and do as you said, which is the need to accelerate, we just can't sit around here when there's so much discovery work to be done with the scalability, even though it might take years to get to this artificial intelligence virtual cell, which I have to agree, everyone in biology would say that's the holy grail. And as you remember at our conference in London, Demi Hassabis said that's what we'd like to do now. So it has the attention of leaders in AI around the world, obviously in the science and the biomedical community like you and many others. So it is an extraordinary time where we just can't sit still with these tools that we have, right?Anna Greka (21:15):Absolutely. And I think this is going to be, you mentioned the ASCI presidency in the beginning of our call. This is going to be the president gets to give an address at the annual meeting in Chicago. This is going to be one of the points I make, no matter what field in biomedicine we're in, we live in, I believe, a golden era and we have so many tools available to us that we can really accelerate our ability to help more patients. And of course, this is our mandate, the most important stakeholders for everything that we do as physician-scientists are our patients ultimately. So I feel very hopeful for the future and our ability to use these tools and to really make good on the promise of research is a public good. And I really hope that we can advance our knowledge for the benefit of all. And this is really an exciting time, I think, to be in this field and hopefully for the younger colleagues a time to really get excited about getting in there and getting involved and asking the big questions.Career ReflectionsEric Topol (22:21):Well, you are the prototype for this and an inspiration to everyone really, I'm sure to your lab group, which you highlighted in the TED Talk and many other things that you do. Now I want to spend a little bit of time about your career. I think it's fascinating that you grew up in Greece and your father's a nephrologist and your mother's a pathologist. So you had two physicians to model, but I guess you decided to go after nephrology, which is an area in medicine that I kind of liken it to Rodney Dangerfield, he doesn't get any respect. You don't see many people that go into nephrology. But before we get to your decision to do that somehow or other you came from Greece to Harvard for your undergrad. How did you make that connect to start your college education? And then subsequently you of course you stayed in Boston, you've never left Boston, I think.Anna Greka (23:24):I never left. Yeah, this is coming into 31 years now in Boston.Anna Greka (23:29):Yeah, I started as a Harvard undergraduate and I'm now a full professor. It's kind of a long, but wonderful road. Well, actually I would credit my parents. You mentioned that my father, they're both physician-scientists. My father is now both retired, but my father is a nephrologist, and my mother is a pathologist, actually, they were both academics. And so, when we were very young, we lived in England when my parents were doing postdoctoral work. That was actually a wonderful gift that they gave me because I became bilingual. It was a very young age, and so that allowed me to have this advantage of being fluent in English. And then when we moved back to Greece where I grew up, I went to an American school. And from that time, this is actually an interesting story in itself. I'm very proud of this school.Anna Greka (24:22):It's called Anatolia, and it was founded by American missionaries from Williams College a long time ago, 150 and more years ago. But it is in Thessaloniki, Greece, which is my hometown, and it's a wonderful institution, which gave me a lot of gifts as well, preparing me for coming to college in the United States. And of course, I was a good student in high school, but what really was catalytic was that I was lucky enough to get a scholarship to go to Harvard. And that was really, you could say the catalyst that propelled me from a teenager who was dreaming about a career as a physician-scientist because I certainly was for as far back as I remember in fact. But then to make that a reality, I found myself on the Harvard campus initially for college, and then I was in the combined Harvard-MIT program for my MD PhD. And then I trained in Boston at Mass General in Brigham, and then sort of started my academic career. And that sort of brings us to today, but it is an unlikely story and one that I feel still very lucky and blessed to have had these opportunities. So for sure, it's been wonderful.Eric Topol (25:35):We're the ones lucky that you came here and set up shop and you did your productivity and discovery work and sleuthing has been incredible. But I do think it's interesting too, because when you did your PhD, it was in neuroscience.Anna Greka (25:52):Ah, yes. That's another.Eric Topol (25:54):And then you switch gears. So tell us about that?Anna Greka (25:57):This is interesting, and actually I encourage more colleagues to think about it this way. So I have always been driven by the science, and I think that it seems a little backward to some people, but I did my PhD in neuroscience because I was interested in understanding something about these ion channels that were newly discovered at the time, and they were most highly expressed in the brain. So here I was doing work in the brain in the neuroscience program at Harvard, but then once I completed my PhD and I was in the middle of my residency training actually at Mass General, I distinctly remember that there was a paper that came out that implicated the same family of ion channels that I had spent my time understanding in the brain. It turned out to be a channelopathy that causes kidney disease.Anna Greka (26:43):So that was the light bulb, and it made me realize that maybe what I really wanted to do is just follow this thread. And my scientific curiosity basically led me into studying the kidney and then it seemed practical therefore to get done with my clinical training as efficiently as possible. So I finished residency, I did nephrology training, and then there I was in the lab trying to understand the biology around this channelopathy. And that sort of led us into the early projects in my young lab. And in fact, it's interesting we didn't talk about that work, but that work in itself actually has made it all the way to phase II trials in patients. This was a paper we published in Science in 2017 and follow onto that work, there was an opportunity to build this into a real drug targeting one of these ion channels that has made it into phase II trials. And we'll see what happens next. But it's this idea of following your scientific curiosity, which I also talked about in my TED Talk, because you don't know to what wonderful places it will lead you. And quite interestingly now my lab is back into studying familial Alzheimer's and retinitis pigmentosa in the eye in brain. So I tell people, do not limit yourself to whatever someone says your field is or should be. Just follow your scientific curiosity and usually that takes you to a lot more interesting places. And so, that's certainly been a theme from my career, I would say.Eric Topol (28:14):No, I think that's perfect. Curiosity driven science is not the term. You often hear hypothesis driven or now with AI you hear more AI exploratory science. But no, that's great. Now I want to get a little back to the AI story because it's so fascinating. You use lots of different types of AI such as cellular imaging would be fusion models and drug discovery. I mean, you've had drug discovery for different pathways. You mentioned of course the ion channel and then also as we touched on with your Cell paper, the whole idea of targeting the cargo receptor with a small molecule and then things in between. You discussed this of course at the London panel, but maybe you just give us the skinny on the different ways that you incorporate AI in the state-of-the-art science that you're doing?Anna Greka (29:17):Sure, yeah, thank you. I think there are many ways in which even for quite a long time before AI became such a well-known kind of household term, if you will, the concept of machine learning in terms of image processing is something that has been around for some time. And so, this is actually a form of AI that we use in order to process millions of images. My lab has by produced probably more than 20 million images over the last few years, maybe five to six years. And so, if you can imagine it's impossible for any human to process this many images and make sense of them. So of course, we've been using machine learning that is becoming increasingly more and more sophisticated and advanced in terms of being able to do analysis of images, which is a lot of what we cell biologists do, of course.Anna Greka (30:06):And so, there's multiple different kinds of perturbations that we do to cells, whether we're using CRISPR or base editing to make, for example, genome wide or genome scale perturbations or small molecules as we have done as well in the past. These are all ways in which we are then using machine learning to read out the effects in images of cells that we're looking at. So that's one way in which machine learning is used in our daily work, of course, because we study misshape and mangled proteins and how they are recognized by these cargo receptors. We also use AlphaFold pretty much every day in my lab. And this has been catalytic for us as a tool because we really are able to accelerate our discoveries in ways that were even just three or four years ago, completely impossible. So it's been incredible to see how the young people in my lab are just so excited to use these tools and they're becoming extremely savvy in using these tools.Anna Greka (31:06):Of course, this is a new generation of scientists, and so we use AlphaFold all the time. And this also has a lot of implications of course for some of the interventions that we might think about. So where in this cargo receptor complex that we study for example, might we be able to fit a drug that would disrupt the complex and lead the cargo tracks into the lysosome for degradation, for example. So there's many ways in which AI can be used for all of these functions. So I would say that if we were to organize our thinking around it, one way to think about the use of machine learning AI is around what I would call understanding biology in cells and what in sort of more kind of drug discovery terms you would call target identification, trying to understand the things that we might want to intervene on in order to have a benefit for disease.Anna Greka (31:59):So target ID is one area in which I think machine learning and AI will have a catalytic effect as they already are. The other of course, is in the actual development of the appropriate drugs in a rational way. So rational drug design is incredibly enabled by AlphaFold and all these advances in terms of understanding protein structures and how to fit drugs into them of all different modalities and kinds. And I think an area that we are not yet harnessing in my group, but I think the Ladders to Cures Accelerator hopes to build on is really patient data. I think that there's a lot of opportunity for AI to be used to make sense of medical records for example and how we extract information that would tell us that this cohort of patients is a better cohort to enroll in your trial versus another. There are many ways in which we can make use of these tools. Not all of them are there yet, but I think it's an exciting time for being involved in this kind of work.Eric Topol (32:58):Oh, no question. Now it must be tough when you know the mechanism of these families disease and you even have a drug candidate, but that it takes so long to go from that to helping these families. And what are your thoughts about that, I mean, are you thinking also about genome editing for some of these diseases or are you thinking to go through the route of here's a small molecule, here's the tox data in animal models and here's phase I and on and on. Where do you think because when you know so much and then these people are suffering, how do you bridge that gap?Anna Greka (33:39):Yeah, I think that's an excellent question. Of course, having patients as our partners in our research is incredible as a way for us to understand the disease, to build biomarkers, but it is also exactly creating this kind of emotional conflict, if you will, because of course, to me, honesty is the best policy, if you will. And so, I'm always very honest with patients and their families. I welcome them to the lab so they can see just how long it takes to get some of these things done. Even today with all the tools that we have, of course there are certain things that are still quite slow to do. And even if you have a perfect drug that looks like it fits into the right pocket, there may still be some toxicity, there may be other setbacks. And so, I try to be very honest with patients about the road that we're on. The small molecule path for the toxic proteinopathies is on its way now.Anna Greka (34:34):It's partnered with a pharmaceutical company, so it's on its way hopefully to patients. Of course, again, this is an unpredictable road. Things can happen as you very well know, but I'm at least glad that it's sort of making its way there. But to your point, and I'm in an institute where CRISPR was discovered, and base editing and prime editing were discovered by my colleagues here. So we are in fact looking at every other modality that could help with these diseases. We have several hurdles to overcome because in contrast to the liver and the brain, the kidney for example, is not an organ in which you can easily deliver nucleic acid therapies, but we're making progress. I have a whole subgroup within the bigger group who's focusing on this. It's actually organized in a way where they're running kind of independently from the cell biology group that I run.Anna Greka (35:31):And it's headed by a person who came from industry so that she has the opportunity to really drive the project the way that it would be run milestone driven, if you will, in a way that it would be run as a therapeutics program. And we're really trying to go after all kinds of different nucleic acid therapies that would target the mutations themselves rather than the cargo receptors. And so, there's ASO and siRNA technologies and then also actual gene editing technologies that we are investigating. But I would say that some of them are closer than others. And again, to your question about patients, I tell them honestly when a project looks to be more promising, and I also tell them when a project looks to have hurdles and that it will take long and that sometimes I just don't know how long it will take before we can get there. The only thing that I can promise patients in any of our projects, whether it's Alzheimer's, blindness, kidney disease, all I can promise is that we're working the hardest we possibly can on the problem.Anna Greka (36:34):And I think that is often reassuring I have found to patients, and it's best to be honest about the fact that these things take a long time, but I do think that they find it reassuring that someone is on it essentially, and that there will be some progress as we move forward. And we've made progress in the very first discovery that came out of my lab. As I mentioned to you, we've made it all the way to phase II trials. So I have seen the trajectory be realized, and I'm eager to make it happen again and again as many times as I can within my career to help as many people as possible.The Paucity of Physician-ScientistsEric Topol (37:13):I have no doubts that you'll be doing this many times in your career. No, there's no question about it. It's extraordinary actually. There's a couple of things there I want to pick up on. Physician-scientists, as you know, are a rarefied species. And you have actually so nicely told the story about when you have a physician-scientist, you're caring for the patients that you're researching, which is, most of the time we have scientists. Nothing wrong with them of course, but you have this hinge point, which is really important because you're really hearing the stories and experiencing the patients and as you say, communicating about the likelihood of being able to come up with a treatment or the progress. What are we going to do to get more physician-scientists? Because this is a huge problem, it has been for decades, but the numbers just keep going lower and lower.Anna Greka (38:15):I think you're absolutely right. And this is again, something that in my leadership of the ASCI I have made sort of a cornerstone of our efforts. I think that it has been well-documented as a problem. I think that the pressures of modern clinical care are really antithetical to the needs of research, protected time to really be able to think and be creative and even have the funding available to be able to pursue one's program. I think those pressures are becoming so heavy for investigators that many of them kind of choose one or the other route most often the clinical route because that tends to be, of course where they can support their families better. And so, this has been kind of the conundrum in some ways that we take our best and brightest medical students who are interested in investigation, we train them and invest in them in becoming physician-scientists, but then we sort of drop them at the most vulnerable time, which is usually after one completes their clinical and scientific training.Anna Greka (39:24):And they're embarking on early phases of one's careers. It has been found to be a very vulnerable point when a lot of people are now in their mid-thirties or even late thirties perhaps with some family to take care of other burdens of adulthood, if you will. And I think what it becomes very difficult to sustain a career where one salary is very limited due to the research component. And so, I think we have to invest in our youngest people, and it is a real issue that there's no good mechanism to do that at the present time. So I was actually really hoping that there would be an opportunity with leadership at the NIH to really think about this. It's also been discussed at the level of the National Academy of Medicine where I had some role in discussing the recent report that they put out on the biomedical enterprise in the United States. And it's kind of interesting to see that there is a note made there about this issue and the fact that there needs to be, I think, more generous investment in the careers of a few select physician-scientists that we can support. So if you look at the numbers, currently out of the entire physician workforce, a physician-scientist comprised of less than 1%.Anna Greka (40:45):It's probably closer to 0.8% at this point.Eric Topol (40:46):No, it's incredible.Anna Greka (40:48):So that's really not enough, I think, to maintain the enterprise and if you will, this incredible innovation economy that the United States has had this miracle engine, if you will, in biomedicine that has been fueled in large part by physician investigators. Of course, our colleagues who are non-physician investigators are equally important partners in this journey. But we do need a few of the physician-scientists investigators I think as well, if you really think about the fact that I think 70% of people who run R&D programs in all the big pharmaceutical companies are physician-scientists. And so, we need people like us to be able to work on these big problems. And so, more investment, I think that the government, the NIH has a role to play there of course. And this is important from both an economic perspective, a competition perspective with other nations around the world who are actually heavily investing in the physician-scientist workforce.Anna Greka (41:51):And I think it's also important to do so through our smaller scale efforts at the ASCI. So one of the things that I have been involved in as a council member and now as president is the creation of an awards program for those early career investigators. So we call them the Emerging-Generation Awards, and we also have the Young Physician-Scientist Awards. And these are really to recognize people who are making that transition from being kind of a trainee and a postdoc and have finished their clinical training into becoming an independent assistant professor. And so, those are small awards, but they're kind of a symbolic tap on the shoulder, if you will, that the ASCI sees you, you're talented, stay the course. We want you to become a future member. Don't give up and please keep on fighting. I think that can take us only so far.Anna Greka (42:45):I mean, unless there's a real investment, of course still it will be hard to maintain people in the pipeline. But this is just one way in which we have tried to, these programs that the ASCI offers have been very successful over the last few years. We create a cohort of investigators who are clearly recognized by members of the ASCI is being promising young colleagues. And we give them longitudinal training as part of a cohort where they learn about how to write a grant, how to write a paper, leadership skills, how to run a lab. And they're sort of like a buddy system as well. So they know that they're in it together rather than feeling isolated and struggling to get their careers going. And so, we've seen a lot of success. One way that we measure that is conversion into an ASCI membership. And so, we're encouraged by that, and we hope that the program can continue. And of course, as president, I'm going to be fundraising for that as well, it's part of the role. But it is a really worthy cause because to your point, we have to somehow make sure that our younger colleagues stay the course that we can at least maintain, if not bolster our numbers within the scientific workforce.Eric Topol (43:57):Well, you outlined some really nice strategies and plans. It's a formidable challenge, of course. And we'd like to see billions of dollars to support this. And maybe someday we will because as you say, if we could relieve the financial concerns of people who have curiosity driven ideas.Anna Greka (44:18):Exactly.Eric Topol (44:19):We could do a lot to replenish and build a big physician-scientist workforce. Now, the last thing I want to get to, is you have great communication skills. Obviously, anybody who is listening or watching this.Eric Topol (44:36):Which is another really important part of being a scientist, no less a physician or the hybrid of the two. But I wanted to just go to the backstory because your TED Talk, which has been watched by hundreds of thousands of people, and I'm sure there's hundreds of thousands more that will watch it, but the TED organization is famous for making people come to the place a week ahead. This is Vancouver used to be in LA or Los Angeles area and making them rehearse the talk, rehearse, rehearse, rehearse, which seems crazy. You could train the people there, how to give a talk. Did you have to go through that?Anna Greka (45:21):Not really. I did rehearse once on stage before I actually delivered the talk live. And I was very encouraged by the fact that the TED folks who are of course very well calibrated, said just like that. It's great, just like that.Eric Topol (45:37):That says a lot because a lot of people that do these talks, they have to do it 10 times. So that kind of was another metric. But what I don't like about that is it just because these people almost have to memorize their talks from giving it so much and all this coaching, it comes across kind of stilted and unnatural, and you're just a natural great communicator added to all your other things.Anna Greka (46:03):I think it's interesting. Actually, I would say, if I may, that I credit, of course, I actually think that it's important, for us physician-scientists, again, science and research is a public good, and being able to communicate to the public what it is that we do, I think is kind of an obligation for the fact that we are funded by the public to do this kind of work. And so, I think that's important. And I always wanted to cultivate those communication skills for the benefit of communicating simply and clearly what it is that we do in our labs. But also, I would say as part of my story, I mentioned that I had the opportunity to attend a special school growing up in Greece, Anatolia, which was an American school. One of the interesting things about that is that there was an oratory competition.Anna Greka (46:50):I got very early exposure entering that competition. And if you won the first prize, it was in the kind of ancient Rome way, first among equals, right? And so, that was the prize. And I was lucky to have this early exposure. This is when I was 14, 15, 16 years old, that I was training to give these oratory speeches in front of an audience and sort of compete with other kids who were doing the same. I think these are just wonderful gifts that a school can give a student that have stayed with me for life. And I think that that's a wonderful, yeah, I credit that experience for a lot of my subsequent capabilities in this area.Eric Topol (47:40):Oh, that's fantastic. Well, this has been such an enjoyable conversation, Anna. Did I miss anything that we need to bring up, or do you think we have it covered?Anna Greka (47:50):Not at all. No, this was wonderful, and I thoroughly enjoyed it as well. I'm very honored seeing how many other incredible colleagues you've had on the show. It's just a great honor to be a part of this. So thank you for having me.Eric Topol (48:05):Well, you really are such a great inspiration to all of us in the biomedical community, and we'll be cheering for your continued success and thanks so much for joining today, and I look forward to the next time we get a chance to visit.Anna Greka (48:20):Absolutely. Thank you, Eric.**************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. And such support is becoming more vital In light of current changes of funding and support for biomedical research at NIH and other US governmental agencies.Thanks to my producer Jessica Nguyen and to Sinjun Balabanoff for audio and video support at Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

Huntington’s disease was long thought to be caused by the slow buildup of a toxic protein, but new research has revealed that it’s actually driven by the expansion of a gene that, at a certain length, triggers quick neuron death. In this week’s episode of The Top Line, we hear from Steven McCarroll, Ph.D., a Huntington’s disease researcher at the Broad Institute of MIT and Harvard, whose team recently published that research in the journal Cell. McCarroll joins Fierce Biotech’s Darren Incorvaia to dig into the findings, which not only change our understanding of the disease itself, but also open up new avenues for potential treatments. To learn more about the topics in this episode: New findings shed light on cause of Huntington's disease progression Scientists look to survival secrets of plants for Huntington's treatments Scientists use long-approved GSK HIV drug to stave off dementia, Huntington's proteins in mice Sage drops dalzanemdor as Huntington's failure completes clean sweep of midphase flops See omnystudio.com/listener for privacy information.

Life's Chemistry Unleashed- Ambient Nature Series: A Remixed Interview with Dr. Todd Golub (Harvard/ MIT)---In this ambient nature sounds remixed episode, we present an interview with Dr. Todd Golub , M.D., Core Institute Member, Chief Scientific Officer, Director of the Cancer Program, The Broad Institute of Harvard and MIT. --Disclaimer: The views of this podcast represent those of my guests and I, and these ideas do not constitute medical or professional suggestions, advice or recommendations. Please see the relevant medical professionals for medical or professional recommendations, suggestions or advice.

Meredith sits down with Dr. Steven Hyman – a brain expert at The Broad Institute – for a talk about the meaning of change … and whether it's actually possible. Are people capable of change – in life and relationships? If so, how? Dr. Hyman explains the plastic brain, how it works, how eating a great sandwich in Chicago can be the best thing ever, and why Meredith once thought she looked like Reese Witherspoon. We also catch up with a very special Love Letters couple. Learn more about your ad choices. Visit megaphone.fm/adchoices

Scientists from Northwestern Medicine, the Broad Institute of MIT and Harvard have uncovered the first rare genetic disorder linked to a long non-coding RNA gene. In this episode, Gemma Carvill, PhD, explains how this discovery, published in The New England Journal of Medicine, came to be and the critical roles non-coding regions of the genome may have in human health. 

2025 promises to be another busy year for intellectual property law. In this episode of IP Talk with Wolf Greenfield, you'll hear Wolf Greenfield attorneys from a variety of practice areas reviewing some of the top issues of 2024 and offering their insights on what to expect in the months ahead. Here are some of the highlights:01:02 - Chelsea Loughran's thoughts on The University of California v. Broad Institute, a federal court case involving competing patent applications for the CRISPR-Cas9 gene-editing system02:25 - Zach Piccolomini is watching the Unified Patent Court (UPC) for upcoming decisions in the standard essential patent space and “fair, reasonable and non-discriminatory” royalties03:34 - Jonathan Roses with insight on recent Orange Book developments and what to expect with the new administration  05:31 - Jen Wang offers advice for dealing with rejections in the wake of the Federal Circuit overturning a 40-year-old obviousness test for design patents in the LKQ v. GM case06:34 - Scott McKeown on noteworthy 2024 developments at the USPTO and some thoughts for what might happen in 2025 08:43 - John Strand on the Dewberry case, which was just argued before the Supreme Court (a decision is expected in the spring)10:23 - Gabe McCool discusses the BIO Secure Act  

CancerNetwork, in a partnership with KidneyCAN, spoke with 2 genitourinary oncologists, Elizabeth P. Henske, MD, and Jason Muhitch, PhD, about how advocacy and funding through interdisciplinary collaboration between patient advocates, researchers, and physicians have resulted in numerous clinical breakthroughs in kidney cancer.  Henske is a professor of medicine at the Harvard Medical School, an associate member of the Broad Institute of MIT and Harvard, director of the Center for LAM Research and Clinical Care, and a physician at Brigham and Women's Hospital and Dana-Farber Cancer Institute. Muhitch is an associate professor of Oncology, co-chair of the Genitourinary Translational Research Group, deputy director of Graduate Studies, and a member of the Department of Immunology at Roswell Park Comprehensive Cancer Center at Roswell Park Comprehensive Cancer Center. First, the state of kidney cancer advocacy was discussed, with Muhitch emphasizing multidisciplinary collaboration and the role of conferences, such as the Kidney Cancer Research Summit (KCRS) and the International Kidney Cancer Symposium, in bringing these groups together. Henske emphasized the strength of the advocacy network for kidney cancer, particularly as a mechanism for exchanging information, offering patient support and education, and facilitating research. Next, funding was touched upon, with Henske expressing her interest in conveying the importance and urgency of kidney cancer research to Congress. Muhitch agreed, suggesting that the meetings with congressional offices offer opportunities to explain how funding can impact kidney cancer outcomes and scientist training, as well as the strength of patient advocacy in influencing Congress. Muhitch and Henske then discussed the Kidney Cancer Research Program, which has enabled significant increases in funding for kidney cancer research, helped to facilitate clinical breakthroughs for common kidney cancer types, and set a foundation for exploring different kidney cancer variants. The discussion then turned to encouraging research for renal cell carcinoma , which Muhitch expressed can be attributed to partnership award recipients who went on to conduct research evaluating biomarkers predictive of patient responses to immunotherapies.  Regarding additional funding mechanisms, Henske and Muhitch discussed numerous private foundations providing smaller research grants. Henske explained that these smaller grants are instrumental in taking the first steps to explore rarer kidney cancer subtypes, with Muhitch explaining that the earlier funding can formulate research that leads to greater funding from the Kidney Cancer Research Program. KidneyCAN is a nonprofit organization with a mission to accelerate cures for kidney cancer through education, advocacy, and research funding. Learn more about KidneyCAN's mission and work here.

One of the most mysterious and frightening entities in medicine are prion diseases — rare neurodegenerative disorders that are usually infectious in nature but involve not bacteria or viruses, but proteins. Prions are misfolded proteins that can induce normal proteins to become misfolded as well, resulting in a chain reaction that leads to irreversible brain damage and death. What makes prions alarming is that they are incurable, can incubate for decades in a person's brain without symptoms, and are usually associated with 100% mortality within months to a few years. Sonia Vallabh, PhD was a recently-married lawyer in her early career when she witnessed her mother's baffling sudden health decline and death. Her mother was ferried from hospital to hospital, yet dozens of doctors could not figure out why she was seemingly succumbing to rapidly progressive dementia at the age of 52. It wasn't until after her death that Vallabh discovered the cause was a genetic prion disease. Subsequent testing revealed that Sonia Vallabh herself had inherited the same genetic abnormality. Determined to find a solution, Vallabh and her husband Eric, a transportation engineer, decided to retrain as biomedical scientists in a race to cure her before it grew too late. The couple now leads a prion research lab at the Broad Institute at MIT and Harvard. They are also the co-founders of the nonprofit Prion Alliance. Over the course of our conversation, Vallabh opens up about what it was like to accompany her mother in her last months of life, the psychological toll of dealing with a fatal medical mystery, how she lives each day with an awareness of how ephemeral life is, what prion diseases are and what makes them so difficult to treat, what makes her optimistic about the future of her work, and more. In this episode, you'll hear about: 3:23 - Vallabh's early memories of her mother and the devastating experience that overcame her at 52 years old16:37 - The process of grieving the loss a parent22:32 - What prion diseases are25:35 - How Vallabh made the decision to undergo the genetic testing that confirmed she inherited a mutation thah causes prion disease 36:27 - Vallabh's major career change to become biomedical researchers 45:50 - Where the quest for an effective therapy for prion disease currently stands 52:08 - Vallabh's message to listeners on how to approach life View Sonia Vallabh's TED Talk on her quest to cure prion disease. Visit our website www.TheDoctorsArt.com where you can find transcripts of all episodes.If you enjoyed this episode, please subscribe, rate, and review our show, available for free on Spotify, Apple Podcasts, or wherever you get your podcasts. If you know of a doctor, patient, or anyone working in health care who would love to explore meaning in medicine with us on the show, feel free to leave a suggestion in the comments or send an email to info@thedoctorsart.com.Copyright The Doctor's Art Podcast 2024

Marty Chavez is computer scientist, entrepreneur, partner and vice chairman of Sixth Street Partners, board member at Alphabet Inc, The Broad Institute of MIT and Harvard, and the Stanford Medicine Board of Fellows. And that just scratches the surface. In addition to all of his work, and what brings him to Thanks for Rolling is his love of Jiu Jitsu.  We were lucky enough to have Marty give us some of his time to talk about his journey, his passion for jiu jitsu, along with his philosophies for making time for the things you love. This is a really fun conversation with a fascinating guest. Thank you Marty! Marty's Website: www.rmartinchavez.com 10th Planet Springfield: www.10thplanetspringfieldma.com The music on Thanks for Rolling is performed by Future Vision/FineTune Music and is licensed through Adobe Stock with code: ASLC-1936E775-D86B800A95

Talking media startups, news deserts and the future of the Fourth Estate This conversation took place on Tuesday, October 15 at the Roux Institute at Northeastern University. Authors Dan Kennedy and Ellen Clegg sat down for a conversation about their book What Works in Community News: Media Startups, News Deserts, and the Future of the Fourth Estate (Beacon Press) at the Roux Institute at Northeastern University on Tuesday, October 15. Local news is essential to democracy. Meaningful participation in civic life is impossible without it. However, local news is in crisis. According to one widely cited study, some 2,500 newspapers have closed over the last generation. And it is often marginalized communities of color who have been left without the day-to-day journalism they need to govern themselves in a democracy. In this book, journalists Ellen Clegg and Dan Kennedy cut through the pessimism surrounding this issue, showing readers that new, innovative journalism models are popping up across the country to fill news deserts and empower communities. Through a blend of on-the-ground reporting and interviews, Clegg and Kennedy show how these operations found seed money and support, and how they hired staff, forged their missions, and navigated challenges from the pandemic to police intimidation to stand as the last bastion of collective truth—and keep local news in local hands.   Dan Kennedy Dan Kennedy is a professor of journalism in the College of Arts, Media and Design and a nationally known media commentator. Professor Kennedy teaches news reporting, opinion writing, media ethics, and other journalism courses with an emphasis on how technology is changing the business of news. He has also been published in The Washington Post, The Boston Globe, Nieman Lab, Nieman Reports, Poynter Online, and other venues. Ellen Clegg Ellen Clegg spent more than 3 decades at The Boston Globe and retired in 2018 after 4 years of running the opinion pages. In between stints at the Globe, she was deputy director of communications at the Broad Institute of MIT and Harvard. She is a member of the steering committee for the Elizabeth Neuffer Fellowship at the International Women's Media Foundation. Ellen is co-founder and co-chair of Brookline.News, a nonprofit startup news organization in Brookline, Massachusetts.

The fifth Season of the National Institute of Neurological Disorders and Stroke's Building Up the Nerve podcast, where we help you strengthen your science communication skills with tools and advice to use throughout your career. We know that navigating your career can be daunting, but we're here to help—it's our job!In the third episode of the season, we talk about Collaborating with Partners in Research, focusing on how to best learn from and include the perspectives of non-scientists and persons with lived experience in research and science.Featuring Alice S. Chen-Plotkin, MD, Professor at the University of Pennsylvania, Director of the MIND Initiative, and Neurologist at Hospital of the University of Pennsylvania; Sonia Vallabh, PhD, Senior Group Leader at Broad Institute of MIT and Harvard, and Assistant Professor of Neurology at Massachusetts General Hospital, and Olajide Williams, MD, Vice Dean of Community Health & Professor at Columbia University Vagelos College of Physicians & Surgeons. ResourcesHip Hop Public Health: https://www.hhph.org/ The Tipping Point by Malcom GladwellInternational Youth Neuroscience Association: https://www.youthneuro.org/Hybrid Workshop: Advances in Therapeutics Development for Parkinson's Disease co-chaired by Dr. Alice Chen-PlotkinCureFFI.org by Dr. Eric Vallabh MinikelHDBuzz: Huntington's disease research newsABRCMS: Annual Biomedical Research Conference for Minoritized Scientists: https://abrcms.org/  Transcript available at http://ninds.buzzsprout.com/.

It's always interesting to think about how many technologies that we take for granted today, such as genome sequencing and bioengineering, were completely unheard of just a few decades ago. This episode, we're going back to the roots of these applications with Dr. James Collins, who is widely regarded as one of the founders of the field of synthetic biology. Tune in to hear his thoughts on the importance of leveraging the tools we have today for pandemic prevention and readiness, how to reframe thinking around biosecurity when it comes to applications of these technologies, and how AI can help us outrun antibiotic resistance with rapid drug discovery.Dr. James Collins is a professor of medical engineering and science at MIT, a core faculty member at the Wyss Institute of Harvard University, and a member of the Broad Institute of MIT and Harvard. He is a member of the National Academies of Engineering, Science, and Medicine, and his technologies have been licensed by over 25 biotech and medical device companies.

HOST: Hildy Grossman, CO-HOST: Jordan Rich GUESTS: Jaime Cheah, Ph.D., Director of Collaborative Screening in the Center for the Development of Therapeutics at the Broad Institute and Jane Wilkinson, co-founder and president of CANCollaborate Our guests, Jane Wilkinson and Jaime Cheah join the conversation to tell us about some incredible work to expand the use … Continue reading Everything Old Is New Again! Imagine This: Repurposing Existing Drugs For New Uses →

Dans cet épisode, nous parlons de l'impact de l'intelligence artificielle et de la blockchain sur le secteur financier. Nous abordons les cas d'usage, les défis de performance et l'importance de la décentralisation. On évoque aussi les questions éthiques et la gouvernance de l'IA, ainsi que les incentives économiques pour un usage responsable. Nous discutons également de l'impact de ces technologies sur la recherche médicale, la finance et la gestion de la désinformation. On aborde la croissance de notre communauté, l'innovation autour des modèles de langage et les effets de l'IA sur l'emploi. Les risques des deepfakes et l'importance de vérifier l'authenticité des contenus via la blockchain sont aussi au programme. Enfin, nous parlons d'investissements, de projets innovants, et du rôle de la culture dans la résolution des problèmes d'information et d'identité.

When I think of digital biology, I think of Patrick Hsu—he's the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley.Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Here's the transcript with links to the audio and external links to relevant papers and things we discussed.Eric Topol (00:06):Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick.Patrick Hsu (00:29):Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric.The Arc InstituteEric Topol (00:33):Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going.Patrick Hsu (01:05):Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives.(02:05):One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs.Patrick, Patrick and SilvanaEric Topol (02:52):So the two Patrick's. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing?Patrick Hsu (03:08):Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That's a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused.Eric Topol (04:58):Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right?Patrick Hsu (05:14):I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today.Eric Topol (06:07):Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow?Patrick Hsu (06:24):So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin.New Models of Funding Life ScienceEric Topol (06:42):That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they're both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference.Patrick Hsu (07:26):Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century.(08:38):And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together.(09:42):You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what we try to do at Arc is think about a model that you might liken more to overlapping Viking shields where there's sort of five core areas that we're deeply investing in, in genetics and genomics, computation, neuroscience, immunology and chemical biology. Now we really think of these as five areas that are maybe the minimal critical mass that you would need to make a dent on something as complicated as complex human diseases. It's certainly not the only thing that you need, but we needed a critical mass of investigators working at least in these areas.Eric Topol (11:05):Well, yeah, and they really converge on where the hottest advances are being made these days. Now can you work at Arc Institute without being one of these three universities or is it really that you maintain your faculty and your part of this other entity?Patrick Hsu (11:24):So we have a few elements to even just the academic side of the house. We have our core investigators. I'm one of them, where we have dually appointed faculty who retain their latter rank or tenured appointment in their home department, but their labs are physically cited at the Arc headquarters where we built out a lab in Stanford Research Park in Palo Alto. And so, folks move their labs there. They continue to train graduate students based on whatever graduate programs they're formally affiliated with through their university affiliation. And so, we have nearly 40 PhD students across our labs that are training on site every day.(12:03):So in addition to our core investigators, we also have what we call our innovation investigators, which is more of a grant program to faculty at our partner universities. They receive unrestricted funding from us to seed a new project or accelerate an existing area in their group and their labs stay at their home campus and they just get that funding to augment their work. The third way is our technical staff model where folks basically just come work at Arc and many of them also are establishing their own research groups focusing on technology R&D areas. And so, we have five of those technology centers working in molecular engineering, multi-omics, complex cellular models, in vivo models, and in machine learning.Discovery of Bridge RNAsEric Topol (12:54):Yeah, that's a great structure. In fact, just a few months ago, Patrick Collison, the other Patrick came to Stanford HAI where I'm on the board and you've summarized it really well and it's very different than the other models and other entities, companies included that you mentioned. It's really very impressive. Now speaking of impressive on June 26, this past few months ago, which incidentally is coincident with the draft genome in the year 2000, the human sequence. You and your colleagues, perhaps the most impressive jump in terms of an Arc Institute contribution published two papers back-to-back in Nature about bridge RNA: [Bridge RNAs direct programmable recombination of target and donor DNA] and [Structural mechanism of bridge RNA-guided recombination.] And before I get you to describe this breakthrough in genome editing, some would call it genome editing 3.0 or CRISPR 3.0, whatever. But what we have today in the clinic with the approval of CRISPR 1.0 for sickle cell and thalassemia is actually quite crude. I think most people will know it's just a double stranded DNA cleavage with all sorts of issues about repair and it's not very precise. And so, CRISPR 2.0 is supposed to be represented by David Liu's contributions and his efforts at Broad like prime and base editing and then comes yours. So maybe you can tell us about it and how it is has to be viewed as quite an important advance.Patrick Hsu (14:39):The first thing I would say before CRISPR, is that we had RNA interference. And so, even before this modern genome editing revolution with programmable CRISPRs, we had this technology that had a lot of the core selling points as well. Any target will now become druggable to us. We simply need to reprogram a guide RNA and we can get genetic access to things that are intracellular. And I think both the discovery of RNA interference by Craig Mello and Andy Fire or the invention or discovery of programmable CRISPR technologies, both depend on the same fundamental biological mechanism. These non-coding guide RNAs that are essentially a short RNA search string that you can easily reprogram to retarget a desired enzyme function, and natively both RNAi and CRISPR are molecular scissors. Their RNA or DNA nucleases that can be reprogrammed to different regions of the genome or the transcriptome to make a cut.(15:48):And as bioengineers, we have come up with all kinds of creative ways to leverage the ability to make site specific cuts to do all kinds of incredible things including genome editing or beyond transcriptional up or down regulation, molecular imaging and so on and so forth. And so, the first thing that we started thinking about in our lab was, why would mother nature have stopped only RNAi and CRISPR? There probably are lots of other non-coding RNAs out there that might be able to be programmable and if they did exist, they probably also do more complicated and interesting things than just guide a molecular scissors. So that was sort of the first core kind of intuition that we had. The second intuition that we had on the technology side, I was just wearing my biology hat, I'll put on my technology hat, is the thing that we call genome editing today hardly involves the genome.(16:50):It's really you're making a cut to change an individual base or an individual gene or locus. So really you're doing small scale single locus editing, so you might call it gene level or locus level cuts. And what you really want to be able to do is do things at the genome scale at 100 kb, a megabase at the chromosome scale. And I think that's where I think the field will inevitably go if you follow the technology curves of longer and longer range gene sequencing, longer and longer range gene synthesis, and then longer and longer range gene editing. And so, what would that look like? And we started thinking, could there be essentially recombination technologies that allow you to do cut and paste in a single step. Now, the reason for that is the way that we do gene editing today involves a cut and then a multi-step process of cellular DNA repair that resolves the cut to make the exertion or the error prone deletion or the modification that ends up happening.(17:59):And so, it's very complicated and whether that's nucleases or base or prime editing, you're all generally limited to the small-scale single locus changes. However, there are natural mechanisms that have solved this cut and paste problem, right? There are these viruses or bacterial versions of viruses known as phage that have generally been trying to exert their multi kilobase genomes into bacterial hosts and specialize throughout billions of years. So our core thought was, well, if there are these new non-coding RNAs, what kind of functions would we be excited about? Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms? They're incredibly widespread. Transposons are thought to be some of the most diverse enzyme mechanisms found in nature. And so, we started computationally by asking ourselves a very simple question. If a mobile element inserts itself into foreign DNA and it's able to somehow be programmable, presumably the inside or something encoded in the inside of the element is predictive of some sequence on the outside of the element.(19:15):And so, that was the core insight we took, and we thought let's look across the boundaries of many different mobile genetic elements and we zoomed in on a particular sub family of these MGE known as insertion sequence (IS) elements which are the most autonomous minimal transposons. Normally transposons have all kinds of genes that they use to hitchhike around the genomic galaxy and endow the bacterial host with some fitness advantage like some ability to metabolize some copper and some host or some metal. And these IS elements have only the enzymes that they need to jump around. And if you identify the boundaries of these using modern computational methods, this is actually a really non-trivial problem. But if you solve that problem to figure out with nucleotide resolution where the element boundaries end and then you look for the open reading frame of the transposases enzyme inside of this element, you'll find that it's not just that coding sequence.(20:19):There are also these non-coding flanks inside of the element boundaries. And when we looked across the non-coding, the entire IS family tree, there are hundreds of these different types of elements. We found that this particular family IS110, had the longest non-coding ends of all IS elements. And we started doing experiments in the lab to try to figure out how these work. And what we found was that these elements are cut and paste elements, so they excise themselves into a circular form and paste themselves back in into a target site linearly. But the circularization of this element brings together two distal ends together, which brings together a -35 and a -10 box that create and reconstitute a canonical bacterial transcriptional promoter. This essentially is like plugging a plug into an electrical socket in the wall and it jacks up transcription. Now you would think this transcription would turn on the transposase enzyme so it can jump around more but it transcribes a non-coding RNA out of this non-coding end.(21:30):We're like, holy crap, are these RNAs actually involved in regulating the transposon? Now the boring answer would be, oh, it regulates the expression. It's like an antisense regulate or something. The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase. So we started zooming in bound dramatically on this and we undertook a covariation analysis where we were able to show that this cryptic non-coding RNA has a totally novel guide RNA structure, totally distinct from RNAi or CRISPR guide RNAs. And it had a target site that covaried with the target site of the element. And so we're like, oh wow, this could be a programmable transposase. The second thing that we found was even more surprising, there was a second region of complementarity in that same RNA that recognized the donor sequence, which is the circularized element itself. And so, this was the first example of a bispecific guide RNA, and also the first example of RNA guided self-recognition by a mobile genetic element.Eric Topol (22:39):It's pretty extraordinary because basically you did a systematic assessment of jumping genes or transposons and you found that they contain things that previously were not at all recognized. And then you have a way to program these to edit, change the genome without having to do any cuts or nicks, right?Patrick Hsu (23:05):Yeah. So what we showed in a test tube is when we took this, so-called bridge RNA, which we named because it bridges the target and donor together along with the recombinase enzyme. So the two component system, those are the only two things that you need. They're able to cut and paste DNA and recombine them in a test tube without any DNA repair, meaning that it's independent of cellular DNA repair and it does strand nicking, exchange, junction resolution and religation all in a single mechanism. So that's when we got super excited about its potential applications as bioengineering tool.Eric Topol (23:46):Yeah, it's pretty extraordinary. And have you already gone into in vivo assessment?Patrick Hsu (23:54):Yes, in our initial set of papers, what we showed is that these are programmable and functional or recombinases in a test tube and in bacterial cells. And by reprogramming the target and donor the right way, you can use these enzymes not just for insertion, but also for flipping and cutting out DNA. And so, we actually have in a single mechanism the ability to do bridge editing, if you will, for universal DNA recombination, insertion, excision or inversion, similar to what folks have been doing for decades with Cre recombinase, but with fully programmable recognition sequences. The work that we're doing now in the lab as you can imagine is to adapt these into robust tools for mammalian genome editing, including of course, human genomes. We're excited about this, we're making good progress. The CRISPR has had thousands of labs over the last 10, 15 years working on it to make these therapeutic level potency and selectivity. We're going to work and follow that same blueprint for getting bridge systems to get to that level of performance, but we're on the path and we're very optimistic for the future.Exemplar of Digital BiologyEric Topol (25:13):Yeah, I think it's quite extraordinary and it's a whole different look to what we've been seeing in the CRISPR era for over the past decade and how that's been advancing and getting more specific and less need for repair and being able to be more versatile. But this takes it to yet another dimension. Now, this brings me to the field that when I think of this term digital biology, I think of you and now our mutual acquaintance, Jensen Huang, who everybody knows now. Back some months ago, he wrote and said at a conference, “Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There's no question that digital biology is going to be it. For the first time in human history, biology has the opportunity to be engineering, not science.” So can you critique Jensen? Is he right? And tell us how you conceive the field of digital biology.Patrick Hsu (26:20):If you look at gene therapy today, the core concepts are actually remarkably simple. They're elegant. Of course, you're missing a broken gene, you need to put it back. And that can be curative. Very simple, powerful concept. However, for complex diseases where you don't have just a single gene that goes wrong, in many cases we actually have no idea what to do. And in fact, when you're trying to put in DNA, that's over more than a gene scale. We kind of very quickly run out of ideas. Is it a CAR and a cytokine, a CAR and a cytokine and another thing? And then we're kind of out of ideas. And so, we started thinking in the lab, how can we actually design genomes where it's not just let's reduce the genome into individual Lego blocks, iGem style with promoters and different genes that we just sort of shuffle the Lego blocks around, but actually use AI to design genome sequences.(27:29):So to do that, we thought we would have to first of all, train a model that can learn and decode the foreign language of biology and use that in order to design sequences. And so, we sort of have been training DNA foundation models and virtual cell models at Arc, sort of a major effort of ours where the first thing that we tried was to take a variance of transformer architecture that's used to train ChatGPT from OpenAI, but instead apply this to study the next DNA token, right? Now, the interesting thing about next token prediction in English is that you can actually learn a surprising amount of information by just predicting the next word. You can learn world knowledge is the capital of Azerbaijan, is it Baku or is it London, right? Or if you're walking around in the kitchen, then the next text is, I then left the kitchen or the bathroom, right?(28:33):Now you're learning about spatial reasoning, and so you can also learn translation obviously. And so similarly, I think predicting the next token or the next base and DNA can lead you to learn about molecular biochemistry, is the next amino acid residue, hydrophobic or hydrophilic. And it can teach you about the mechanics of some catalytic binding pocket or something. You can learn about a disease mutation. Is the next base, the sick linked base or the wild type base and so on and so forth. And what we found was that at massive scale, DNA foundation models learn about molecular function, not just at the DNA level, but also at the RNA and the protein. And indeed, we could use these to design molecular systems like CRISPR-Cas systems, where you have a protein and the guide RNA. It could also design new DNA transposons, and we could design sequences that look plausibly like real genomes, where we generate a megabase a million bases of continuous genome sequence. And it really looks and feels like it could be a blurry picture of something that you would actually sequence. This has been a wonderful collaboration with Brian Hie, a PI at Stanford and an Arc investigator, and we're really excited about what we've seen in this work because it promises the better performance with even more scale. And so, simply by scaling up these models, by adding in more compute, more training data or more powerful models, they're going to get sharper and sharper.New A.I. Models in Life ScienceEric Topol (30:25):Yeah. Well, this whole use of large language models for the language of life, whether it's the genome proteins and on and on, actually RNA and even cells has really taken root. And of course, this is really one of the foundations of that field of digital biology, which brings together generative AI, AI tools and trying to push forward our understanding in biology. And also, obviously what's been emphasized in drug discovery, perhaps it's been emphasized even too much because we still have a lot to learn about biology, but that gets me to these models. Like today, AlphaProteo was announced by DeepMind, as we all know, AlphaFold 1, 2, now 3. They were kind of precursors of being able to predict proteins from amino acid 3D structure. And that kind of took the field by a little bit like ChatGPT for life science, but now it's a new model all the time. So you've been working on various models and Arc Institute, how do you see this unfolding? Are we just going to have every aspect of the language of life being approached in all the different interactions? And this is going to help us get to a much more deep level of understanding.Patrick Hsu (31:56):I'll say two things. The first is a lot of models that you just described are what I would call task specific models. A model for de novo design of a binder, a model for protein structure prediction. And there are other models for protein fitness or for RNA structure prediction, et cetera, et cetera. And I think what we're going to move towards are more unifying models where there's different classes of models at different levels of scale. So we will have these atomic level models for looking at generative chemistry or ligand docking. We have models that can unify genomes and their molecules, and then we have models that can unify cells and tissues. And so, for example, if you took an H&E stain of some liver, there are folks building models where you can then predict what the single cell spatial transcriptome will look like of that model. And that's obviously operating at a very different level of abstraction than a de novo protein binder. But in the long run, all of these are going to get, I think unified. I think the reason why this is possible is that biology, unlike physics, actually has this unifying theory of evolution that runs across all of its length scales from atomic, molecular, cellular, organismal to entire ecosystem. And the promise of these models is no short then to make biology a predictive discipline.Patrick Hsu (33:37):In physics, the experimentalists win the big prizes for the theorists when they measure gravitational waves or whatever. But in biology, we're very practical people. You do something three times and do a T-test. And I think my prediction is we can actually gauge the success of these LLMs or whatever in biology by how much we respect theory in this field.The A.I. ScientistEric Topol (34:05):Yeah. Well, that's a really interesting perspective, an important perspective because the proliferation of models, which we're going to get into not just doing the things that you described, but also being able to be “pseudo” scientists, the so-called AI scientist. Maybe you could comment about that concept because that's been the idea that everything from the question that could be asked to the hypothesis and the experiment design and the analysis of data and then the feedback. So what is the role of the scientists, that seems to have been overplayed? And maybe you can put that in context.Patrick Hsu (34:48):So yeah, right now there's a lot of excitement that we can use AI agents not just to do software enterprise workflows, but to be a research assistant. And then over time, itself an autonomous research scientist that can read the literature, come up with an idea, maybe run a bunch of robots in the lab or do a bunch of computational analyses and then potentially even analyze data, conclude what is going on and actually write an entire paper. Now, I think the vision of this is compelling in the long term. I think the question is really about timescale. If you break down the scientific method into its constituent parts, like hypothesis generation, doing an experiment, analyzing experiment and iterating, we're clearly going to use AI of some kind at every single step of this cycle. I think different steps will require different levels of maturity. The way that I would liken this is just wet lab automation, folks have dreamed about having pipetting robots that just do their western blots and do their cell culture for them for generations.(36:01):But of course, today they don't actually really feel fundamentally different from the same ones that we had in the 90s, let's say. Right? And so, obviously they're getting better, but it seems to me one of the trends I'm very bullish about is the explosion of humanoid robots and robot foundation models that have a world model and a sense of physics and proportionate space loaded onto them. Within five years, we're going to have home robots that can fold your clothes, that can organize your kitchen and do all of this while you're sleeping, so you wake up to a clean home every day.Eric Topol (36:40):It's not going to be just Roomba anymore. There's going to be a lot more, but it isn't just the hardware, it's also the agents playing in software, right?Patrick Hsu (36:50):It's the integrated loop of the hardware and the software where the ability to make the same machine generally intelligent will make it adaptable to a broad array of tasks. Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab, where instead of creating a centrifuge or a new type of pipetter that's optimized for your Beckman or Hamilton device, instead you just have robot arms that you snap onto the edge of the bench and then they just work alongside you. And I do think that's coming, although it'll take a lot of hardware and software and computer vision engineering to make that possible.A Sense of HumorEric Topol (37:32):Yeah, and I think also going back to originating the question, there still is quite a debate about the creativity and the lack of any simulation of AGI, whatever that means anymore. And so, the human in the loop part of this is obviously I think it's still of critical nature. Now, the other thing I learned about you is you have a great sense of humor, which is really important by the way. And recently, which is great that you're active on X or Twitter because that's one way we get to see what you're thinking on a day-to-day basis. But I think you put out a poll which was really quite provocative , and it was about, here's what it said, “do more people in the world *truly* understand transformers or health insurance?” And interestingly, you got 49% for transformers at 51% for health insurance. Can you tell us what you're thinking when you put that poll together? Because obviously a lot of people don't understand either of these.Patrick Hsu (38:44):I think the core question is, there are different ways of looking at the world, some of which are very bottom up and some of which are very top down. And one of the very surprising things about transformers is they're taking something that is in principle, an incredibly simple task, which is if you have a string of text, what is the next letter? And somehow at massive, massive scale, you can unlock something that looks an awful lot like reasoning, and you've got these emergent behaviors. Now the bottoms up theory of just the linear algebra that's going on in these models couldn't possibly really help us predict that we have these emerging capabilities. And I think similarly in healthcare, there's a literal set of parts that are operating in some complex way that at massive scale becomes this incredibly confusing and dynamic system for how we can actually incentivize how we make medicines, how we actually take care of people, and how we actually pay for any of this from an economic point of view. And so, I think it was, in some sense if transformers can actually be an explainable by just linear algebra equations, maybe there will be a way to decompose the seemingly incredibly confusing world of healthcare in order to actually build a better way forward.Computing Power and the GPU Arms RaceEric Topol (40:12):Yeah. Well that's great. Now the other thing I wanted to ask you about, we open source and the arms race of GPUs and this whole kind of idea is you touched on the need for coalescing a lot of these tools to exploit the synergy. But we have an issue because many academic labs like here at Scripps Research and so many others, including as I learned even at Stanford, have limited access to GPUs. So computing power of large language models is a problem. And then the models that exist today that can be adopted like Llama or others, and they're somewhat limited. And then we also have a movement towards trying to make things more open source, like for example, recently OpenCRISPR with Profluent Bio that is basically trying to use AI for CRISPR guides. And so, how do you deal with this arms race, computing power, open source, proprietary models that are not easily accessible without a lot of resources?Patrick Hsu (41:30):So the first thing I would say is, we are in the academic science sphere really unprepared for the level of resources that are required for doing this type of cutting edge computational work. There are top Stanford computer science professors or computational researchers who have a single GPU in their office, and that's actually what their whole lab runs off of.(41:58):The UC Berkeley campus, the grid runs on something like 12 megawatts of power and how are they going to build an on-premises GPU clusters, like a central question that can scale across the entire needs? And these are two of the top computer science universities in the world. And so, I think one of our kind of core beliefs at Arc is, as science both experimentally and computationally has gotten incredibly complex, not just in terms of conceptually, but also just the actual infrastructure and machines and know-how that you need to do things. We actually need to essentially support this. So we have a private GPU cloud that we use to train our models, and we have access to significantly large clusters for large burst kind of train outs as necessary. And I think infrastructurally for running genomics experiments or doing scalable brain organoid screens, right, we're also building out the infrastructure to support that experimentally.Eric Topol (43:01):Yeah, no, I think this is one of the advantages of the new model like the Arc Institute because not many centers have that type of plasticity with access to computing power when needed. So that's where a brilliant mind you and the Arc Institute together makes for a formidable recipe for future advances and of course building on the ones you've already accomplished.The Primacy of Human TalentPatrick Hsu (43:35):I would just say, my main skill, if I have one, is to recruit really, really smart people. And so, everything that you're seeing and hearing about is the work of unbelievable colleagues who are curious, passionate, and incredible scientists.Eric Topol (43:53):But it also takes the person who can judge those who are in that category set as a role model. And you're certainly doing that. I guess just in closing, I mean, it's just such a delight to get to meet you here and kind of get your thoughts on what is the hottest thing in life science without question, which brings together the fields of AI and what's going on, not just obviously in genome editing, but this digital biology era that we're still in the early phases of, I mean, I think you could say that it's just going to continue to accelerate the exponential curve. We're still kind of on the bottom of that, I would imagine where we're headed. Any other things that you want to bring up that I haven't touched on that will round out this conversation?Patrick Hsu (44:50):I mean, I think it's very early days here at Arc.Patrick Hsu (44:53):When we founded Arc, we asked ourselves, how do we measure success? We don't have customers or revenue in the way that a typical startup does. And we felt sort of three things. The first was research institutes live and die by their talent. Can we actually hire incredible people when we make offers to people we want to come, do they come? The second was, when those folks do come to Arc, do they feel like they're able to work on important research programs that they couldn't do sort of at their prior university or company? And then longer term, the third thing was, and there's just no shortcut around this, you need to do important work. And I think we've been really excited that there are early signs that we're able to do all three of these things, and we're still, again, just following the same scaling laws that we're seeing in natural language and vision, but for the domain of biology. And so, we're excited about what's ahead and think if there are folks who are interested in learning more about Arc, just shoot me an email or DM.Eric Topol (46:07):Yeah, well I would just say, congratulations on what you've already achieved. I know you're going to keep rocking it because you already have in a short time. And for anybody who doesn't know about Arc Institute and your work and your team, I hope this is going to be putting them on notice actually what can be accomplished outside of the usual NIH funded model, which is kind of a risk-free zone where you basically have to have your results nailed down before you send in your proposal frequently, and it doesn't do great things for young people. Really, I think you actually qualify in that demographic where it's hard for them to break in for getting NIH grants and also for this type of work that you're doing. So we'll look for the next bridge beyond bridge RNAs of your just fantastic efforts. So Patrick, thanks so much for joining us today, and we'll be checking back with you and following all the great work that you'll be doing in the times ahead.Patrick Hsu (47:14):Thanks so much, Eric. It was such a pleasure to be here today. Appreciate the opportunity.*******************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informative!Voluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly help fund our summer internship programs.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Note: you can select preferences to receive emails about newsletters, podcasts, or all I don't want to bother you with an email for content that you're not interested in. Get full access to Ground Truths at erictopol.substack.com/subscribe

Good morning from Pharma and Biotech daily: the podcast that gives you only what's important to hear in Pharma e Biotech world.This week's commercialization news includes updates on Zepbound supply, Enjaymo's new home, and expanding access to HIV drugs. Medicare has tweaked rules for drug price talks, while GSK reports that its RSV vaccine protects against disease over three seasons. GSK's Viiv plans to expand the supply of HIV drugs in Africa, and Sanofi's rare disease drug finds a new home at Recordati. Other news includes a protein prediction winning the Chemistry Nobel and Alnylam submitting an important drug application. Trends suggest that biosimilars may make a mark in 2025, with incentives favoring them in the Medicare market. Protein prediction wins the Chemistry Nobel Prize, Alnylam submits a crucial drug application, Lilly partners with AI specialist Insitro to develop metabolic medicines, and Purespring raises $105 million for gene therapy for kidney disease. AI startup Basecamp allies with The Broad Institute to create 'programmable' genetic medicines. Additionally, Lilly appoints Mount Sinai scientist Thomas Fuchs as its first Chief AI Officer to lead AI initiatives in drug discovery and clinical trials. Other news includes J&J closing a cancer study, Alnylam seeking approval for a potential blockbuster drug, and Propharma receiving a regulatory and compliance award at CPhI.The Supreme Court declined to review a Texas abortion case related to emergency care, a blow to the Biden administration's efforts. A survey found that nearly 70% of healthcare organizations affected by cyberattacks experienced disruptions in patient care. Steward Health Care is auctioning off assets, including closing Norwood Hospital in Massachusetts. Baxter reported no structural damage at their North Carolina site affected by Hurricane Helene. The payer-provider relationship in healthcare is becoming more complex with consolidation and value-based care, leading to tensions over reimbursement and access.Kezar's lupus trial has been put on hold after four patient deaths, making it a potential buyout target. Investor Kevin Tang is interested in acquisition. Big pharma is also investing in cell and gene therapies, with companies like Lilly and Sanofi making moves in the industry. The Genscript Biotech Global Forum 2025 is coming up on January 15, offering a platform to discuss innovations and challenges in gene and cell therapy. Additionally, Lilly's obesity clinical program faces challenges, while Stealth's ultrarare disease candidate may not meet approval standards.Eli Lilly's obesity program is highlighted as a key factor in the company's dominance in the industry, with CEO David Ricks confident in their position. Wuxi Biologics faces uncertainty in the U.S. after setbacks, while big pharma companies show growing interest in cell and gene therapy. Five radiopharma biotechs are identified as potential buyout targets, and Trilink Biotechnologies introduces custom sets of mRNA for screening studies. Other news includes increased investment in cell and gene therapy, activist investor Starboard's stake in Pfizer, and Merck's success with Keytruda in head and neck cancer. AstraZeneca puts $2 billion towards heart disease drugs, Sanofi offloads a rare autoimmune drug, and AbbVie trims earnings guidance.

Send us a textAbout the guest: Austin Argentieri, PhD is a researcher in the Analytic & Translational Genetics Unit at the Massachusetts General Hospital with academic appointments at Harvard & the Broad Institute. His research focuses on large-scale analyses to to understand human aging.Episode summary: Nick and Dr. Argentieri discuss: chronological vs. biological age; DNA methylation and aging clocks; proteomics and protein measurements in aging research; health, longevity, and human healthspan; and more.Related episodes:Aging, mTOR, Sirtuins, Rapamycin, Metformin, the Truth of Resveratrol & Longevity Supplements, David Sinclair & Anti-Aging Myths | Matt Kaeberlein | #151Cellular Aging, Taurine, Nutrition, Senescence, Longevity, Mitochondria, Metabolism | Vijay Yadav | #122*This content is never meant to serve as medical advice.Support the Show.All episodes (audio & video), show notes, transcripts, and more at the M&M Substack Try Athletic Greens: Comprehensive & convenient daily nutrition. Free 1-year supply of vitamin D with purchase.Try SiPhox Health—Affordable, at-home bloodwork w/ a comprehensive set of key health marker. Use code TRIKOMES for a 10% discount.Try the Lumen device to optimize your metabolism for weight loss or athletic performance. Use code MIND for 10% off.Learn all the ways you can support my efforts

Welcome to Alternative Dog Moms - a podcast about what's happening in the fresh food community and the pet industry. Kimberly Gauthier is the blogger behind Keep the Tail Wagging, and Erin Scott hosts the Believe in Dog podcast.CHAPTERS:00:00 - Introduction01:56 - Blue zones for pets04:13 - A dog's health is 20% genetic / 80% environmental07:02 - Reaction medicine vs. proactive medicine10:07 - Dr. Karen Becker's fresh food journey13:52 - The Forever Dog Life recipes23:05 - Where do pet parents start (with the book)?27:35 - Ovary sparing/vasectomy techniques33:03 - Helping our dogs get a diverse gut microbiomeLINKS DISCUSSED:The Forever Dog (https://amzn.to/477d3XH)The Forever Dog Life (https://amzn.to/4dXnsr8)Blue Zones for people (https://www.bluezones.com/)Broad Institute at MIT (https://www.broadinstitute.org/)Dr. Barbara Royal on The Alternative Dog Moms (https://pod.link/1616283441/episode/dd39bcc698fcae8e4e14dd05cdddb6dc)The Alternative Dog Moms discuss finding a Board Certified Veterinary Nutrition (https://pod.link/1616283441/episode/788949852fa7b039152eb5fefca65fce)Dr. Michelle Kutzler's research on spay/neuter (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222805/)WSAVA updated spay/neuter guidelines (https://wsava.org/global-guidelines/reproduction-guidelines/)Animal Biome's State of the Gut Report (https://animalbiome.com/state-of-the-gut-2024)OUR BLOG/PODCASTS...Kimberly: Keep the Tail Wagging, KeepTheTailWagging.comErin Scott: Believe in Dog podcast, BelieveInDogPodcast.comFACEBOOK...Keep the Tail Wagging, Facebook.com/KeepTheTailWaggingBelieve in Dog Podcast, Facebook.com/BelieveInDogPodcastINSTAGRAM...Keep the Tail Wagging, Instagram.com/RawFeederLifeBelieve in Dog Podcast, Instagram.com/Erin_The_Dog_MomThanks for listening to our podcast. You can learn more about Erin Scott's first podcast at BelieveInDogPodcast.com. And you can learn more about raw feeding, raising dogs naturally, and Kimberly's dogs at KeepTheTailWagging.com. And don't forget to subscribe to The Alternative Dog Moms.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Things I learned talking to the new breed of scientific institution, published by Abhishaike Mahajan on August 30, 2024 on LessWrong. Note: this article is sponsored by and cross-posted to the Good Science Project. They also write a fair bit, and their articles were essential reading for writing this essay! Also, this article would not be possible without the hours of discussion/editing help I've had with several people from these institutions, and a few outside of them. Huge shout-out to all of them! Introduction Arcadia Science, Speculative Technologies, FutureHouse, Arc, and Convergent. All of these are a new form of scientific institute. Most are funded entirely by a few billionaires. Most are non-profits. Most of them focus on the life-sciences. Most of them have sprung up in just the last few years. They do all also have one common thread: a grand statement. We are an experiment in a new way to do science. And they are! Traditionally, research is conducted in academic or private industry labs - dependent on NIH grants in the former and markets in the latter. Given the (often singular) sources of no-strings-attached funding, these new institutions need not satisfy either the NIH or the markets, allowing them to conduct research in a unique fashion. In one sense, the experimental aspect of these institutions revolves around the focus of the research itself, addressing fields or using methods that the founders - correctly or not - view as underserved/underutilized. But, on a more subtle level, the experimental aspect could be more closely tied to the culture of these organizations. Institutions like Arcadia, FutureHouse, and the rest could be viewed as the production of auteurs - a term from filmmaking for films with such a heavy sense of the director's personal taste that the film is inseparable from the director. This is where the novelty within these institutions primarily lie, in how the founders of the institute wish science was conducted. And wielding billions of dollars, thousands of hours of work, and hundreds of scientists as a means to test whether their theories are correct. Of course, nothing under the sun is truly new. There is an age-old history of scientist dissatisfaction with how 'things are traditionally done', and confidently building new institutions to solve the problems they've seen. Many of these are now household names amongst researchers: Broad Institute, Whitehead Institute, Max Planck Society, Howard Hughes Medical Institute (HHMI), and so on. Each of these were started with similar contrarian mentalities as the current era of institutions. Some of these were more experimental than others, most notably HHMI, which prized itself on its focus on interdisciplinary research above all else. But all were experiments, many of them extraordinarily successful. Yet, the current iteration of new research institutes is still arguably more experimental than its ancestors. While the last generation of institutes was typically tied directly to universities, the current era of ones (outside of Arc) are independent, allowing them a larger sense of opinionation on how science should be done. But, despite this experimentation, there is relatively little information out there on what's going on inside them. Not in terms of science, but more-so the vibes. While aspects of these organizations have been written about previously, such as in articles in The Atlantic and Endpoints, they aren't assessing vibes! These other articles are, first and foremost, news-pieces; valuable, but lack any opinionated observations on the inner-workings of the institutions. Nadia Asparouhova's essay on the subject comes closest to this regarding the history of these institutions, but still few details on how they practically function. This essay attempts to discuss that missing s...

In an era defined by rapid technological advancements and pressing global health challenges, leaders like Seema Kumar stand at the forefront of innovation, wielding their expertise to forge transformative paths in healthcare. As CEO of Cure and with a distinguished career spanning Johnson & Johnson, and more, Seema Kumar embodies a commitment to integrating cutting-edge innovation with compassionate healthcare solutions. Join us as we delve into her insights on bridging innovation and healthcare to drive meaningful global impact. [02:01] - About Seema Kumar Seema is the CEO at Cure.  She has spent 20 years at Johnson & Johnson and has a background spanning NIH, Broad Institute, Whitehead Institute, and Hopkins. --- Support this podcast: https://podcasters.spotify.com/pod/show/tbcy/support

Dr. James Fanale worked in healthcare for nearly five decades, but never reallyknew what it meant to be a patient until he was told he had cancer - Stage 4 lungcancer. James was president and CEO of Care New England, retiring in 2022, shortly after being diagnosed. Jim typically ran the Falmouth Road Race and insists on being able to continuerunning. Since his cancer diagnosis, Jim has focused on his personal life. He's retired and planning trips with his wife, Deb. Jim is in the middle of writing a book called “Onward,” which focuses on the empathy in medicine and the emotional weight of the journey of patients and their caregivers. All the proceeds will be donated to a new “caregivers fund” at the Dana-Farber. Dr. Barbie is the Director of the Lowe Center for Thoracic Oncology atDana-Farber Cancer Institute and an Associate Professor of Medicine at HarvardMedical School. He is also Associate Director of the Belfer Center for Applied Cancer Science and an Associate Member of the Broad Institute. According to the American Cancer Society, there will be an estimated 234,580 new cases of lung cancer in the United States for 2024. Lung cancer is by far the leading cause of cancer death in the U.S., accounting for about 1 in 5 of all cancer deaths. Each year, more people die of lung cancer than of colon, breast, and prostate cancers combined. On a positive note, the number of new lung cancer cases continues to decrease, partly because more people are quitting smoking (or not starting). The number of lung cancer deaths continue to drop as well due to fewer people smoking and advances in early detection and treatment, according to the American Cancer Society.

Dr. Hahn is the William Rosenberg Professor of Medicine in the Department of Medical Oncology at Dana-Farber Cancer Institute and Harvard Medical School and an Institute Member of the Broad Institute of MIT and Harvard. He serves as an Executive Vice President and the Chief Operating Officer at the Dana-Farber Cancer Institute. Dr. Hahn has made numerous discoveries that have informed our currentmolecular understanding of cancer. His laboratory has pioneered the use of integrated functional genomic approaches to identify and validate cancer targets. The tools, models and approaches that his laboratory has developed are widely used worldwide to discover and validate molecularly targeted cancer therapies. Dr. Hahn has served as the President of the American Society for ClinicalInvestigation and has been elected to the Association of American Physicians and the National Academy of Medicine. Dr. Hahn has been the recipient of many honors and awards including the Wilson S. Stone Award from M.D. Anderson Cancer Center for outstanding research in cancer (2000), a Howard Temin Award from the National Cancer Institute (2001), the Ho-Am Prize in Medicine (2010), the Richard and Hinda Rosenthal Award from AACR (2015) and the Claire and Richard Morse Award (2019). He has been elected to the American Society of Clinical Investigation, American Association of Physicians, the National Academy of Inventors and the National Academy of Medicine.

Your gut microbiome consists of trillions of microbiota and is a critical health determinant, affecting your immune system, mood, energy level, and much more. As a scientific field, microbiome research is new to the scene, but the intricate relationship between our gut and our overall health is clear – and getting clearer. In April, Netflix started streaming Hack Your Health, an informative documentary about the gut microbiome, gut health, and the science of eating. In this collaborative event between Town Hall Seattle and the Institute for Systems Biology, Hack Your Health Director Anjali Nayar will sit down with gut microbiome specialist Dr. Sean Gibbons, a scientific advisor on the film, to discuss the project, interesting developments in microbiome research, and much more. Anjali Nayar is an Indian-Canadian director, former climate scientist, and tech founder. Anjali's newest film, a Netflix Original called Hack Your Health: The Secrets of Your Gut is streaming on Netflix, and her fantasy short Closer has over 5 million views and won the 2022 Prism Prize Audience Award (Canada's top music video awards). As of 2024, she is developing a slew of scripted projects and a series with the Golden State Warriors. Her prior films have been supported by Cinereach, Sundance, and Tribeca, won countless awards, jury prizes, gone theatrical, and been acquired by Netflix and Amazon. Sean Gibbons, Ph.D., is associate professor at Institute for Systems Biology. He received his PhD in biophysical sciences from the University of Chicago in 2015, winning a prestigious EPA STAR Graduate Fellowship. He completed his postdoctoral training in the Department of Biological Engineering at MIT and The Broad Institute in 2018. He joined ISB as Washington Research Foundation Distinguished Investigator and assistant professor in 2018. His research on the human microbiome has been published in top scientific journals, including Nature, Science, and Cell. Presented by Town Hall Seattle and the Institute for Systems Biology.  

Pradeep is a brilliant geneticist and Director of Preventive Cardiology, holds the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine at Mass General Hospital and on faculty at Harvard Medical School and the Broad Institute. His prolific research has been illuminating for the field of improving our approach to reduce the risk of heart disease. That's especially important because heart disease is the global (and US) #1 killer and is on the increase. We didn't get into lifestyle factors here since there was so much ground to cover on new tests. drugs, and strategies.A video snippet of our conversation on ApoB. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Transcript with links to key publications and audioEric Topol (00:06):Well, welcome to Ground Truths. I'm Eric Topol and with me is Pradeep Natarajan from Harvard. He's Director of Preventative Cardiology at the Mass General Brigham Health System and he has been lighting it up on the field of cardiovascular. We're going to get to lots of different parts of that story and so, Pradeep welcome.Pradeep Natarajan (00:31):Thanks Eric, really delighted and honored to be with you and have this discussion.Eric Topol (00:36):Well, for years I've been admiring your work and it's just accelerating and so there's so many things to get to. I thought maybe what we'd start off with is you recently wrote a New England Journal piece about two trials, two different drugs that could change the landscape of cardiovascular prevention in the future. I mean, that's one of the themes we're going to get to today is all these different markers and drugs that will change cardiology as we know it now. So maybe you could just give us a skinny on that New England Journal piece.Two New Lipid Targets With RNA DrugsPradeep Natarajan (01:16):Yeah, yeah, so these two agents, the trials were published at the same time. These phase two clinical trials for plozasiran, which is an siRNA against APOC3 and zodasiran, which is an siRNA against ANGPTL3. The reason why we have medicines against those targets are based on human genetics observations, that individuals with loss of function mutations and either of those genes have reduced lipids. For APOC3, it's reduced triglycerides for ANGPTL3 reduced LDL cholesterol and reduced triglycerides and also individuals that have those loss of function mutations also have lower risk for coronary artery disease. Now that's a very similar parallel to PCSK9. We have successful medicines that treat that target because people have found that carriers of loss of function mutations in PCSK9 lead to lower LDL cholesterol and lower coronary artery disease.(02:11):Now that suggests that therapeutic manipulation without significant side effects from the agents themselves for APOC3 and ANGPTL3 would be anticipated to also lower coronary artery disease risk potentially in complementary pathways to PCSK9. The interesting thing with those observations is that they all came from rare loss of function mutations that are enriched in populations of individuals. However, at least for PCSK9, has been demonstrated to have efficacy in large groups of individuals across different communities. So the theme of that piece was really just the need to study diverse populations because those insights are not always predictable about which communities are going to have those loss of function mutations and when you find them, they often have profound insights across much larger groups of individuals.Eric Topol (03:02):Well, there's a lot there that we can unpack a bit of it. One of them is the use of small interfering RNAs (siRNA) as drugs. We saw in the field of PCSK9, as you mentioned. First there were monoclonal antibodies directed against this target and then more recently, there's inclisiran which isn't an RNA play if you will, where you only have to take it twice a year and supposedly it's less expensive and I'm still having trouble in my practice getting patients covered on their insurance even though it's cheaper and much more convenient. But nonetheless, now we're seeing these RNA drugs and maybe you could comment about that part and then also the surprise that perhaps is unexplained is the glucose elevation.Pradeep Natarajan (03:53):Yeah, so for medicines and targets that have been discovered through human genetics, those I think are attractive for genetic-based therapies and longer interval dosing for the therapies, which is what siRNAs allow you to do because the individuals that have these perturbations, basically the naturally occurring loss of function mutations, they have these lifelong, so basically have had a one-time therapy and have lived, and so far, at least for these targets, have not had untoward side effects or untoward phenotypic consequences and only reduce lipids and reduce coronary artery disease. And so, instead of taking a pill daily, if we have conviction that that long amount of suppression may be beneficial, then longer interval dosing and not worrying about the pill burden is very attractive specifically for those specific therapeutics. And as you know, people continue to innovate on further prolonging as it relates to PCSK9.(04:57):Separately, some folks are also developing pills because many people do feel that there's still a market and comfort for daily pills. Now interestingly for the siRNA for zodasiran at the highest dose, actually for both of them at the highest doses, but particularly for zodasiran, there was an increase in insulin resistance parameters actually as it relates to hyperglycemia and less so as it relates to insulin resistance, that is not predicted based on the human genetics. Individuals with loss of function mutations do not have increased risks in hyperglycemia or type 2 diabetes, so that isolates it related to that specific platform or that specific technology. Now inclisiran, as you'd mentioned, Eric is out there. That's an siRNA against PCSK9 that's made by a different manufacturer. So far, the clinical trials have not shown hyperglycemia or type 2 diabetes as it relates inclisiran, so it may be related to the specific siRNAs that are used for those targets. That does merit further consideration. Now, the doses that the manufacturers do plan to use in the phase three clinical trials are at lower doses where there was not an increase in hyperglycemia, but that does merit further investigation to really understand why that's the case. Is that an expected generalized effect for siRNAs? Is it related to siRNAs for this specific target or is it just related to the platform used for these two agents which are made by the same manufacturer?Eric Topol (06:27):Right, and I think the fact that it's a mystery is intriguing at the least, and it may not come up at the doses that are used in the trials, but the fact that it did crop up at high doses is unexpected. Now that is part of a much bigger story is that up until now our armamentarium has been statins and ezetimibe to treat lipids, but it's rapidly expanding Lp(a), which for decades as a cardiologist we had nothing to offer. There may even be drugs to be able to lower people who are at high risk with high Lp(a). Maybe you could discuss that.What About Lp(a)?Pradeep Natarajan (07:13):Yeah, I mean, Eric, as you know, Lp(a) has been described as a cardiovascular disease risk factors for quite so many years and there are assays to detect lipoprotein(a) elevation and have been in widespread clinical practice increasing widespread clinical practice, but we don't yet have approved therapies. However, there is an abundance of literature preclinical data that suggests that it likely is a causal factor, meaning that if you lower lipoprotein(a) when elevated, you would reduce the risk related to lipoprotein(a). And a lot of this comes from similar human genetic studies. The major challenge of just relating a biomarker to an outcome is there are many different reasons why a biomarker might be elevated, and so if you detect a signal that correlates a biomarker, a concentration to a clinical outcome, it could be related to that biomarker, but it could be to the other reasons that the biomarker is elevated and sometimes it relates to the outcome itself.(08:10):Now human genetics is very attractive because if you find alleles that strongly relate to that exposure, you can test those alleles themselves with the clinical outcome. Now the allele assignment is established at birth. No other factor is going to change that assignment after conception, and so that provides a robust, strong causal test for that potential exposure in clinical outcome. Now, lipoprotein(a) is unique in that it is highly heritable and so there are lots of different alleles that relate to lipoprotein(a) and so in a well powered analysis can actually test the lipoprotein(a) SNPs with the clinical outcomes and similar to how there is a biomarker association with incident myocardial infarction and incident stroke, the SNPs related to lipoprotein(a) show the same. That is among the evidence that strongly supports that this might be causal. Now, fast forward to many years later, we have at least three phase three randomized clinical trials testing agents that have been shown to be very potent at lowering lipoprotein(a) that in the coming years we will know if that hypothesis is true. Importantly, we will have to understand what are the potential side effects of these medicines. There are antisense oligonucleotides and siRNAs that are primarily in investigation. Again, this is an example where there's a strong genetic observation, and so these genetic based longer interval dosing therapies may be attractive, but side effects will be a key thing as well too. Those things hard to anticipate really can anticipate based on the human genetics for off target effects, for example.(09:52):It's clearly a risk signal and hopefully in the near future we're going to have specific therapies.Eric Topol (09:57):Yeah, you did a great job of explaining Mendelian randomization and the fact the power of genetics, which we're going to get into deeper shortly, but the other point is that do you expect now that there's these multiple drugs that lower Lp(a) efficiently, would that be enough to get approval or will it have to be trials to demonstrate improved cardiovascular outcomes?Pradeep Natarajan (10:24):There is a great regulatory path at FDA for approval just for LDL cholesterol lowering and inclisiran is on the market and the phase three outcomes data has not yet been reported because there is a wide appreciation that LDL cholesterol lowering is a pretty good surrogate for cardiovascular disease risk lowering. The label will be restricted to LDL cholesterol lowering and then if demonstrated to have clinical outcomes, the label could be expanded. For other biomarkers including lipoprotein(a), even though we have strong conviction that it is likely a causal factor there hasn't met the bar yet to get approval just based on lipoprotein(a) lowering, and so we would need to see the outcomes effects and then we would also need to understand side effects. There is a body of literature of side effects for other therapies that have targeted using antisense oligonucleotides. We talked about potential side effects from some siRNA platforms and sometimes those effects could overtake potential benefits, so that really needs to be assessed and there is a literature and other examples.(11:31):The other thing I do want to note related to lipoprotein(a) is that the human genetics are modeled based on lifelong perturbations, really hard to understand what the effects are, how great of an effect there might be in different contexts, particularly when introduced in middle age. There's a lot of discussion about how high lipoprotein(a) should be to deliver these therapies because the conventional teaching is that one in five individuals has high lipoprotein(a), and that's basically greater than 75 nanomoles per liter. However, some studies some human genetic studies to say if you want to get an effect that is similar to the LDL cholesterol lowering medicines on the market, you need to start with actually higher lipoprotein(a) because you need larger amounts of lipoprotein(a) lowering. Those are studies and approaches that haven't been well validated. We don't know if that's a valid approach because that's modeling based on this sort of lifelong effect. So I'm very curious to see what the overall effect will be because to get approval, I think you need to demonstrate safety and efficacy, but most importantly, these manufacturers and we as clinicians are trying to find viable therapies in the market that it won't be hard for us to get approval because hopefully the clinical trial will have said this is the context where it works. It works really well and it works really well on top of the existing therapies, so there are multiple hurdles to actually getting it directly to our patients.How Low Do You Go with LDL Cholesterol?Eric Topol (13:02):Yeah, no question about that. I'm glad you've emphasized that. Just as you've emphasized the incredible lessons from the genetics of people that have helped guide this renaissance to better drugs to prevent cardiovascular disease. LDL, which is perhaps the most impressive surrogate in medicine, a lab test that you already touched on, one of the biggest questions is how low do you go? That is Eugene Braunwald, who we all know and love. They're in Boston. The last time I got together with him, he was getting his LDL down to close to zero with various tactics that might be extreme. But before we leave these markers, you're running preventive cardiology at man's greatest hospital. Could you tell us what is your recipe for how aggressive do you go with LDL?Pradeep Natarajan (14:04):Yeah, so when I talk to patients where we're newly getting lipid lowering therapies on, especially because many people don't have a readout of abnormal LDL cholesterol when we're prescribing these medicines, it's just giving them a sense of what we think an optimal LDL cholesterol might be. And a lot of this is based on just empirical observations. So one, the average LDL cholesterol in the modern human is about 100 to 110 mg/dL. However, if you look at contemporary hunter gatherers and non-human primates, their average LDL is about 40 to 50 and newborn babies have an LDL cholesterol of about 30. And the reason why people keep making LDL cholesterol lowering medicines because as you stack on therapies, cardiovascular disease events continue to reduce including down to these very low LDL cholesterol values. So the population mean for LDL cholesterol is high and everybody likely has hypercholesterolemia, and that's because over the last 10,000 years how we live our lives is so dramatically different and there has not been substantial evolution over that time to change many of these features related to metabolism.(15:16):And so, to achieve those really low LDL cholesterol values in today's society is almost impossible without pharmacotherapies. You could say, okay, maybe everybody should be on pharmacotherapies, and I think if you did that, you probably would reduce a lot of events. You'll also be treating a lot of individuals who likely would not get events. Cardiovascular disease is the leading killer, but there are many things that people suffer from and most of the times it still is not cardiovascular disease. So our practice is still rooted in better identifying the individuals who are at risk for cardiovascular disease. And so, far we target our therapies primarily in those who have already developed cardiovascular disease. Maybe we'll talk about better identifying those at risk, but for those individuals it makes lots of sense to get it as low as possible. And the field has continued to move to lower targets.(16:07):One, because we've all recognized, at least based on these empirical observations that lower is better. But now increasingly we have a lot of therapies to actually get there, and my hope is that with more and more options and the market forces that influence that the cost perspective will make sense as we continue to develop more. As an aside, related aside is if you look at the last cholesterol guidelines, this is 2018 in the US this is the first time PCSK9 inhibitors were introduced in the guidelines and all throughout that there was discussions of cost. There are a lot of concerns from the field that PCSK9 inhibitors would bankrupt the system because so many people were on statins. And you look at the prior one that was in 2013 and cost was mentioned once it's just the cost effectiveness of statins. So I think the field has that overall concern.(17:01):However, over time we've gotten comfortable with lower targets, there are more medicines and I think some of this competition hopefully will drive down some of the costs, but also the overall appreciation of the science related to LDL. So long-winded way of saying this is kind of the things that we discussed just to give reassurance that we can go to low LDL cholesterol values and that it's safe and then we think also very effective. Nobody knows what the lower limit is, whether zero is appropriate or not. We know that glucose can get too low. We know that blood pressure can be too low. We don't know yet that limit for LDL cholesterol. I mean increasingly with these trials we'll see it going down really low and then we'll better appreciate and understand, so we'll see 40 is probably the right range.Eric Topol (17:49):40, you said? Yeah, okay, I'll buy that. Of course, the other thing that we do know is that if you push to the highest dose statins to get there, you might in some people start to see the hyperglycemia issue, which is still not fully understood and whether that is, I mean it's not desirable, but whether or not it is an issue, I guess it's still out there dangling. Now the other thing that since we're on LDL, we covered Lp(a), PCSK9, the siRNA, is ApoB. Do you measure ApoB in all your patients? Should that be the norm?Measuring ApoBPradeep Natarajan (18:32):Yeah, so ApoB is another blood test. In the standard lipid panel, you get four things. What's measured is cholesterol and triglycerides, they're the lipids insoluble in blood to get to the different tissues that get packaged in lipoprotein molecules which will have the cholesterol, triglycerides and some other lipids and proteins. And so, they all have different names as you know, right? Low density lipoprotein, high density lipoprotein and some others. But also in the lipid panel you get the HDL cholesterol, the amount of cholesterol in an HDL particle, and then most labs will calculate LDL cholesterol and LDL cholesterol has a nice relationship with cardiovascular disease. You lower it with statins and others. Lower risk for cardiovascular disease, turns out a unifying feature of all of these atherogenic lipoproteins, all these lipoproteins that are measured and unmeasured that relate to cardiovascular disease, including lipoprotein(a), they all have an additional protein called ApoB. And ApoB, at least as it relates to LDL is a pretty good surrogate of the number of LDL particles.(19:37):Turns out that that is a bit better at the population level at predicting cardiovascular disease beyond LDL cholesterol itself. And where it can be particularly helpful is that there are some patients out there that have an unexpected ratio between ApoB and LDL. In general, the ratio between LDL cholesterol and ApoB is about 1.1 and most people will have that rough ratio. I verify that that is the expected, and then if that is the expected, then really there is no role to follow ApoB. However, primarily the patients that have features related to insulin resistance have obesity. They may often have adequate looking LDL cholesterols, but their ApoB is higher. They have more circulating LDL particles relative to the total amount of LDL cholesterol, so smaller particles themselves. However, the total number of particles may actually be too high for them.(20:34):And so, even if the LDL cholesterol is at target, if the ApoB is higher, then you need to reduce. So usually the times that I just kind of verify that I'm at appropriate target is I check the LDL cholesterol, if that looks good, verify with the ApoB because of this ratio, the ApoB target should be about 10% lower. So if we're aiming for about 40, that's like 36, so relatively similar, and if it's there, I'm good. If it's not and it's higher, then obviously increase the LDL cholesterol lowering medicines because lower the ApoB and then follow the ApoB with the lipids going forward. The European Society of Cardiology has more emphasis on measuring ApoB, that is not as strong in the US guidelines, but there are many folks in the field, preventive cardiologists and others that are advocating for the increasing use of ApoB because I think there are many folks that are not getting to the appropriate targets because we are not measuring ApoB.Why Aren't We Measuring and Treating Inflammation?Eric Topol (21:37):Yeah, I think you reviewed it so well. The problem here is it could be part of the standard lipid panel, it would make this easy, but what you've done is a prudent way of selecting out people who it becomes more important to measure and moderate subsequently. Now this gets us to the fact that we're lipid centric and we don't pay homage to inflammation. So I wrote a recent Substack on the big miss on inflammation, and here you get into things like the monoclonal antibody to interleukin-6, the trial that CANTOS that showed significant reduction in cardiovascular events and fatal cancers by the way. And then you get into these colchicine trials two pretty good size randomized trials, and here the entry was coronary disease with a high C-reactive protein. Now somehow or other we abandon measuring CRP or other inflammatory markers, and both of us have had patients who have low LDLs but have heart attacks or significant coronary disease. So why don't we embrace inflammation? Why don't we measure it? Why don't we have better markers? Why is this just sitting there where we could do so much better? Even agents that are basically cost pennies like colchicine at low doses, not having to use a proprietary version could be helpful. What are your thoughts about us upgrading our prevention with inflammation markers?Pradeep Natarajan (23:22):Yeah, I mean, Eric, there is an urgent need to address these other pathways. I say urgent need because heart disease has the dubious distinction of being the leading killer in the US and then over the last 20 years, the leading killer in the world as it takes over non-communicable diseases. And really since the early 1900s, there has been a focus on developing pharmacotherapies and approaches to address the traditional modifiable cardiovascular disease risk factors. That has done tremendous good, but still the curves are largely flattening out. But in the US and in many parts of the world, the deaths attributable to cardiovascular disease are starting to tick up, and that means there are many additional pathways, many of them that we have well recognized including inflammation. More recently, Lp(a) that are likely important for cardiovascular disease, for inflammation, as you have highlighted, has been validated in randomized controlled trials.(24:18):Really the key trial that has been more most specific is one on Canakinumab in the CANTOS trial IL-1β monoclonal antibody secondary prevention, so cardiovascular disease plus high C-reactive protein, about a 15% reduction in cardiovascular disease and also improvement in cancer related outcomes. Major issues, a couple of issues. One was increased risk for severe infections, and the other one is almost pragmatic or practical is that that medicine was on the market at a very high price point for rare autoinflammatory conditions. It still is. And so, to have for a broader indication like cardiovascular disease prevention would not make sense at that price point. And the manufacturer tried to go to the FDA and focus on the group that only had C-reactive protein lowering, but that's obviously like a backwards endpoint. How would you know that before you release the medicine? So that never made it to a broader indication.(25:14):However, that stuck a flag in the broader validation of that specific pathway in cardiovascular disease. That pathway has direct relevance to C-reactive protein. C-reactive protein is kind of a readout of that pathway that starts from the NLRP3 inflammasome, which then activates IL-1β and IL-6. C-reactive protein we think is just a non causal readout, but is a reliable test of many of these features and that's debatable. There may be other things like measuring IL-6, for example. So given that there is actually substantial ongoing drug development in that pathway, there are a handful of companies with NLRP3 inflammasome inhibitors, but small molecules that you can take as pills. There is a monoclonal antibody against IL-6 that's in development ziltivekimab that's directed at patients with chronic kidney disease who have lots of cardiovascular disease events despite addressing modifiable risk factors where inflammatory markers are through the roof.(26:16):But then you would also highlighted one anti-inflammatory that's out there that's pennies on the dollar, that's colchicine. Colchicine is believed to influence cardiovascular disease by inhibiting NLRP3, I say believed to. It does a lot of things. It is an old medicine, but empirically has been shown in at least two randomized controlled trials patients with coronary artery disease, actually they didn't measure C-reactive protein in the inclusion for these, but in those populations we did reduce major adverse cardiovascular disease events. The one thing that does give me pause with colchicine is that there is this odd signal for increased non-cardiovascular death. Nobody understands if that's real, if that's a fluke. The FDA just approved last year low dose colchicine, colchicine at 0.5 milligrams for secondary prevention given the overwhelming efficacy. Hasn't yet made it into prevention guidelines, but I think that's one part that does give me a little bit pause. I do really think about it particularly for patients who have had recurrent events. The people who market the medicine and do research do remind us that C-reactive protein was not required in the inclusion, but nobody has done that secondary assessment to see if measuring C-reactive protein would be helpful in identifying the beneficial patients. But I think there still could be more work done on better identifying who would benefit from colchicine because it's an available and cheap medicine. But I'm excited that there is a lot of development in this inflammation area.Eric Topol (27:48):Yeah, well, the development sounds great. It's probably some years away. Do you use colchicine in your practice?Pradeep Natarajan (27:56):I do. Again, for those folks who have had recurrent events, even though C-reactive protein isn't there, it does make me feel like I'm treating inflammation. If C-reactive protein is elevated and then I use it for those patients, if it's not elevated, it's a much harder sell from my standpoint, from the patient standpoint. At the lower dose for colchicine, people generally are okay as far as side effects. The manufacturer has it at 0.5 milligrams, which is technically not pennies on the dollar. That's not generic. The 0.6 milligrams is generic and they claim that there is less side effects at the 0.5 milligrams. So technically 0.6 milligrams is off label. So it is what it is.CHIP and Defining High Risk People for CV DiseaseEric Topol (28:40):It's a lot more practical, that's for sure. Now, before I leave that, I just want to mention when I reviewed the IL-1β trial, you mentioned the CANTOS trial and also the colchicine data. The numbers of absolute increases for infection with the antibody or the cancers with the colchicine are really small. So I mean the benefit was overriding, but I certainly agree with your concern that there's some things we don't understand there that need to be probed more. Now, one of the other themes, well before one other marker that before we get to polygenic risk scores, which is center stage here, defining high risk people. We've talked a lot about the conventional things and some of the newer ways, but you've been one of the leaders of study of clonal hematopoiesis of indeterminate potential known as CHIP. CHIP, not the chips set in your computer, but CHIP. And basically this is stem cell mutations that increase in people as we age and become exceptionally common with different mutations that account in these clones. So maybe you can tell us about CHIP and what I don't understand is that it has tremendous correlation association with cardiovascular outcomes adverse as well as other system outcomes, and we don't measure it and we could measure it. So please take us through what the hell is wrong there.Pradeep Natarajan (30:14):Yeah, I mean this is really exciting. I mean I'm a little bit biased, but this is observations that have been made only really over the last decade, but accelerating research. And this has been enabled by advances in genomic technologies. So about 10 years or plus ago, really getting into the early days of population-based next generation sequencing, primarily whole exome sequencing. And most of the DNA that we collect to do these population-based analyses come from the blood, red blood cells are anucleate, so they're coming from white blood cells. And so, at that time, primarily interrogating what is the germline genetic basis for coronary artery disease and early onset myocardial infarction. At the same time, colleagues at the Broad Institute were noticing that there are many additional features that you can get from the blood-based DNA that was being processed by the whole exome data. And there were actually three different groups that converged on that all in Boston that converged on the same observation that many well-established cancer causing mutations.(31:19):So mutations that are observed in cancers that have been described to drive the cancers themselves were being observed in these large population-based data sets that we were all generating to understand the relationship between loss of function mutations in cardiovascular disease. That's basically the intention of those data sets for being generated for other things. Strong correlation with age, but it was very common among individuals greater than 70; 10% of them would have these mutations and is very common because blood cancer is extremely, it's still pretty rare in the population. So to say 10% of people had cancer causing driver mutations but didn't have cancer, was much higher than anyone would've otherwise expected. In 2014, there were basically three main papers that described that, and they also observed that there is a greater risk of death. You'd say, okay, this is a precancerous lesion, so they're probably dying of cancer.(32:17):But as I said, the absolute incidence rate for blood cancer is really low and there's a relative increase for about tenfold, but pretty small as it relates to what could be related to death. And in one of the studies we did some exploratory analysis that suggested maybe it's actually the most common cause of death and that was cardiovascular disease. And so, a few years later we published a study that really in depth really looked at a bunch of different data sets that were ascertained to really understand the relationship between these mutations, these cancer causing mutations in cardiovascular disease, so observed it in enrichment and older individuals that had these mutations, CHIP mutations, younger individuals who had early onset MI as well too, and then also look prospectively and showed that it related to incident coronary artery disease. Now the major challenge for this kind of analysis as it relates to the germline genetic analysis is prevalence changes over time.(33:15):There are many things that could influence the presence of clonal hematopoiesis. Age is a key enriching factor and age is the best predictor for cardiovascular disease. So really important. So then we modeled it in mice. It was actually a parallel effort at Boston University (BU) that was doing the same thing really based on the 2014 studies. And so, at the same time we also observed when you modeled this in mice, you basically perturb introduce loss of function mutations in the bone marrow for these mice to recapitulate these driver mutations and those mice also have a greater burden of atherosclerosis. And Eric, you highlighted inflammation because basically the phenotype of these cells are hyper inflamed cells. Interestingly, C-reactive protein is only modestly elevated. So C-reactive protein is not fully capturing this, but many of the cytokines IL-1β, IL-6, they're all upregulated in mice and in humans when measured as well.(34:11):Now there've been a few key studies that have been really exciting about using anti-inflammatories in this pathway to address CHIP associated cardiovascular disease. So one that effort that I said in BU because they saw these cytokines increased, we already know that these cytokines have relationship with atherosclerosis. So they gave an NLRP3 inflammasome inhibitor to the mice and they showed that the mice with or without CHIP had a reduction in atherosclerosis, but there was a substantial delta among the mice that are modeled as having CHIP. Now, the investigators in CANTOS, the manufacturers, they actually went back and they survey where they had DNA in the CANTOS trial. They measured CHIP and particularly TET2 CHIP, which is the one that has the strongest signal for atherosclerosis. As I said, overall about 15% reduction in the primary outcome in CANTOS. Among the individuals who had TET2 CHIP, it was a 64% reduction in event.(35:08):I mean you don't see those in atherosclerosis related trials. Now this has the caveat of it being secondary post hoc exploratory, the two levels of evidence. And so, then we took a Mendelian randomization approach. Serendipitously, just so happens there is a coding mutation in the IL-6 receptor, a missense mutation that in 2012 was described that if you had this mutation, about 40% of people have it, you have a 5%, but statistically significant reduction in coronary artery disease. So we very simply said, if the pathway of this NLRP3 inflammasome, which includes IL-6, if you have decreased signaling in that pathway, might you have an even greater benefit from having that mutation if you had CHIP versus those who didn't have CHIP. So we looked in the UK Biobank, those who didn't have CHIP 5% reduction, who had that IL-6 receptor mutation, and then those who did have CHIP, if they had that mutation, it was about a 60% reduction in cardiovascular disease.(36:12):Again, three different lines of evidence that really show that this pathway has relevance in the general population, but the people who actually might benefit the most are those with CHIP. And I think as we get more and more data sets, we find that not all of the CHIP mutations are the same as it relates to cardiovascular disease risk. It does hone in on these key subsets like TET2 and JAK2, but this is pretty cool as a preventive cardiologist, new potential modifiable risk factor, but now it's almost like an oncologic paradigm that is being applied to coronary artery disease where we have specific driver mutations and then we're tailoring our therapies to those specific biological drivers for coronary artery disease. Hopefully, I did that justice. There's a lot there.Why Don't We Measure CHIP?Eric Topol (36:57):Well, actually, it's phenomenal how you've explained that, but I do want to review for our listeners or readers that prior to this point in our conversation, we were talking about germline mutations, the ones you're born with. With CHIP, we're talking about acquired somatic mutations, and these are our blood stem cells. And what is befuddling to me is that with all the data that you and others, you especially have been publishing and how easy it would be to measure this. I mean, we've seen that you can get it from sequencing no less other means. Why we don't measure this? I mean, why are we turning a blind eye to CHIP? I just don't get it. And we keep calling it of indeterminate potential, not indeterminate. It's definite potential.Pradeep Natarajan (37:51):Yeah, no, I think these are just overly cautious terms from the scientists. Lots of people have CHIP, a lot of people don't have clinical outcomes. And so, I think from the lens of a practicing hematologists that provide some reassurance on the spectrum for acquired mutation all the way over to leukemia, that is where it comes from. I don't love the acronym as well because every subfield in biomedicine has its own CHIP, so there's obviously lots of confusion there. CH or clinical hematopoiesis is often what I go, but I think continuing to be specific on these mutations. Now the question is why measure? Why aren't we measuring it? So there are some clinical assays out there. Now when patients get evaluated for cytopenias [low cell counts], there are next generation sequencing tests that look for these mutations in the process for evaluation. Now, technically by definition, CHIP means the presence of these driver mutations that have expanded because it's detectable by these assays, not a one-off cell because it can only be detected if it's in a number of cells.(38:55):So there has been some expansion, but there are no CBC abnormalities. Now, if there's a CBC abnormality and you see a CHIP mutation that's technically considered CCUS or clonal cytopenia of unknown significance, sometimes what is detected is myelodysplastic syndrome. In those scenarios still there is a cardiovascular disease signal, and so many of our patients who are seen in the cancer center who are being evaluated for these CBC abnormalities will be detected to have these mutations. They will have undergone some risk stratification to see what the malignancy potential is. Still pretty low for many of those individuals. And so, the major driver of health outcomes for this finding may be cardiovascular. So those patients then get referred to our program. Dana-Farber also has a similar program, and then my colleague Peter Libby at the Brigham often sees those patients as well. Now for prospective screening, so far, an insurance basically is who's going to pay for it.(39:51):So an insurance provider is not deemed that appropriate yet. You do need the prospective clinical trials because the medicines that we're talking about may have side effects as well too. And what is the yield? What is the diagnostic yield? Will there actually be a large effect estimate? But there has been more and more innovation, at least on the assay and the cost part of the assay because these initial studies, we've been using whole exome sequencing, which is continuing to come down, but is not a widely routine clinical test yet. And also because as you highlighted, these are acquired mutations. A single test is not necessarily one and done. This may be something that does require surveillance for particular high risk individuals. And we've described some risk factors for the prevalence of CHIP. So surveillance may be required, but because there are about 10 genes that are primarily implicated in CHIP, that can substantially decrease the cost of it. The cost for DNA extraction is going down, and so there are research tests that are kind of in the $10 to $20 range right now for CHIP. And if flipped over to the clinical side will also be reasonably low cost. And so, for the paradigm for clinical implementation, that cost part is necessary.Eric Topol (41:10):I don't know the $10 or $20 ones. Are there any I could order on patients that I'm worried about?Pradeep Natarajan (41:17):Not yet clinical. However, there is a company that makes the reagents for at least the cores that are developing this. They are commercializing that test so that many other cores, research cores can develop it. I think it's in short order that clinical labs will adopt it as well too.Eric Topol (41:36):That's great.Pradeep Natarajan (41:37):I will keep you apprised.What About Polygenic Risk Scores?Eric Topol (41:39):I think that's really good news because like I said, we're so darn lipid centric and we have to start to respect the body of data, the knowledge that you and others have built about CHIP. Now speaking of another one that drives me nuts is polygenic risk score (PRS) for about a decade, I've been saying we have coronary disease for most people is a polygenic trait. It's not just a familial hypercholesterolemia. And we progressively have gotten better and better of the hundreds of single variants that collectively without a parental history will be and independently predict who is at double, triple or whatever risk of getting heart disease, whereby you could then guide your statins at higher aggressive or pick a statin, use one or even go beyond that as we've been talking about. But we don't use that in practice, which is just incredible because it's can be done cheap.(42:45):You can get it through whether it's 23andMe or now many other entities. We have an app, MyGeneRank where we can process that Scripss does for free. And only recently, Mass General was the first to implement that in your patient population, and I'm sure you were a driver of that. What is the reluctance about using this as an orthogonal, if you will, separate way to assess a person's risk for heart disease? And we know validated very solidly about being aggressive about lipid lowering when you know this person's in the highest 5% polygenic risk score. Are we just deadheads in this field or what?Pradeep Natarajan (43:30):Yeah, I mean Eric, as you know, lots of inertia in medicine, but this one I think has a potential to make a large impact. Like CHIP mutations, I said news is about 10% in individuals greater than 70. The prospect here is to identify the risk much earlier in life because I think there is a very good argument that we're undertreating high risk individuals early on because we don't know how to identify them. As you highlighted, Dr. Braunwald about LDL cholesterol. The other part of that paradigm is LDL cholesterol lowering and the duration. And as we said, everybody would benefit from really low LDL cholesterol, but again, you might overtreat that if you just give that to everybody. But if you can better identify the folks very early in life, there is a low cost, low risk therapy, at least related to statins that you could have a profound benefit from the ones who have a greater conviction will have future risk for cardiovascular disease.(44:21):You highlighted the family history, and the family history has given the field of clues that genetics play a role. But as the genome-wide association studies have gotten larger, the polygenic risk scores have gotten better. We know that family history is imperfect. There are many reasons why a family member who is at risk may or may not have developed cardiovascular disease. A polygenic risk score will give a single number that will estimate the contribution of genetics to cardiovascular disease. And the thing that is really fascinating to me, which is I think some of a clinical implementation challenge is that the alleles for an individual are fixed. The genotyping is very cheap. That continues to be extremely cheap to do this test. But the weights and the interpretation of what the effects should be for each of the SNPs are continually being refined over time.(45:18):And so, given the exact same SNPs in the population, the ability to better predict cardiovascular diseases getting better. And so, you have things that get reported in the literature, but literally three years later that gets outdated and those hypotheses need to be reassessed. Today, I'll say we have a great relative to other things, but we have a great polygenic risk score was just reported last year that if you compare it to familial hypercholesterolemia, which has a diagnostic yield of about 1 in 300 individuals, but readily detectable by severe hypercholesterolemia that has about threefold risk for cardiovascular disease. By polygenic risk score, you can find 1 in 5 individuals with that same risk. Obviously you go higher than that, it'll be even higher risk related to that. And that is noble information very early in life. And most people develop risk factors later in life. It is happening earlier, but generally not in the 30s, 40s where there's an opportunity to make a substantial impact on the curve related to cardiovascular disease.(46:25):But there is a lot of momentum there. Lots of interest from NIH and others. The major challenge is though the US healthcare system is really not well set up to prevention, as you know, we practice healthcare after patient's developed disease and prevent the complications related to progression. The stakeholder incentives beyond the patient themselves are less well aligned. We've talked a lot here today about payers, but we don't have a single payer healthcare system. And patients at different times of their lives will have different insurers. They'll start early in life with their parents, their first employer, they'll move on to the next job and then ultimately Medicare. There's no entity beyond yourself that really cares about your longevity basically from the beginning and your overall wellness. That tension has been a major challenge in just driving the incentives and the push towards polygenic risk scores. But there are some innovative approaches like MassMutual Life Insurance actually did a pilot on polygenic risk scoring.(47:33):They're in the business of better understanding longevity. They get that this is important data. Major challenges, there are federal protections against non-discrimination in the workplace, health insurance, not necessarily life insurance. So I think that there are lots of things that have to be worked out. Everybody recognizes that this is important, but we really have to have all the incentives aligned for this to happen at a system-wide level in the US. So there's actually lots of investment in countries that have more nationalized healthcare systems, lots of development in clinical trials in the UK, for example. So it's possible that we in the US will not be the lead in that kind of evidence generation, but maybe we'll get there.The GLP-1 DrugsEric Topol (48:16):Yeah, it's frustrating though, Pradeep, because this has been incubating for some time and now we have multi ancestry, polygenic risk scores, particularly for heart disease and we're not using it, and it's not in my view, in the patient's best interest just because of these obstacles that you're mentioning, particularly here in the US. Well, the other thing I want to just get at with you today is the drugs that we were using for diabetes now blossoming for lots of other indications, particularly the glucagon-like peptide 1 (GLP-1) drugs. This has come onto the scene in recent years, not just obviously for obesity, but it's anti-inflammatory effects as we're learning, mediated not just through the brain but also T cells and having extraordinary impact in heart disease for people with obesity and also with those who have heart failure, about half of heart failure for preserved ejection fraction. So recently you and your colleagues recently published a paper with this signal of optic neuropathy. It was almost seven eightfold increase in a population. First, I wanted to get your sense about GLP-1. We're also going to get into the SGLT2 for a moment as well, but how do you use GLP-1? What's your prognosis for this drug class going forward?Pradeep Natarajan (49:55):As it relates to the paper, I can't claim credit as one of my former students who is now Mass Eye and Ear resident who participated, but we can talk about that. There's obviously some challenges for mining real world data, but this was related to anecdotes that they were observing at Mass Eye and Ear and then studied and observed an enrichment. In general though, I feel like every week I'm reading a new clinical trial about a new clinical outcome benefit as it relates to GLP-1 receptor agonists. This is kind of one thing that stands out that could be interrogated in these other clinical trials. So I would have that caveat before being cautious about ocular complications. But the data has been overwhelmingly beneficial, I think, because at minimum, obesity and inflammation are relayed to myriad of consequences, and I'm really excited that we have therapies that can address obesity that are safe.(50:52):There's a legacy of unsafe medicines for obesity, especially related to cardiovascular disease. So the fact that we have medicines that are safe and effective for lowering weight that also have real strong effects on clinical outcomes is tremendous. We in cardiology are increasingly using a range of diabetes medicines, including GLP-1 receptor agonists and SGLT2 inhibitors. I think that is also the secular changes of what influences cardiovascular disease over time. I talked about over the last 10 years or so with this increase in deaths attributable to cardiovascular disease. If you look at the influences of traditional clinical risk factors today, many of them have decreased in importance because when abnormal, we recognize them, in general we modify them when recognized. And so, many of the things that are unaddressed, especially the features related to insulin resistance, obesity, they start rising in importance. And so, there is a dramatic potential for these kinds of therapies in reducing the residual risks that we see related to cardiovascular disease. So I'm enthusiastic and excited. I think a lot more biology that needs to be understood of how much of this is being influenced specifically through this pathway versus a very effective weight loss medicine. But also interesting to see the insights on how the effect centrally on appetite suppression has profound influences on weight loss as well too. And hopefully that will lead to more innovations in weight management.The SGLT-2 DrugsEric Topol (52:25):And likewise, perhaps not getting near as much play, but when it came on the cardiovascular scene that an anti-diabetic drug SGLT2 was improving survival, that was big, and we still don't know why. I mean, there's some ideas that it might be a senolytic drug unknowingly, but this has become a big part of practice of cardiology in patients with diabetes or with preserved ejection fraction heart failure. Is that a fair summary for that drug?Pradeep Natarajan (53:00):Yeah, I totally agree. I mean, as there has been increased recognition for heart failure preserved ejection fraction, it has been almost disheartening over the last several years that we have not had very specific effective therapies to treat that condition. Now, it is a tremendous boon that we do have medicines interestingly focused on metabolism that are very helpful in that condition for heart failure with preserved ejection fraction. But there is still much more to be understood as far as that condition. I mean, the major challenge with heart failure, as you know, especially with heart failure preserved ejection fraction, it likely is a mix of a wide variety of different etiologies. So in parallel with developing effective therapies that get at some aspect is really understanding what are the individual drivers and then targeting those specific individual drivers. That requires a lot of unbiased discovery work and further profiling to be done. So lot more innovation, but relative to heart failure itself, it is not had widespread recognition as heart failure reduced ejection fraction. So much more to innovate on, for sure.Eric Topol (54:07):Right, right. Yeah, I am stunned by the recent progress in cardiovascular medicine. You have been center stage with a lot of it, and we've had a chance to review so much. And speaking of genetics, I wanted to just get a little insight because I recently came across the fact that your mother here at the City of Hope in Southern California is another famous researcher. And is that, I don't know what chromosome that is on regarding parental transmission of leading research. Maybe you can tell me about that.Pradeep Natarajan (54:41):Yeah, I mean, I guess it is a heritable trait when a parent has one profession that there is a higher likelihood that the offspring will have something similar. So both of my parents are PhDs, nonphysicians. There is a diabetes department at the City of Hope, so she's the chair of that department. So very active. We do overlap in some circles because she does investigate both vascular complications and renal complications. And then sometimes will ask my advice on some visualization. But she herself has just had a science translational medicine paper, for example, just a couple of months ago. So it's fun to talk about these things. To be honest, because my parents are researchers, I was not totally sure that I would be a researcher and kind of wanted to do something different in medicine. But many of my early observations and just how common cardiovascular disease is around me and in my community and wanting to do something useful is what got me specifically into cardiology.(55:45):But obviously there are numerous outstanding, important questions. And as I went through my career, really focused on more basic investigations of atherosclerosis and lipids. What got me excited sort of after my clinical training was the ability to ask many of these questions now in human populations with many new biological data sets, at least first centered on genetics. And the capabilities continue to expand, so now I teach first year Harvard medical students in their genetics curriculum. And when I talk to them just about my career arc, I do remind them they're all doing millions of things and they're exploring lots of things, but when they get to my shoes, the capabilities will be tremendously different. And so, I really advise them to take the different experiences, mainly in an exercise for asking questions, thoughtfully addressing questions, connecting it back to important clinical problems. And then once they start to understand that with a few different approaches, then they'll totally take off with what the opportunities are down the road.Eric Topol (56:51):No, it's great. I mean, how lucky somebody could be in the first year of med school with you as their teacher and model. Wow. Pradeep, we've really gone deep on this and it's been fun. I mean, if there's one person I'm going to talk to you about cardiovascular risk factors and the things that we've been into today, you would be the one. So thank you for taking the time and running through a lot of material here today, and all your work with great interest.Pradeep Natarajan (57:24):Thanks, Eric. I really appreciate it. It's tremendous honor. I'm a big fan, so I would be glad to talk about any of these things and more anytime.***************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informative!Voluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly helped fund our summer internship programs for 2023 and 2024.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Note: you can select preferences to receive emails about newsletters, podcasts, or all I don't want to bother you with an email for content that you're not interested in. Get full access to Ground Truths at erictopol.substack.com/subscribe

This week Patrick is joined by Austin Argentieri, Research Fellow at Harvard Medical School, Affiliate Member of the Broad Institute of MIT and Harvard, and Research Fellow at Massachusetts General Hospital. Austin's work focuses on the proteomics of aging and how proteomic signatures are highly predictive for estimating biological age. From the potential of therapeutic applications, to why no “fountain of youth” genes have yet been identified, he and Patrick discuss the heritability of aging and how proteomics can help identify risk of age-related disease.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Thank you for reading Ground Truths. This post is public so feel free to share it.Transcript with audio and external linksEric Topol (00:05):Hello, it's Eric Topol with Ground Truths, and I am really thrilled to have with me Professor Faisal Mahmood, who is lighting it up in the field of pathology with AI. He is on the faculty at Harvard Medical School, also a pathologist at Mass General Brigham and with the Broad Institute, and he has been publishing at a pace that I just can't believe we're going to review that in chronological order. So welcome, Faisal.Faisal Mahmood (00:37):Thanks so much for having me, Eric. I do want to mention I'm not a pathologist. My background is in biomedical imaging and computer science. But yeah, I work very closely with pathologists, both at Mass General and at the Brigham.Eric Topol (00:51):Okay. Well, you know so much about pathology. I just assume that you were actually, but you are taking computational biology to new levels and you're in the pathology department at Harvard, I take it, right?Faisal Mahmood (01:08):Yeah, I'm at the pathology department at Mass General Brigham. So the two hospitals are now integrated, so I'm at the joint department.Eric Topol (01:19):Good. Okay. Well, I'm glad to clarify that because as far as I knew you were hardcore pathologist, so you're changing the field in a way that is quite unique, I should say, because a number of years ago, deep learning was starting to get applied to pathology just like it was and radiology and ophthalmology. And we saw some early studies with deep learning whereby you could find so much more on a slide that otherwise would be not even looked at or considered or even that humans wouldn't be able to see. So maybe you could just take us back first to the deep learning phase before these foundation models that you've been building, just to give us a flavor for what was the warmup in this field?Faisal Mahmood (02:13):Yeah, so I think around 2016 and 2017, it was very clear to the computer vision community that deep learning was really the state of the art where you could have abstract feature representations that were rich enough to solve some of these fundamental classification problems in conventional vision. And that's around the time when deep learning started to be applied to everything in medicine, including pathology. So we saw some earlier cities in 2016 and 2017, mostly in machine learning conferences, applying this to very basic patch level pathology dataset. So then in 2018 and 2019, there were some studies in major journals including in Nature Medicine, showing that you could take large amounts of pathology data and classify what's known to us and including predicting what's now commonly referred to as non-human identifiable features where you could take a label and this could come from molecular data, other kinds of data like treatment response and so forth, and use that label to classify these images as responders versus non-responders or having a certain kind of mutation or not.(03:34):And what that does is that if there is a morphologic signal within the image, it would pick up on that morphologic signal even though humans may not have picked up on it. So it was a very exciting time of developing all of these supervised, supervised foundation models. And then I started working in this area around 2019, and one of the first studies we did was to try to see if we can make this a little bit more data efficient. And that's the CLAM method that we published in 2021. And then we took that method and applied it to the problem of cancers of unknown primary, that was also in 2021.Eric Topol (04:17):So just to review, in the phase of deep learning, which was largely we're talking about supervised with ground truth images, there already was a sign that you could pick up things like the driver mutation, the prognosis of the patient from the slide, you could structural variations, the origin of the tumor, things that would never have been conceived as a pathologist. Now with that, I guess the question is, was all this confined to whole slide imaging or could you somehow take an H&E slide conventional slide and be able to do these things without having to have a whole slide image?Faisal Mahmood (05:05):So at the time, most of the work was done on slides that were fully digital. So taking a slide and then digitizing the image and creating a whole slide image. But we did show in 2021 that you could put the slide under a microscope and then just capture it with a camera or just with a cell phone coupled to a camera, and then still make those predictions. So these models were quite robust to that kind of domain adaptation. And still I think that even today the slide digitization rate in the US remains at around 4%, and the standard of care is just looking at a glass light under a microscope. So it's very important to see how we can further democratize these models by just using the microscope, because most microscopes that pathologists use do have a camera attached to them. So can we somehow leverage that camera to just use a model that might be trained on a whole slide image, still work with the slide under a microscope?Eric Topol (06:12):Well, what you just said is actually a profound point that is only 4% of the slides are being reviewed digitally, and that means that we're still an old pathology era without the enlightenment of machine eyes. I mean these digital eyes that can be trained even without supervised learning as we'll get to see things that we'll never see. And to make, and I know we'll be recalling back in 2022, you and I wrote a Lancet piece about the work that you had done, which is very exciting with cardiac biopsies to detect whether a heart transplant was a rejection. This is a matter of life or death because you have to give more immunosuppression drugs if it's a rejection. But if you do that and it's not a rejection or you miss it, and there's lots of disagreement among pathologists, cardiac pathologists, regarding whether there's a transplant. So you had done some early work back then, and because much of what we're going to talk about, I think relates more to cancer, but it's across the board in pathology. Can you talk about the inner observer variability of pathologists when they look at regular slides?Faisal Mahmood (07:36):Yeah. So when I first started working in this field, my kind of thinking was that the slide digitization rate is very low. So how do we get people to embrace and adapt digital pathology and machine learning models that are trained on digital data if the data is not routinely digitized? So one of my kind of line of thinking was that if we focus on problems that are inherently so difficult that there isn't a good solution for them currently, and machine learning provides, or deep learning provides a tangible solution, people will be kind of forced to use these models. So along those lines, we started focusing on the cancers of unknown primary problem and the myocardial biopsy problem. So we know that the Cohen's kappa or the intra-observer variability that also takes into account agreement by chance is around 0.22. So it's very, very low for endomyocardial biopsies. So that just means that there are a large number of patients who have a diagnosis that other pathologists might not agree with, and the downstream treatment regimen that's given is entirely based on that diagnosis. The same patient being diagnosed by a different cardiac pathologist could be receiving a very different regimen and could have a very, very different outcome.(09:14):So the goal for that study is published in Nature of Medicine in 2022, was to see if we could use deep learning to standardize that and have it act as an assistive tool for cardiac pathologists and whether they give more standardized responses when they're given a machine learning based response. So that's what we showed, and it was a pleasure to write that corresponding piece with you in the Lancet.Eric Topol (09:43):Yeah, no, I mean I think that was two years ago and so much has happened since then. So now I want to get into this. You've been on a tear every month publishing major papers and leading journals, and I want to just go back to March and we'll talk about April, May, and June. So back in March, you published two foundation models, UNI and CONCH, I believe, both of these and back-to-back papers in Nature Medicine. And so, maybe first if you could explain the foundation model, the principle, how that's different than the deep learning network in terms of transformers and also what these two different, these were mega models that you built, how they contributed to help advance the field.Faisal Mahmood (10:37):So a lot of the early work that we did relied on extracting features from a resonant trained on real world images. So by having these features extracted, we didn't need to train these models end to end and allowed us to train a lot of models and investigate a lot of different aspects. But those features that we used were still based on real world images. What foundation models led us do is they leveraged self supervised learning and large amounts of data that would be essentially unlabeled to extract rich feature representations from pathology images that can then be used for a variety of different downstream tasks. So we basically collected as much data as we could from the Brigham and MGH and some public sources while trying to keep it as diverse as possible. So the goal was to include infectious, inflammatory, neoplastic all everything across the pathology department while still being as diverse as possible, including normal tissue, everything.(11:52):And the hypothesis there, and that's been just recently confirmed that the hypothesis was that diversity would matter much more than the quantity of data. So if you have lots and lots of screening biopsies and you use all of them to train the foundation model, there isn't enough diversity there that it would begin to learn those fundamental feature representations that you would want it to learn. So we used all of this data and then trained the UNI model and then together with it was an image text model where it starts with UNI and then reinforces the feature representations using images and texts. And that sort of mimics how humans learn about pathology. So a new resident, new trainee learning pathology has a lot of knowledge of the world, but it's perhaps looking at a pathology image for the first time. But besides looking at the image, they're also being reinforced by all these language cues from, whether it's from text or from audio signals. So the hope there was that text would kind of reinforce that and generate better feature representation. So the two studies were made available together. They were published in Nature Medicine back in March, and with that we made both those models public. So at the time we obviously had no idea that they would generate so much interest in this field, downloaded 350,000 times on Hugging Face and used for all kinds of different applications that I would've never thought of. So that's been very exciting to see.Eric Topol (13:29):Can you give some examples of some of the things you wouldn't have thought of? Because it seems like you think of everything.Faisal Mahmood (13:35):Yeah, people have used it to when there was a challenge for detecting tuberculosis, I think in a very, very different kind of a dataset. It was from the Nightingale Foundation and they have large data sets. So that was very interesting to see. People have used it to create newer data sets that can then be used for training additional foundation models. It's being used to extract rich feature representations from pathology images, corresponding spatial transcriptomic data, trying to predict spatial transcriptomics directly from histology. And there's a number of other options.Eric Topol (14:27):Well, yeah, that was March. Before we get to April, you slipped in the spatial omics thing, which is a big deal that is ability to look at tissue, human tissue over time and space. I mean the spatial temporal, it will tell us so much whether an evolution of a cancer process or so many things. Can you just comment because this is one of the major parts of this new era of applying AI to biology?Faisal Mahmood (15:05):So I think there are a number of things we can do if we have spatial data spatially resolved omic data with histology images. So the first thing that comes to my mind as a computer scientist would be that can we train a joint foundation model where we would use the spatially resolved transcriptomics to further enforce the pathology signal as a ground truth in a contrastive manner, similar to what we do with text, and can we use that to extract even richer feature representation? So we're doing that. In fact, we made a data set of about a thousand pathology images with corresponding spatial transcriptomic information, both curated from public resources as well as some internal data publicly available so people could investigate that question further. We're entrusted in other aspects of this because there is some indication including a study from James Zou's group at Stanford showing that we can predict histology, predict the spatial transcriptomic signal directly from histology. So there's early indications that we might also be able to do that in three dimensions. So yeah, it's definitely very interesting. More and more of that data is becoming available and how machine learning can sort of augment that is very exciting.Eric Topol (16:37):Yeah, I mean, most of the spatial omics has been a product of single cell sequencing, whether it's single nuclei and different omics, not just DNA, of course, RNA and even methylation, whatnot. So the fact that you could try to impute that from the histologies is pretty striking. Now, that was March and then in April you published to me an extraordinary paper about demographic bias and how generative AI, we're in the generative AI year now since as we discussed with foundation models, here again that gen AI could actually reduce biases and enhance fairness, which of course is so counterintuitive to everything that's been written to date. So maybe you can take us through how we can get a reduction in bias in pathology.Faisal Mahmood (17:34):Yeah, so in the study, the study was about, this had been investigated in other fields, but what we try to show is that a model trained on large, diverse, publicly available data. When that's applied internally and we stratify it based on demographic differences, race and so forth, we see these very clear disparities and biases. And we investigated a lot of different solutions that were out there to equalize the distribution of the data to balance the distribution using or sampling and some of these simple techniques. And none of them worked quite well. And then we observed that using foundation models or just having richer feature representations eliminates some of those biases. In parallel, there was another study from Google where they use generative AI to synthesize additional images from those underrepresented groups and then use those images to enhance the training signal. And then they also showed that you could reduce those biases.(18:49):So I think the common denominator there is that richer feature representations contribute to reduced biases. So the biases not because there is some inherent signal tied to these subgroups, but the bias is essentially there because the feature representations are not strong enough. Another general observation is that there's some kind of a confounder often there that leads to the bias. And one example would be that patients with socioeconomic disparities might just be diagnosed late and there might not be enough advanced cases in the training dataset. So quite often when you go in and look at what your training distribution looks like and how it varies from your test distribution and what that dataset shift is, you're able to figure out where the bias inherently comes from. But as a general principle, if you use the richest possible feature representation or focus on making your feature representations richer by using better foundation models and so forth, you are able to reduce a lot of the bias.Eric Topol (19:58):Yeah, that's really another key point here is about the richer features and the ability counterintuitively to actually reduce bias. And what is important in interrogating data inputs, as you said before, you wind up with a problem with bias. Now, then it comes May since we're just March and April, in May you published TriPath, which is now bringing in the 3D world of pathology. So maybe you can give us a little skinny on that one.Faisal Mahmood (20:36):Yeah. So just looking at the spectrum of where pathology is today, I think that we all agree in the community that pathologists often look at extremely sampled tissue. So human tissue is inherently three-dimensional, and by the time it gets to a pathologist, it's been sampled and cut so many times that it often would lack that signal. And there are a number of studies that have shown that if you subsequently cut sections, you get to a different outcome. If you look at multiple slides for a prostate biopsy, you get to a different Gleason score. There are all of these studies that have shown that 3D pathology is important. And with that, there's been a growing effort to build tools, microscopes, imaging tools that can image tissue in 3D. And there are about 10 startups who've built all these different technologies, open-top light-sheet microscopy, microCT and so forth that can image tissue really well in three dimensions, but none of them have had clinical adoption.(21:39):And we think that a key reason is that there isn't a good way for a pathologist to examine such a large volume of tissue. If they spend so much time examining this large volume of tissue, they would never be able to get through all the, so the goal here really was to develop a computational tool that would look through the large volume and highlight key regions that a pathologist can then examine. And the secondary goal was that does using three dimensional tissue actually improve patient stratification and does using, essentially using three 3D deep learning, having 3D convolutions extract richer features from the three dimensions that can then be used to separate patients into distinct risk groups. So that's what we did in this particular case. The study relied on a lot of data from Jonathan Liu's group at University of Washington, and also data that we collected at Harvard from tissue that came from the Brigham and Women's Hospital. So it was very exciting to show that what the value of 3D pathology can be and how it can actually translate into the clinic using some of these computational tools.Eric Topol (22:58):Do you think ultimately someday that will be the standard that you'll have a 3D assessment of a biopsy sample?Faisal Mahmood (23:06):Yeah, I'm really convinced that ultimately 3D would become the standard because the technology to image these tissue is becoming better and better every year, and it's getting closer to a point where the imaging can be fast enough to get to clinical deployment. And then on the computational end, we're increasingly making a lot of progress.Eric Topol (23:32):And it seems, again, it's something that human eyes couldn't do because you'd have to look at hundreds of slides to try to get some loose sense of what's going on in a 3D piece of tissue. Whereas here you're again taking advantage, exploiting the digital eyes. Now this culminates to your June big paper PathChat in Nature, and this was a culmination of a lot of work you've been doing. I don't know if you do any sleep or your team, but then you published a really landmark paper. Can you take us through that?Faisal Mahmood (24:12):Yeah, so I think that with the foundation models, we could extract very rich feature representation. So to us, the obvious next step was to take those feature representations and link them with language. So a human would start to communicate with a generative AI model where we could ask questions about what's going on in a pathology image, it would be capable of making a diagnosis, it would be capable of writing a report, all of those things. And the reason we thought that this was really possible is because pathology knowledge is a subset of the world's knowledge. And companies like OpenAI are trying to build singular, multimodal, large language models that would harbor the world's information, the world knowledge and pathology is much, much more finite. And if we have the right kind of training data, we should be able to build a multimodal large language model that given any pathology image, it can interpret what's going on in the image, it can make a diagnosis, it can run through grading, prognosis, everything that's currently done, but also be an assistant for research, analyzing lots of images to see if there's anything common across them, cohorts of responders versus non-responders and so forth.(25:35):So we started by collecting a lot of instruction data. So we started with the foundation models. We had strong pathology image foundation models, and then we collected a lot of instruction data where we have images, questions, corresponding answers. And we really leveraged a lot of the data that we had here at Brigham and MGH. We're obviously teaching hospitals. We have questions, we have existing teaching training materials and work closely with pathologists at multiple institutions to collect that data. And then finally trained a multimodal large language model where we could give it a whole slide image, start asking questions, what was in the image, and then it started generating all these entrusting morphologic descriptions. But then the challenge of course is that how do you validate this? So then we created validation data sets, validated on what multiple choice questions on free flowing questions where multiple pathologists, we had a panel of seven pathologists look through every response from our model as well as more generic models like the OpenAI, GPT-4 and BiomedCLIP and other models that are publicly available, and then compare how well this pathology specific model does in comparison to some of those other models.(26:58):And we found that it was very good at morphologic description.Eric Topol (27:05):It's striking though to think now that you have this large language model where you're basically interacting with the slide, and this is rich, but in another way, just to ask you, we talk about multimodal, but what about if you have electronic health record, the person's genome, gut microbiome, the immune status and social demographic factors, and all these layers of data, environmental exposures, and the pathology. Are we going to get to that point eventually?Faisal Mahmood (27:45):Yeah, absolutely. So that's what we're trying to do now. So I think that it's obviously one step at a time. There are some data types that we can very easily integrate, and we're trying to integrate those and really have PathChat as being a binder to all of that data. And pathology is a very good binder because pathology is medicine's ground truth, a lot of the fundamental decisions around diagnosis and prognosis and treatment trajectory is all sort of made in pathology. So having everything else bind around the pathology is a very good idea and indication. So for some of these data types that you just mentioned, like electronic medical records and radiology, we could very easily go that next step and build integrative models, both in terms of building the foundation model and then linking with language and getting it to generate responses and so forth. And for other data types, we might need to do some more specific training data types that we don't have enough data to build foundation models and so forth. So we're trying to expand out to other data types and see how pathology can act as a binder.Eric Topol (28:57):Well if anybody's going to build it, I'm betting on you and your team there, Faisal. Now what this gets us to is the point that, was it 96% or 95% of pathologists in this country are basically in an old era, we're not eking out so much information from slides that they could, and here you're kind of in another orbit, you're in another world here whereby you're coming up with information. I mean things I never thought really the prognosis of a patient over extended period of time, the sensitivity of drugs to the tumor from the slide, no less the driver mutations to be able to, so you wouldn't even have to necessarily send for mutations of the cancer because you get it from the slide. There's so much there that isn't being used. It's just to me unfathomable. Can you help me understand why the pathology community, now that I know you're not actually a pathologist, but you're actually trying to bring them along, what is the reason for this resistance? Because there's just so much information here.Faisal Mahmood (30:16):So there are a number of different reasons. I mean, if you go into details for why digital pathology is not actively happening. Digitizing an entire department is expensive, retaining large amounts of slides is expensive. And then the value proposition in terms of patient care is definitely there. But the financial incentives, reimbursement around AI is not quite there yet. It's slowly getting there, but it's not quite there yet. In the meantime, I think what we can really focus on, and what my group is thinking a lot about is that how can we democratize these models by using what the pathologists already have and they all have a microscope and most of them have a microscope with a camera attached to it. Can we train these models on whole slide images like we have them and adapt them to just a camera coupled to a microscope? And that's what we have done for PathChat2.(31:23):I think one of the demos that we showed after the article came out was that you could use PathChat on your computer with the whole slide image, but you can also use it with a microscope just coupled to a camera and you put a glass light underneath. And in an extreme lower source setting, you can also use it with just a cell phone coupled to a microscope. We're also building a lighter weight version of it that wouldn't require internet, so it would just be completely locally deployed. And then it could be active in lower source settings where sometimes sending a consult can take a really, really long time, and quite often it's not very easy for hospitals in lower source settings to track down a patient again once they've actually left because they might've traveled a long distance to get to the clinic and so forth. So the value of having PathChat deployed in a lower source setting where it can run locally without internet is just huge because it can accelerate the diagnosis so much. In particular for very simple things, which it's very, very good at making a diagnosis for those cases.Eric Topol (32:33):Oh, sure. And it can help bridge inequities, I mean, all sorts of things that could be an outgrowth of that. But what I still having a problem with from the work that you've done and some of the other people that well that are working assiduously in this field, if I had a biopsy, I want all the information. I don't want to just have the old, I would assume you feel the same way. We're not helping patients by not providing the information that's there just with a little help from AI. If it's going to take years for this transformation to occur, a lot of patients are going to miss out because their pathologists are not coming along.Faisal Mahmood (33:28):I think that one way to of course solve this would be to have it congressionally mandated like we had for electronic medical records. And there are other arguments to be made. It's been the case for a number of different hospitals have been sued for losing slides. So if you digitize all your slides and you're not going to lose them, but I think it will take time. So a lot of hospitals are making these large investments, including here at the Brigham and MGH, but it will take time for all the scanners, all the storage solutions, everything to be in place, and then it will also take time for pathologists to adapt. So a lot of pathologists are very excited about the new technology, but there are also a lot of pathologists who feel that their entire career has been diagnosing cases or using a microscope and slide. So it's too big of a transition for them. So I think there'll obviously be some transition period where both would coexist and that's happening at a lot of different institutions.Eric Topol (34:44):Yeah, I get what you're saying, Faisal, but when I wrote Deep Medicine and I was studying what was the pathology uptake then of deep learning, it was about 2% and now it's five years later and it's 4% or 5% or whatever. This is a glacial type evolution. This is not keeping up with how the progress that's been made. Now, the other thing I just want to ask you before finishing up, there are some AI pathology companies like PathAI. I think you have a startup model Modella AI, but what can the companies do when there's just so much reluctance to go into the digital era of pathology?Faisal Mahmood (35:31):So I think that this has been a big barrier for most pathology startups because around seven to eight years ago when most of these companies started, the hope was that digital pathology would happen much faster than it actually has. So I think one thing that we're doing at Modella is that we understand that the adoption of digital pathology is slow. So everything that we are building, we're trying to enable it to work with the current solutions that exist. So a pathologist can capture images from a pathology slide right in their office with a camera with a microscope and PathChat, for example, works with that. And then the next series of tools that we're developing around generative AI would also be developed in a manner that it would be possible to use just a camera coupled to a microscope. So I think that I do feel that all of these pathology AI companies would have been doing much, much better if everything was digital, because adopting the tools that they developed would very straightforward. Right now, the barrier is that even if you want to deploy an AI driven solution, if your hospital is not entirely digital, it's not possible to do that. So it requires this huge upfront investment.Eric Topol (37:06):Yeah, no, it's extraordinary to me. This is such an exciting time and it's just not getting actualized like it could. Now, if somebody who's listening to our conversation has a relative or even a patient or whatever that has a biopsy and would like to get an enlightened interpretation with all the things that could be found that are not being detected, is there a way to send that to a center that is facile with this? Or if that's a no go right now?Faisal Mahmood (37:51):So I think at the moment it's not possible. And the reason is that a lot of the generic AI tools are not ready for this. The models are very, very specific for specific purposes. The generalist models are just getting started, but I think that in the years to come, this would be a competitive edge for institutions who do adopt AI. They would definitely have a competitive edge over those who do not. We do from time to time, receive requests from patients who want us to run their slides on the cancers of unknown primary tool that we built. And it depends on whether we are allowed to do so or not, because it has to go through a regular diagnostic first and how much information can we get from the patient? But it's on a case by case basis.Eric Topol (38:52):Well, I hope that's going to change soon because you have been, your team there has just been working so hard to eke out all that we can learn from a path slide, and it's extraordinary. And it made me think about what we knew five years ago, which already was exciting, and you've taken that to the fifth power now or whatever. So anyway, just to congratulate you for your efforts, I just hope that it will get translated Faisal. I'm very frustrated to learn how little this is being adopted here in this country, a rich country, which is ignoring the benefits that it could provide for patients.Faisal Mahmood (39:40):Yeah. That's our goal over the next five years. So the hope really is to take everything that we have developed so far and then get it in aligned with where the technology currently is, and then eventually deploy it both at our institution and then across the country. So we're working hard to do that.Eric Topol (40:03):Well, maybe patients and consumers can get active about this and demand their medical centers to go digital instead of living in an analog glass slide world, right? Yeah, maybe that's the route. Anyway, thank you so much for reviewing at this pace of your publications. It's pretty much unparalleled, not just in pathology AI, but in many parts of life science. So kudos to you, Richard Chen, and your group and so many others that have been working so hard to enlighten us. So thanks. I'll be checking in with you again on whatever the next model that you build, because I know it will be another really important contribution.Faisal Mahmood (40:49):Thank you so much, Eric. Thanks.**************************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informativeVoluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly helped fund our summer internship programs for 2023 and 2024.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Side note: My X/twitter account @erictopol was hacked yesterday, 27 July, with no help from the platform to regain access despite many attempts. Please don't get scammed! Get full access to Ground Truths at erictopol.substack.com/subscribe

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1392: Cal Newport explores how principles from advanced manufacturing techniques can revolutionize office work. By shifting from a push to a pull system, organizations can alleviate chronic overload, streamline workflows, and enhance productivity. Read along with the original article(s) here: https://www.calnewport.com/blog/2020/06/03/on-running-an-office-like-a-factory/ Quotes to ponder: "If you work in an organization, you know what it's like to have too much to do and not enough resources to do it." "many leaders continue to believe that their organizations thrive when overloaded, often both creating pressure and rewarding those who deliver under duress. It's a popular but pathological approach to management." "When knowledge work processes are managed via push, it's difficult to track tasks in process because so many of them reside in individual email in-boxes, project files, and to-do lists." Episode references: The Broad Institute: https://www.broadinstitute.org/ Breaking Logjams in Knowledge Work: https://sloanreview.mit.edu/article/breaking-logjams-in-knowledge-work/ Deep Work: https://www.amazon.com/Deep-Work-Focused-Success-Distracted/dp/1455586692 Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1392: Cal Newport explores how principles from advanced manufacturing techniques can revolutionize office work. By shifting from a push to a pull system, organizations can alleviate chronic overload, streamline workflows, and enhance productivity. Read along with the original article(s) here: https://www.calnewport.com/blog/2020/06/03/on-running-an-office-like-a-factory/ Quotes to ponder: "If you work in an organization, you know what it's like to have too much to do and not enough resources to do it." "many leaders continue to believe that their organizations thrive when overloaded, often both creating pressure and rewarding those who deliver under duress. It's a popular but pathological approach to management." "When knowledge work processes are managed via push, it's difficult to track tasks in process because so many of them reside in individual email in-boxes, project files, and to-do lists." Episode references: The Broad Institute: https://www.broadinstitute.org/ Breaking Logjams in Knowledge Work: https://sloanreview.mit.edu/article/breaking-logjams-in-knowledge-work/ Deep Work: https://www.amazon.com/Deep-Work-Focused-Success-Distracted/dp/1455586692 Learn more about your ad choices. Visit megaphone.fm/adchoices

More than four years after the pandemic began, a source for COVID-19 still eludes scientists and public health officials. The mystery has given rise to a slew of hypotheses ranging from natural zoonotic transmission to lab leaks. But to get to the bottom and find the real source of the virus, you have to start with the evidence. Alina Chan is a scientific advisor at the Broad Institute and the co-author of the book, Viral: The Search for the Origin of COVID-19. She and her co-author Matt Ridley follow one evidence thread to the next in order to get closer to the truth. Alina joins Greg to chat about the two dominant hypotheses on COVID-19's source, the challenges and methodology of identifying a virus' origin, and why it's crucial we find out where COVID-19 came from. *unSILOed Podcast is produced by University FM.*Episode Quotes:The debate over studying high-risk pathogens43:50: There are definitely people who think that all of this research should be banned, but I think that there should be a certain amount allowed to continue. Again, this sort of research, where there's actually any pandemic risk, constitute an extremely small fraction of virology. So, I would say, like, less than even a percent, maybe even less than that. So, most virology doesn't even concern animal viruses. And those that do often do not pose a risk to cause outbreaks in people. But there are these types of research projects that are now becoming more and more trendy around the world, following in the footsteps of U.S. leaders to take these pathogens that could cause outbreaks in people and study them in the labs. And it's unclear where the risk is because some of these labs are doing it at such low biosafety levels. Or is it because there are so many of these high-biosafety labs now, and the work is increasing, yet in these labs there's still room for human error.Public vs. scientists51:56: Your political affiliation doesn't determine anymore whether you think this virus was natural or came through a lab. I would say that the difference between the public and scientists is that scientists, especially experts, tend to lean on priors very heavily, as well as peer-reviewed literature. Is the focus on avoiding retroactive blame or preventing future research constraints?43:03: I think it's both. So, since then, and over the past few years, you've seen so many letters by virologists. Dozens of them have signed letters saying we are totally fine in the U.S. We do not need any more oversight or regulation. We are good at self-auditing and self-inspecting. We don't need any external oversight over our work. So, there's a clear fear amongst virologists that if this pandemic was started by a lab accident in Wuhan, they would become constrained as well, and that people would also perceive them to no longer just be the good guys but to be a source of risk and danger.Why bats carry so many viruses24:45: I think bats, aside from humans, are probably the most interesting mammalian species out there for virologists. It's because these bats have been found to carry so many different types of pathogens, many of which can jump into people. So, like Ebola, coronaviruses, both MERS and SARS were found to have come from reservoirs, for example, but they're quite similar to humans in the context that they live in large groups. So, you go into one of these caves, easily millions of bats in there, but actually, they're quite different. So, they can fly, and so their body has to adapt to handle that really high heat that happens when you're flapping your wings at such a high speed. And that is related to traits in bats that help them to coexist with so many of these viruses. So, these viruses, while they cause very severe diseases in people, in bats, they just live mostly in the gut and don't cause any severe disease. So, bats have this invulnerability in a sense to all these very dangerous pathogens.Show Links:Recommended Resources:Dr. Peter Daszak - EcoHealth Alliance Wuhan institute of VirologyRalph S. Baric“The proximal origin of SARS-CoV-2” | Nature MedicineGood Judgment case studyGuest Profile:Faculty Profile at Broad Institute Her Work:Viral: The Search for the Origin of COVID-19

In this episode, Tim Lucas at The NeuroTech Institute and Duane Mancini discuss why Universities have historically struggled to commercialize innovation, the Broad Institute, how he modeled the NeuroTech Institute after it, achieving clinical adoption, and so much more. Tim Lucas LinkedIn The NeuroTech Institute Website Duane Mancini LinkedIn Project Medtech LinkedIn Project Medtech Website

In early June, Dr. Alina Chan published an essay in The New York Times arguing what the media once considered a conspiracy: Covid-19 originated in the Wuhan lab. The essay made waves on social media. To some, it was a breath of fresh air from the intelligentsia that validated long-held beliefs about where the costliest pandemic in modern history started. But to others, like our other guest today, it was irresponsible and scientifically misleading. Dr. John Moore is a prominent virologist and professor at Cornell University's Weill Medical College. He penned a rebuttal for “The Nation” in which he argued that The New York Times is badly failing their readers on Covid by publishing Chan's argument. Chan is not a virologist; she's a molecular biologist at Harvard and MIT's Broad Institute. Early on in the pandemic, however, she took an interest in understanding the origins of Covid. She then published a book about lab leak that she claims drew the ire of the media and scientific establishment. In this episode of We The 66, we share BOTH of their perspectives.

Alina Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of Viral: The Search for the Origin of Covid-19. She was a member of the Pathogens Project, which the Bulletin of the Atomic Scientists organized to generate new thinking on responsible, high-risk pathogen research.Why the Pandemic Probably Started in a Lab, in 5 Key Points, by Alina Chan for the New York TimesJon Stewart on Vaccine Science and the Wuhan Lab Theory - The Late Show with Stephen ColbertGround News gathers news coverage from around the world, empowers free thinking, and makes media bias explicit. Subscribe through my link at https://check.ground.news/Next for 15% off your subscription.If You Liked This Conversation, You'll Probably Like These Episodes of Where We Go Next:105: Religious Cults, Fringe Science, and the Need for Belief, with Ross Blocher & Carrie Poppy52: The Amazing and Optimistic Future of Augmented Reality, with David Rose47: A New Philosophy of Progress and Why We Don't Have Flying Cars, with Jason Crawford31: Investigating the Origins of COVID-19, with Alina ChanFollow Alina on X: @ayjchan----------If you liked this episode, consider sharing it with someone you think might like it too.Email: michael@wherewegonext.comInstagram: @wwgnpodcast

Subscribe to our channel: https://www.youtube.com/@optispan Check out the Gurkar lab: http://agresearchlab.com/ In May 2024, Matt delivered a talk at the 2024 Glenn AFAR Grantee Conference in Santa Barbara, California and met with several people doing interesting work in the longevity field. One of these was Aditi Gurkar, an assistant professor in the University of Pittsburgh's Aging Institute, Division of Geriatric Medicine whose lab focuses on the downstream effects of DNA damage on aging. Prior to setting up her lab at the University of Pittsburgh, Aditi conducted research on the impact of DNA damage on aging at Scripps Research. She also completed postdoctoral training at Massachusetts General Hospital/Harvard Medical School and the Broad Institute of MIT and Harvard, where she focused on the tumor suppressor p53 as well as on autophagy regulation. Aditi received a PhD and a B.S. from the Boston University School of Medicine and Florida International University in Miami respectively. Matt and Aditi spend much of this episode chatting about senescent cells: how Aditi developed a focus on cellular senescence and found its relevance to aging, potential therapeutic benefits of senescent cell clearance, and the much-debated question of how to define a senescent cell. They also discuss the importance of "zooming out" from narrow areas of focus in the geroscience field to find new solutions and of breaking your own models on your way to productive new directions in science. Producers: Tara Mei, Nicholas Arapis Video Editor: Jacob Keliikoa DISCLAIMER: The information provided on the Optispan podcast is intended solely for general educational purposes and is not meant to be, nor should it be construed as, personalized medical advice. No doctor-patient relationship is established by your use of this channel. The information and materials presented are for informational purposes only and are not a substitute for professional medical advice, diagnosis, or treatment. We strongly advise that you consult with a licensed healthcare professional for all matters concerning your health, especially before undertaking any changes based on content provided by this channel. The hosts and guests on this channel are not liable for any direct, indirect, or other damages or adverse effects that may arise from the application of the information discussed. Medical knowledge is constantly evolving; therefore, the information provided should be verified against current medical standards and practices. More places to find us: Twitter: https://twitter.com/optispanpodcast Twitter: https://twitter.com/optispan Twitter: https://twitter.com/mkaeberlein Linkedin: https://www.linkedin.com/company/optispan https://www.optispan.life/ Hi, I'm Matt Kaeberlein. I spent the first few decades of my career doing scientific research into the biology of aging, trying to understand the finer details of how humans age in order to facilitate translational interventions that promote healthspan and improve quality of life. Now I want to take some of that knowledge out of the lab and into the hands of people who can really use it. On this podcast I talk about all things aging and healthspan, from supplements and nutrition to the latest discoveries in longevity research. My goal is to lift the veil on the geroscience and longevity world and help you apply what we know to your own personal health trajectory. I care about quality science and will always be honest about what I don't know. I hope you'll find these episodes helpful!

This week on Embrace the Squiggle Colleen and Kristine speak with Rachel Lerner-Ley about how being interesting and authentic landed her the job she wanted! We dig into who you shouldn't conform in interviews and how your unique perspectives and experiences are what will land you the right gig for you. Rachel Lerner-Ley is the Merkin Prize & New Directions Scholars Program Manager at the Broad Institute of MIT and Harvard. Prior to the Broad, she worked in the artistic department at Cleveland Play House (recipient of the 2015 Regional Theatre Tony Award), first as the artistic associate and then as the literary manager and resident dramaturg. Other theatre credits include: Williamstown Theatre Festival, The Civilians, Actors Theatre of Louisville, Barrington Stage Company, WildWind Performance Lab at Texas Tech University, and Girl Be Head. Over the course of her theatre career, she read & reported on hundreds of new play submissions and dramaturged 30+ productions & workshops ranging from world premieres to musicals and classics. She is a graduate of Smith College and a member of the LMDA (Literary Managers and Dramaturgs of the Americas).Connect with Rachel at https://www.linkedin.com/in/rlernerley/ Connect with Colleen at www.maxady.comand on Linkedin at www.linkedin.com/in/comaraConnect with Krsitine at https://www.kristinethody.comand on Linkedin at https://www.linkedin.com/in/kristinethodySubscribe to the podcast Embrace the Squiggle and listen every week for a new career adventure!

Regents of the University of California v. Broad Institute, Inc.

In this episode, recorded during Abundance360 2024, Peter and Eric discuss AI policy, government struggles, and AI's global impact.   06:33 | AI's Power and Impact Today 15:03 | AI and the Fight Against Misinformation 27:12 | Government Struggles with Rapid Tech Growth Eric Schmidt is best known as the CEO of Google from 2001-2011, including as the Executive Chairman of Google, Alphabet, and later as their Technical Advisor until 2020. He was also on the board of directors at Apple from 2006-2009 and is currently the Chairman of the board of directors at the Broad Institute. From 2019 to 2021, Eric chaired the National Security Commission on Artificial Intelligence. He's also a founding partner at Investment Endeavors, a VC firm.  Learn more about Abundance360: https://www.abundance360.com/summit  ____________ I only endorse products and services I personally use. To see what they are,  please support this podcast by checking out our sponsors:  Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/   AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter  ____________ I send weekly emails with the latest insights and trends on today's and tomorrow's exponential technologies. Stay ahead of the curve, and sign up now:  Tech Blog Get my new Longevity Practices book for free: https://www.diamandis.com/longevity My new book with Salim Ismail, Exponential Organizations 2.0: The New Playbook for 10x Growth and Impact, is now available on Amazon: https://bit.ly/3P3j54J _____________ Connect With Peter: Twitter Instagram Youtube Moonshots Learn more about your ad choices. Visit megaphone.fm/adchoices

Public Health Careers podcast episode with Pat Leomi, MPH

The engaging memoir of a legendary president of Wellesley College known for authentic and open-hearted leadership, who drove innovation with power and love. The Claims of Life: A Memoir (The MIT Press, 2023) traces the emergence of a young woman who set out believing she wasn't particularly smart but went on to meet multiple tests of leadership in the American academy—a place where everyone wants to be heard and no one wants a boss. In college, Diana Chapman met Chris Walsh, who became a towering figure in academic science. Their marriage of fifty-seven years brought them to the forefront of revolutions in higher education, gender expectations, health-care delivery, and biomedical research.  The Claims of Life offers readers an unusually intimate view of trustworthy leadership that begins and ends in self-knowledge. During a transformative fourteen-year Wellesley presidency, Walsh advanced women's authority, compassionate governance, and self-reinvention. After Wellesley, Walsh's interests took her to the boards of five national nonprofits galvanizing change. She kept counsel with Nobel laureates, feminist icons, and even the Dalai Lama, seeking solutions to the world's climate crisis. With an ear tuned to social issues, The Claims of Life is an inspiring account of a life lived with humor, insight, and meaning that will surely leave a lasting impression on its readers. Diana Chapman Walsh is President Emerita of Wellesley College and an emerita member of the governing boards of MIT and Amherst College. She was a trustee of the Kaiser Family Foundation, the Institute for Healthcare Improvement, and the Mind and Life Institute, and also chaired the Broad Institute's inaugural board and cofounded the Council on the Uncertain Human Future. Caleb Zakarin is editor at the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

The engaging memoir of a legendary president of Wellesley College known for authentic and open-hearted leadership, who drove innovation with power and love. The Claims of Life: A Memoir (The MIT Press, 2023) traces the emergence of a young woman who set out believing she wasn't particularly smart but went on to meet multiple tests of leadership in the American academy—a place where everyone wants to be heard and no one wants a boss. In college, Diana Chapman met Chris Walsh, who became a towering figure in academic science. Their marriage of fifty-seven years brought them to the forefront of revolutions in higher education, gender expectations, health-care delivery, and biomedical research.  The Claims of Life offers readers an unusually intimate view of trustworthy leadership that begins and ends in self-knowledge. During a transformative fourteen-year Wellesley presidency, Walsh advanced women's authority, compassionate governance, and self-reinvention. After Wellesley, Walsh's interests took her to the boards of five national nonprofits galvanizing change. She kept counsel with Nobel laureates, feminist icons, and even the Dalai Lama, seeking solutions to the world's climate crisis. With an ear tuned to social issues, The Claims of Life is an inspiring account of a life lived with humor, insight, and meaning that will surely leave a lasting impression on its readers. Diana Chapman Walsh is President Emerita of Wellesley College and an emerita member of the governing boards of MIT and Amherst College. She was a trustee of the Kaiser Family Foundation, the Institute for Healthcare Improvement, and the Mind and Life Institute, and also chaired the Broad Institute's inaugural board and cofounded the Council on the Uncertain Human Future. Caleb Zakarin is editor at the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/gender-studies

The engaging memoir of a legendary president of Wellesley College known for authentic and open-hearted leadership, who drove innovation with power and love. The Claims of Life: A Memoir (The MIT Press, 2023) traces the emergence of a young woman who set out believing she wasn't particularly smart but went on to meet multiple tests of leadership in the American academy—a place where everyone wants to be heard and no one wants a boss. In college, Diana Chapman met Chris Walsh, who became a towering figure in academic science. Their marriage of fifty-seven years brought them to the forefront of revolutions in higher education, gender expectations, health-care delivery, and biomedical research.  The Claims of Life offers readers an unusually intimate view of trustworthy leadership that begins and ends in self-knowledge. During a transformative fourteen-year Wellesley presidency, Walsh advanced women's authority, compassionate governance, and self-reinvention. After Wellesley, Walsh's interests took her to the boards of five national nonprofits galvanizing change. She kept counsel with Nobel laureates, feminist icons, and even the Dalai Lama, seeking solutions to the world's climate crisis. With an ear tuned to social issues, The Claims of Life is an inspiring account of a life lived with humor, insight, and meaning that will surely leave a lasting impression on its readers. Diana Chapman Walsh is President Emerita of Wellesley College and an emerita member of the governing boards of MIT and Amherst College. She was a trustee of the Kaiser Family Foundation, the Institute for Healthcare Improvement, and the Mind and Life Institute, and also chaired the Broad Institute's inaugural board and cofounded the Council on the Uncertain Human Future. Caleb Zakarin is editor at the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/biography

Thomas Jefferson famously said he'd prefer newspapers without government over government without newspapers. In large parts of the United States today, government exists without independent news sources—undermining accountability and diminishing civic participation. Ellen Clegg and Dan Kennedy tell us that despite these troubling trends, there's much to celebrate in the work of community news outlets around the country.  Clegg spent over three decades at The Boston Globe and retired in 2018 after four years of running the opinion pages. In between stints at the Globe, she was deputy director of communications at the Broad Institute of MIT and Harvard. She is a member of the steering committee for the Elizabeth Neuffer Fellowship at the International Women's Media Foundation and the co-founder and co-chair of Brookline.News, a nonprofit startup news organization. Kennedy is a Northeastern University professor in the School of Journalism and a nationally known media commentator. He was a panelist on the GBH News television program “Beat the Press” and a weekly columnist for the network. He was also a columnist for The Guardian and produces Media Nation, an online publication that serves as a media watchdog. Kennedy is a recipient of the Yankee Quill Award from the New England Academy of Journalists and the James W. Carey Journalism Award from the Media Ecology Association. See omnystudio.com/listener for privacy information.

A recent study on how to get rid of microplastics in water sparked presenter Marnie Chesterton's curiosity. When she turns on the tap in her kitchen each day, what comes out is drinkable, clean water. But where did it come from, and what's in it? Dr Stewart Husband from Sheffield University answers this and more, including listener questions from around the UK. Is water sterile? Should I use a filter? And why does my water smell like chlorine? Also, new research indicates that bumblebees can show each other how to solve puzzles too complex for them to learn on their own. Professor Lars Chittka put these clever insects to the test and found that they could learn through social interaction. How exactly did the experiment work, and what does this mean for our understanding of social insects? Reporter Hannah Fisher visits the bee lab at Queen Mary University in London. And finally, more than 20 million years ago, our branch of the tree of life lost its tail. At that point in time, apes split from another animal group, monkeys. Now, geneticist Dr Bo Xia at the Broad Institute of MIT and Harvard thinks he may have found the specific mutation that took our tails. Marnie speaks with evolutionary biologist Dr Tom Stubbs from the Open University about why being tail-less could be beneficial. What would a hypothetical parallel universe look like where humans roam the earth, tails intact? And what would these tails look like? Presenter: Marnie Chesterton Producers: Louise Orchard, Florian Bohr, Jonathan Blackwell, Imaan Moin Editor: Martin Smith Production Co-ordinator: Jana Bennett-Holesworth  BBC Inside Science is produced in collaboration with the Open University.

In this live taping of Lab Rats to Unicorns, John Flavin and Jeff Karp discuss the challenges and triumphs of bringing lab research to market, the evolving landscape of biotech startups, and the importance of interdisciplinary collaboration in driving scientific breakthroughs. Dr. Jeff Karp is a renowned figure in the biotechnology sector, known for his groundbreaking work and innovative approaches in the field. With a career that bridges both academic research and industry application, Dr. Karp has been at the forefront of developing cutting-edge biotechnological solutions to some of healthcare's most pressing challenges. Jeff is the Distinguished Chair at Brigham and Women's Hospital, a Professor at Harvard Medical School, Affiliate Faculty at MIT & The Broad Institute, and Principal Faculty at the Harvard Stem Cell Institute His expertise spans a range of areas, including drug delivery systems, regenerative medicine, and tissue engineering. Dr. Karp's work is characterized by a commitment to translational research, aiming to bring laboratory discoveries into practical medical use. His leadership in various biotech initiatives and collaborations has been instrumental in driving forward the boundaries of medical science and patient care. Jeff has won numerous awards around his innovations and entrepreneurial successes.

Why scientists are trying to make anemones act like corals, and why it's so hard to make pharmaceuticals for brain diseases   First up on this week's show, coaxing anemones to make rocks. Newsletter Editor Christie Wilcox joins host Sarah Crespi to discuss the difficulties of raising coral in the lab and a research group that's instead trying to pin down the process of biomineralization by inserting coral genes into easy-to-maintain anemones.   Next on the show, a look at why therapeutics for both neurodegenerative disease and psychiatric illness are lagging behind other kinds of medicines. Steve Hyman, director of the Stanley Center for Psychiatric Research at the Broad Institute, talks with Sarah about some of the stumbling blocks to developing drugs for the brain—including a lack of diverse genome sequences—and what researchers are doing to get things back on track.   Finally, in a sponsored segment from the Science/AAAS Custom Publishing Office, associate editor Jackie Oberst discusses with Thomas Fuchs, dean of artificial intelligence (AI) and human health and professor of computational pathology and computer science at the Icahn School of Medicine at Mount Sinai, the potential and evolving role of AI in health care. This segment is sponsored by the Icahn School of Medicine at Mount Sinai.   This week's episode was produced with help from Podigy.   About the Science Podcast   Authors: Christie Wilcox; Sarah Crespi   Episode page: https://www.science.org/doi/10.1126/science.adm6756