Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
Powerful Cancer-Fighting Foods and Their Role in Body Repair
Curator: Dr. Sudipta Saha, Ph.D.
In the search for dietary approaches to prevent and fight cancer, certain foods have been found to possess potent anti-cancer properties. These foods not only help reduce the risk of cancer but also assist in repairing the body. Five such foods are green tea, broccoli like vetables, papaya, purple potatoes, and pomegranate—and the bioactive compounds responsible for their benefits.
1. Green Tea
Green tea, particularly rich in the catechin epigallocatechin gallate (EGCG), has gained considerable attention for its cancer-fighting properties. EGCG functions as a potent antioxidant, neutralizing free radicals and reducing oxidative stress, which is a key factor in the development of cancer. Studies suggest that EGCG can inhibit cancer cell proliferation by disrupting the signaling pathways essential for cell growth and survival, especially in breast, prostate, and colorectal cancers. Additionally, green tea has been shown to enhance the body’s immune function, making it more effective at targeting abnormal cells. EGCG induces apoptosis (programmed cell death) in cancer cells, halts angiogenesis (the formation of new blood vessels that nourish tumors), and inhibits metastasis (the spread of cancer cells to other parts of the body).
2. Broccoli and Cauliflower
Cruciferous vegetables like broccoli and cauliflower are rich in sulforaphane, a compound known for its detoxifying and anti-carcinogenic properties. Sulforaphane activates the body’s natural detoxification enzymes, which help eliminate carcinogens before they can damage cells. Moreover, it has been shown to inhibit the growth of various cancer cells, including those of the colon, breast, and prostate. Sulforaphane enhances the activity of phase II detoxification enzymes and induces apoptosis in cancer cells. It also inhibits histone deacetylase, an enzyme associated with cancer cell growth, thus preventing cancerous cells from replicating.
3. Papaya
Papaya is rich in carotenoids such as beta-carotene, lycopene, and beta-cryptoxanthin, which are powerful antioxidants. These compounds neutralize free radicals, reducing oxidative stress that can lead to cancer. Lycopene, in particular, has been linked to a lower risk of cancers, including those of the prostate, breast, and lung. Papaya also contains other bioactive compounds that help modulate immune responses, supporting the body’s ability to identify and destroy cancer cells. Carotenoids act by scavenging free radicals and reducing oxidative stress. Lycopene has also been shown to regulate cell cycle progression and inhibit growth factor signaling in cancer cells.
4. Purple Potatoes
Purple potatoes are unique due to their high levels of anthocyanins, which not only provide them with their distinctive color but also contribute to cancer prevention. Studies suggest that anthocyanins in purple potatoes help repair damaged tissues by promoting stem cell regeneration. They also have anti-inflammatory and anti-proliferative effects, which are crucial for halting cancer growth. Anthocyanins inhibit the growth of cancer cells by inducing cell cycle arrest and promoting the repair of damaged tissues through stem cell activation.
5. Pomegranate
Pomegranates are rich in ellagitannins, compounds that break down into ellagic acid in the body. Ellagic acid has been shown to possess anti-cancer properties by inhibiting tumor growth and promoting apoptosis in cancer cells. Pomegranate juice has demonstrated potential in reducing the progression of cancers such as breast and prostate cancer, due to its ability to suppress inflammation and oxidative stress. Ellagitannins and their metabolites inhibit cell proliferation and induce apoptosis. They also act by reducing inflammation and inhibiting the pathways involved in cancer cell survival and growth.
Conclusion
Incorporating foods like green tea, broccoli, papaya, purple potatoes, and pomegranates into your diet may help fight cancer and promote the repair of damaged tissues. The bioactive compounds found in these foods—EGCG, sulforaphane, carotenoids, anthocyanins, and ellagitannins—work through various mechanisms to inhibit cancer cell growth, induce apoptosis, and support the body’s natural repair processes. Including these nutrient-dense foods in your diet may contribute to overall health and resilience against cancer.
Xenotransplantation: Pioneering a New Era of Organ Availability
Reporter: Dr. Sudipta Saha, Ph.D.
The 2024 World Medical Innovation Forum (WMIF) spotlighted xenotransplantation as a transformative solution to the organ shortage crisis. By leveraging genetically modified pig organs, this emerging field offers a new source of transplants, expanding life-saving care options.
Key breakthroughs in 2024 have brought new hope for patients, but significant hurdles remain, including immunological rejection. Ongoing research focuses on developing immunosuppressive strategies and enhancing organ compatibility.
Collaboration between scientists, clinicians, and regulatory bodies is essential for xenotransplantation’s future. Experts predict wider clinical availability within the next decade, potentially reshaping organ replacement.
This revolutionary step in organ transplantation holds promise for patients and could redefine the future of transplant care globally. Here’s a comprehensive report covering the research contributions of the panelists from the Xenotransplantation: Game Changing Organ Replacement discussion:
1. Jason Gerberry
Specialty Pharma and SMid-Cap Biotech Analyst, BofA Global Research
Gerberry is a prominent financial analyst with deep expertise in specialty pharmaceuticals and small-to-mid-cap biotechnology firms. His research focuses on investment trends, market dynamics, and the financial viability of innovative medical solutions such as xenotransplantation. At WMIF 2024, he provided insights on how breakthroughs in the field could impact the biotech sector, including the potential for significant investments driven by advancements in gene editing and organ transplantation technologies. Gerberry’s analysis offers critical perspectives on the commercial and economic landscape surrounding xenotransplantation.
2. Joren Madsen, MD, PhD
Director, MGH Transplant Center
Paul S. Russell/Warner-Lambert Professor of Surgery, Harvard Medical School Dr. Madsen is a leader in transplant surgery and immunology. His research focuses on allograft rejection and immunosuppressive strategies to enhance transplant tolerance. He has been pivotal in advancing clinical transplant practices at Massachusetts General Hospital (MGH) and has made significant contributions to xenotransplantation research by exploring how genetically engineered pig organs could help mitigate immune rejection in human recipients. Madsen’s work is key to translating laboratory findings into clinical applications.
3. Tatsuo Kawai, MD, PhD
Director of the Legorreta Center for Clinical Transplantation Tolerance
A. Benedict Cosimi Chair in Transplant Surgery, MGH
Dr. Kawai specializes in immune tolerance and organ transplantation. His research emphasizes reducing or eliminating the need for lifelong immunosuppressive drugs in transplant patients. He has led groundbreaking clinical trials on tolerance induction, paving the way for the potential acceptance of xenotransplanted organs without rejection. His research is also closely tied to immune tolerance mechanisms and how xenotransplantation can be made safer for human use.
4. Richard Pierson III, MD
Scientific Director, Center for Transplantation Sciences, MGH
Professor of Surgery, Harvard Medical School
Dr. Pierson is renowned for his work in transplantation immunology, focusing on xenotransplantation. His research addresses the fundamental problem of immune rejection of animal organs in human bodies, particularly tackling hyperacute rejection and graft survival. Dr. Pierson has been instrumental in developing strategies to overcome these barriers by modifying pig genetics and using innovative immunosuppressive therapies, which have brought the field closer to clinical application.
5. Leonardo Riella, MD, PhD
Medical Director of Kidney Transplantation, MGH
Harold and Ellen Danser Endowed Chair in Transplantation, Harvard Medical School
Dr. Riella’s research focuses on kidney transplantation and immunosuppressive therapies aimed at improving long-term graft survival. He has been a significant contributor to the field of xenotransplantation, working on improving immune tolerance and understanding how kidneys from genetically modified pigs can function in human bodies without eliciting strong immune responses. His clinical and translational research is critical for the future of xenotransplantation, particularly in renal applications.
Conclusion
These panelists represent leading voices in xenotransplantation, combining their expertise in surgery, immunology, and biotechnology to address the complex challenges of organ transplantation. Their collaborative efforts at MGH and Harvard Medical School are critical in advancing the science of xenotransplantation, bringing it closer to a clinically viable solution for the global organ shortage crisis.
Nobel Prize in Chemistry 2024 to David Baker, Demis Hassabis and John M. Jumper
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 10/22/2024
ProteinMPNN, which is now available free on the open-source software repository GitHub, will give researchers the tools to make unlimited new designs. “The challenge, of course … is what are you going to design?” Baker says.
In a second Nobel win for AI, the Royal Swedish Academy of Sciences has awarded half the 2024 prize in chemistry to Demis Hassabis, the cofounder and CEO of Google DeepMind, and John M. Jumper, a director at the same company, for their work on using artificial intelligence to predict the structures of proteins. The other half goes to David Baker, a professor of biochemistry at the University of Washington, for his work on computational protein design. The winners will share a prize pot of 11 million Swedish kronor ($1 million).
The potential impact of this research is enormous. Proteins are fundamental to life, but understanding what they do involves figuring out their structure—a very hard puzzle that once took months or years to crack for each type of protein. By cutting down the time it takes to predict a protein’s structure, computational tools such as those developed by this year’s award winners are helping scientists gain a greater understanding of how proteins work and opening up new avenues of research and drug development. The technology could unlock more efficient vaccines, speed up research on cures for cancer, or lead to completely new materials.
Hassabis and Jumper created AlphaFold, which in 2020 solved a problem scientists have been wrestling with for decades: predicting the three-dimensional structure of a protein from a sequence of amino acids. The AI tool has since been used to predict the shapes of all proteins known to science.
“I’ve dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people,” said Demis Hassabis. “AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery. I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery,” he added.
Baker has created several AI tools for designing and predicting the structure of proteins, such as a family of programs called Rosetta. In 2022, his lab created an open-source AI tool called ProteinMPNN that could help researchers discover previously unknown proteins and design entirely new ones. It helps researchers who have an exact protein structure in mind find amino acid sequences that fold into that shape.
Most recently, in late September, Baker’s lab announced it had developed custom molecules that allow scientists to precisely target and eliminate proteins associated with diseases in living cells.
“[Proteins] evolved over the course of evolution to solve the problems that organisms faced during evolution. But we face new problems today, like covid. If we could design proteins that were as good at solving new problems as the ones that evolved during evolution are at solving old problems, it would be really, really powerful,” Baker told MIT Technology Review in 2022.
born 1962 in Seattle, WA, USA. PhD 1989 from University of California, Berkeley, CA, USA. Professor at University of Washington, Seattle, WA, USA and Investigator, Howard Hughes Medical Institute, USA.
University of Washington, Seattle, WA, USA
Howard Hughes Medical Institute, USA
Demis Hassabis “for protein structure prediction”
born 1976 in London, UK. PhD 2009 from University College London, UK. CEO of Google DeepMind, London, UK.
Google DeepMind, London, UK
John M. Jumper “for protein structure prediction”
born 1985 in Little Rock, AR, USA. PhD 2017 from University of Chicago, IL, USA. Senior Research Scientist at Google DeepMind, London, UK.
Google DeepMind, London, UK
The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.
“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.
Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.
The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.
In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.
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This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
2024 Nobel Prize in Physiology or Medicine jointly to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation
Reporter: Aviva Lev-Ari, PhD, RN
Updated 10/22/2024
The revolution in our understanding of transcriptional regulation and dark regions of the genome
The genome of higher eukaryotes are comprised of multiple exonic and intronic regions, with coding and noncoding DNA respectively. Much of the DNA sequence between exonic regions of genes, the sequences encoding the amino acids of a polypeptide, was considered either promoter regions regulating an exonic sequence or ‘junk DNA’, which had merely separated exons and their regulatory elements. It was not considered that this dark DNA or junk DNA was important in regulating transcription of genes. It was felt that most gene regulation occurred in promoter regions by response element factors which bound to specific sequences within these regions.
MicroRNA (miRNA), originally discovered in Caenorhabditis elegans, is found in most eukaryotes, including humans [1–3]. It is predicted that miRNA account for 1-5% of the human genome and regulate at least 30% of protein-coding genes [4–8]. To date, 940 distinct miRNAs molecules have been identified within the human genome [9–12] (http://microrna.sanger.ac.uk accessed July 20, 2010). Although little is currently known about the specific targets and biological functions of miRNA molecules thus far, it is evident that miRNA plays a crucial role in the regulation of gene expression controlling diverse cellular and metabolic pathways.
MiRNA are small, evolutionary conserved, single-stranded, non-coding RNA molecules that bind target mRNA to prevent protein production by one of two distinct mechanisms. Mature miRNA is generated through two-step cleavage of primary miRNA (pri-miRNA), which incorporates into the effector complex RNA-induced silencing complex (RISC). The miRNA functions as a guide by base-pairing with target mRNA to negatively regulate its expression. The level of complementarity between the guide and mRNA target determines which silencing mechanism will be employed; cleavage of target messenger RNA (mRNA) with subsequent degradation or translation inhibition
Fig. (1). MicroRNA maturation and function.
Figure. miRNA maturation and function. Source: Macfarlane LA, Murphy PR. MicroRNA: Biogenesis, Function and Role in Cancer. Curr Genomics. 2010 Nov;11(7):537-61. doi: 10.2174/138920210793175895.
The following is an interview in the journal Journal of Cellular Biology with Dr, Victor Ambros on his discovery of miRNA.
Source: Ambros V. Victor Ambros: the broad scope of microRNAs. Interview by Caitlin Sedwick. J Cell Biol. 2013 May 13;201(4):492-3. doi: 10.1083/jcb.2014pi. PMID: 23671307; PMCID: PMC3653358.
Once, we thought we understood all there was to know about how gene expression is regulated: A cell can tinker with the expression level of a given protein’s messenger RNA by modifying the activity, abundance, and type of transcription factors in the nucleus or with the RNA’s stability once it is made. But then came a surprising story about a short RNA in C. elegans called lin-4, which didn’t encode a protein but prevented expression of the protein encoded by another gene, lin-14, through antisense binding to lin-14 mRNA (1, 2). Today, we know that lin-4 was just the first example of a large number of small RNAs, called microRNAs, which regulate the expression of various other proteins in a similar way.
Victor Ambros, whose lab published that first story about lin-4, has been studying microRNAs (3, 4) and their regulation (5, 6) ever since, pushing forward our understanding of this powerful mechanism. We called him at his office at the University of Massachusetts Medical School to get some perspective on microRNAs and his career and to learn about some of the latest developments in his lab.
“That shared discovery is one of the most precious moments in my career.”
FROM FARM TO LAB TABLE
How did you end up doing a PhD with David Baltimore?
I was the first scientist in my family. My dad was an immigrant from Poland. He came to the States just after World War II and met my mom. They got married, moved to a farm in Vermont, and started farming. My siblings and I grew up amongst the cows and pigs and helped with the haying and cutting corn, stuff like that.
When I was about nine, I got interested in science, and after that I always wanted to be a scientist. I was an amateur astronomer; I built a telescope and started to imagine that I could actually do astronomy or physics as an occupation. But I quickly changed my mind when I reached college, in part because I realized that my math skills weren’t really up to the task of being a physicist and also because I discovered molecular biology and genetics and just fell in love with both subjects. David taught one of the advanced biology classes I took as an undergraduate at MIT, and that probably had some influence on my decision to work with him. After college, I worked as a technician in David’s lab for a year. I liked it a lot and stayed on in his lab when I entered graduate school at MIT. I was lucky because I had gotten a little bit of traction on a project and continued on that as a grad student, so I ended up finishing grad school fairly efficiently.
Had you any idea at the time what the nature of the lin-4 mutant was?
The assumption was that it was a protein product. I mean, nobody ever thought that there would be any other kind of regulator. There really wasn’t any reason to imagine that there were any other kinds of molecules necessary, other than proteins, to carry out everything that’s done in a cell—especially with regard to the regulation of gene expression. The complexity of gene regulation by proteins alone was so enormous that I never imagined—and nobody I knew imagined—that we needed to look for new kinds of regulatory molecules. The realization that lin-4 was antisense to the 3′-untranslated region of lin-14 was totally the result of communication between Gary and me. That shared discovery is one of the most precious moments in my career. But at the time I didn’t realize that this might be the first example of a general mechanism for regulating gene expression because I was prone to thinking that whatever I was studying in the worm was not generally applicable. It wasn’t until genome sequences were made available that the prevalence of this mechanism became clear.
THE RIGHT CONTEXT
You’ve moved to studying processes that modulate microRNA function…
One protein we’ve studied is called Nhl-2. It’s an example of an emerging class of proteins that can modulate, positively or negatively, the RNA-induced silencing complex (RISC) that inhibits mRNAs targeted by microRNAs. This class of genes may have either general effects on RISC activity or, in some cases, more specific effects. One area of interest in the lab right now is trying to understand the specific outcomes for the regulation of particular microRNAs. Do they always interact with all their targets, or is their activity on some targets promoted or inhibited at the expense of other targets? Can their interaction with certain targets be modified depending on context? We’re using genetic and genomic approaches to identify new modulatory cofactors.
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Victor Ambros was born in 1953 in Hanover, New Hampshire, USA. He received his PhD from Massachusetts Institute of Technology (MIT), Cambridge, MA, in 1979 where he also did postdoctoral research 1979-1985. He became a Principal Investigator at Harvard University, Cambridge, MA in 1985. He was Professor at Dartmouth Medical School from 1992-2007 and he is now Silverman Professor of Natural Science at the University of Massachusetts Medical School, Worcester, MA.
Gary Ruvkun was born in Berkeley, California, USA in 1952. He received his PhD from Harvard University in 1982. He was a postdoctoral fellow at Massachusetts Institute of Technology (MIT), Cambridge, MA, 1982-1985. He became a Principal Investigator at Massachusetts General Hospital and Harvard Medical School in 1985, where he is now Professor of Genetics.
This year’s Nobel Prize honors two scientists for their discovery of a fundamental principle governing how gene activity is regulated.
The information stored within our chromosomes can be likened to an instruction manual for all cells in our body. Every cell contains the same chromosomes, so every cell contains exactly the same set of genes and exactly the same set of instructions. Yet, different cell types, such as muscle and nerve cells, have very distinct characteristics. How do these differences arise? The answer lies in gene regulation, which allows each cell to select only the relevant instructions. This ensures that only the correct set of genes is active in each cell type.
Victor Ambros and Gary Ruvkun were interested in how different cell types develop. They discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function.
Ambros and Ruvkun were interested in genes that control the timing of activation of different genetic programs, ensuring that various cell types develop at the right time. They studied two mutant strains of worms, lin-4 and lin-14, that displayed defects in the timing of activation of genetic programs during development. The laureates wanted to identify the mutated genes and understand their function. Ambros had previously shown that the lin-4 gene appeared to be a negative regulator of the lin-14 gene. However, how the lin-14 activity was blocked was unknown. Ambros and Ruvkun were intrigued by these mutants and their potential relationship and set out to resolve these mysteries.
Ambros and Ruvkun performed further experiments showing that the lin-4 microRNA turns off lin-14 by binding to the complementary sequences in its mRNA, blocking the production of lin-14 protein. A new principle of gene regulation, mediated by a previously unknown type of RNA, microRNA, had been discovered! The results were published in 1993 in two articles in the journal Cell.
Ruvkun cloned let-7, a second gene encoding a microRNA. The gene is conserved in evolution, and it is now known that microRNA regulation is universal among multicellular organisms.
Andrew Z. Fire and Craig C. Mello, awarded the Nobel Prize in 2006, described RNA interference, where specific mRNA-molecules are inactivated by adding double-stranded RNA to cells.
Mutations in one of the proteins required for microRNA production result in the DICER1 syndrome, a rare but severe syndrome linked to cancer in various organs and tissues.
Matthew S. Smith is a Contributing Editor for IEEE Spectrum and the former Lead Reviews Editor at Digital Trends.
The Nobel Prize Committee for Physics caught the academic community off-guard by handing the 2024 award to John J. Hopfield and Geoffrey E. Hinton for their foundational work in neural networks.
The pair won the prize for their seminal papers, both published in the 1980s, that described rudimentary neural networks. Though much simpler than the networks used for modern generative AI like ChatGPT or Stable Diffusion, their ideas laid the foundations on which later research built.
Even Hopfield and Hinton didn’t believe they’d win, with the latter telling The Associated Press he was “flabbergasted.” After all, AI isn’t what comes to mind when most people think of physics. However, the committee took a broader view, in part because the researchers based their neural networks on “fundamental concepts and methods from physics.”
“Initially, I was surprised, given it’s the Nobel Prize in Physics, and their work was in AI and machine learning,” says Padhraic Smyth, a distinguished professor at the University of California, Irvine. “But thinking about it a bit more, it was clearer to me why [the Nobel Prize Committee] did this.” He added that physicists in statistical mechanics have “long thought” about systems that display emergent behavior.
Hopfield first explored these ideas in a 1982 paper on neural networks. He described a type of neural network, later called a Hopfield network, formed by a single layer of interconnected neurons. The paper, which was originally categorized under biophysics, said a neural network could retain “memories” from “any reasonably sized subpart.”
Hinton expanded on that work to conceptualize the Boltzmann machine, a more complex neural network described in a 1985 paper Hinton co-authored with David H. Ackley and Terrence J. Sejnowski. They introduced the concept of “hidden units,” additional layers of neurons which exist between the input and output layers of a neural network but don’t directly interact with either. This makes it possible to handle tasks that require a more generalized understanding, like classifying images.
So, what’s the connection to physics?
Hopfield’s paper references the concept of a “spin glass,” a material in which disordered magnetic particles lead to complex interactions. Hinton and his co-authors drew on statistical mechanics, a field of physics that uses statistics to describe the behavior of particles in a system. They even named their network in honor of Ludwig Boltzmann, the physicist whose work formed the foundation of statistical mechanics.
And the connection between neural networks and physics isn’t a one-way street. Machine learning was crucial to the discovery of the Higgs boson, where it sorted the data generated by billions of proton collisions. This year’s Nobel Prize for Chemistry further underscored machine learning’s importance in research, as the award went to a trio of scientists who built an AI model to predict the structures of proteins.
Smyth saw Hopfield’s efforts first-hand as a student at the California Institute of Technology. “Hopfield was able to bring together mathematicians, engineers, computer scientists, and physicists. He got them in the same room, got them excited about modeling the brain, doing pattern recognition and machine learning, unified by mathematical theories he brought in from physics.”
In 2012, Hinton co-founded a company called DNNResearch with two of his students; Ilya Sutskever, who later co-founded OpenAI, and Alex Krizhevsky. Together, the trio collaborated on AlexNet, a hugely influential neural network for computer vision. Hinton also taught at the University of Toronto, where he continued to champion machine learning.
Navdeep Jaitly, now a deep learning researcher at Apple, said Hinton inspired new generations of engineers and researchers. In Jaitly’s case, the influence was direct; Jaitly studied under Hinton at the University of Toronto.
“I came in with experience in statistical modeling,” says Jaitly, “but Hinton still managed to entirely change how I think about problem solving. In terms of his contributions to machine learning, his methods are central to almost everything we do.”
Born 1933 in Chicago, IL, USA. PhD 1958 from Cornell University, Ithaca, NY, USA. Professor at Princeton University, NJ, USA.
Geoffrey E. Hinton University of Toronto, Canada
Born 1947 in London, UK. PhD 1978 from The University of Edinburgh, UK. Professor at University of Toronto, Canada.
was announced on 10/8/2024 in Stockholm, Sweden.
“for foundational discoveries and inventions that enable machine learning with artificial neural networks”
They trained artificial neural networks using physics
This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.
John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.
Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.
Simulation Training modules for interventional radiologists @MGB teaching hospital Recorded immersion views of CT, VR modules, gaming pilot holodeck simulation #WMIF2024
Precision Oncology Deep learning computational pathology for tumor biology microenvironment & DNA sequencing – AI to evaluation tumor microenvironment Immune vs tumor Phenotyping T-cell cytotoxicity #WMIF2024
Vulnerable Plaque in heart attack clots disfunctional endothelium, microphage at necrotic lipid core structure of the plaque micro structural imaging combined with molecular imaging – Fluorescent OCT #WMIF2024
Mean cost of a clinical trial (CL) in CVD +$157MM alternatives to randomized CL AI approaches ML for scalable Clinical event adjudication NLP trained on Heart Failure hospitalizations #WMIF2024
Neurodegeneration Lipid transfer and disregulation loss of homeostasis of lipid metabolism Apo E function in the Brain APOE3 isoform cell-baed asssays NPC1 inhibition enhance Lipid transport ApoE4 #WMIF2024
Autoimmune disregulation of the #Immune#system immune suppression (JAKi) local and site delivery #hydrogel monitoring immune remodeling of T-cells application to #alopecia expands IL-2 #WMIF2024
SImulation Training modules for interventional radiologists @MGB teaching hospital Recorded immersion views of CT, VR modules, gaming pilot holodeck simulation #WMIF2024
Precision Oncology Deep learning computational pathology for tumor biology microenvironment & DNA sequencing – AI to evaluation tumor microenvironment Immune vs tumor Phenotyping T-cell cytotoxicity #WMIF2024
Vulnerable Plaque in heart attack clots disfunctional endothelium, microphage at necrotic lipid core structure of the plaque micro structural imaging combined with molecular imaging – Fluorescent OCT #WMIF2024
Mean cost of a clinical trial (CL) in CVD +$157MM alternatives to randomized CL AI approaches ML for scalable Clinical event adjudication NLP trained on Heart Failure hospitalizations #WMIF2024
Neurodegeneration Lipid transfer and disregulation loss of homeostasis of lipid metabolism Apo E function in the Brain APOE3 isoform cell-baed asssays NPC1 inhibition enhance Lipid transport ApoE4 #WMIF2024
Autoimmune disregulation of the #Immune#system immune suppression (JAKi) local and site delivery #hydrogel monitoring immune remodeling of T-cells application to #alopecia expands IL-2 #WMIF2024
Data: curated, quality data to have predictive value, right End-points, AI enable indexing categorizing data democratize the data and will save time to answers #WMIF2024
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“This milestone makes AISAP the first company in the world to secure FDA clearance in the CADx pathway for the comprehensive diagnosis of structural heart diseases using POCUS,”
Cardio is a cloud-based platform that includes four modules for the computer-assist diagnosis (CADx) of valvular pathologies and eight key cardiac measurements. Its advanced AI algorithms can evaluate a patient’s left ventricle ejection fraction, right and left ventricular dimensions, right ventricular fractional area change, atrial areas, ascending aorta diameter and inferior vena cava diameter in addition to identifying aortic stenosis or mitral, tricuspid or aortic regurgitation.
The platform, trained on more than 24 million echocardiography clips, was designed to help even inexperienced users scan and diagnose a majority of common heart issues within minutes without leaving the patient’s side. In addition, it can communicate with equipment manufactured by a variety of vendors, directing data to a physician’s electronic health record or PACS system as needed.
Ehud Raanani, MD, co-founder of AISAP and director of the Leviev Cardiovascular and Thoracic Center at Sheba Medical Center, said in a statement. “It marks a big step in our goal of delivering point-of-care assisted diagnosis, or POCAD, with unparalleled scalability and accessibility—from the largest academic centers to the most remote rural locations.”
Smadar Kort, MD, system director of noninvasive cardiac imaging at Stony Brook Medicine, who has experience with the platform
said:
“We know that structural heart disease and heart failure are the leading causes of hospitalization and morbidity in the U.S. Enabling a wide variety of qualified physicians to quickly and accurately diagnose these conditions at the bedside could lead to earlier detection and treatment, and better patient outcomes, as well as greater efficiencies and cost savings to health systems, while ultimately saving countless lives.”
JACC editor ‘very important moment’ for Cardiology: New drugs for obesity and prevention, New tools for structural heart analysis for Heart Failure, AI harnessed for Cardiac patient monitoring
Reporter: Aviva Lev-Ari, PhD, RN
The new trends include:
(a) New cardiovascular drugs and prevention strategies
Reflecting on his experience at the recent ESC meeting, Krumholz noted a renewed enthusiasm within the field. After years of concern that progress in cardiovascular research was slowing, he described how new targets for treatment, such as lipoprotein a (LPa) and inflammation, are poised to transform care. He pointed to the upcoming ZEUS trial investigating the role of interleukin-6 (IL-6) inhibition with the drug ziltivekimab to treat coronary inflammation and to see if they can lower cardiovascular events. He also pointed to breakthrough research on anti-obesity medications that could revolutionize therapy for cardiometabolic health. These treatments, which impact conditions like hypertension and lipid management, represent a promising frontier.
“The anti-obesity medications and their effect on cardiometabolic health are creating an entire new frontier where we can make progress,” Krumholz explained. “The prevention area is also exploding with new ideas, new targets, new opportunities, with ways to treat people that may be intermittent, where they’re getting injections every six months or once a year, more like a vaccine approach.”
(b) Explosion in structural heart therapies and new approaches to heart failure
The growth in cardiovascular device innovation, particularly in structural cardiology, also caught Krumholz’s attention. Areas like mitral, tricuspid, and aortic valve interventions continue to see substantial advancements, raising important questions about the durability and timing of interventions for conditions such as aortic regurgitation. Meanwhile, heart failure treatment is experiencing a “revolution,” he added, with both devices and novel drug therapies dramatically improving outcomes. Krumholz specifically highlighted the positive evidence supporting tirzepatide (Mounjaro) in treating heart failure with preserved ejection fraction, a condition that has long lacked effective treatment options.
(c) AI will change how cardiac patients are monitored
In addition to breakthroughs in pharmacology, Krumholz highlighted how artificial intelligence (AI) and digital health technologies are reshaping cardiovascular care. AI is being leveraged to enhance patient monitoring, especially through wearables, and it is opening new opportunities for out-of-hospital care and real-time intervention. He said this isn having an especially large impact on electrophysiology, because patients can now more easily be monitored with wearable devices remotely and the AI can send an alert to the physician when there is a problem. Krumholz described this as the advent of “super medical intelligence,” which could redefine how clinicians diagnose and manage cardiovascular conditions.
As the editor of JACC, Krumholz said he is eager to facilitate the translation of these scientific advances into clinical practice more quickly, with the ultimate goal of reducing cardiovascular disease’s burden worldwide.
DASI Simulations, OH-based company gained FDA clearance for an artificial intelligence (AI) Product that identifies and measures cardiac structures in CT scans
Reporter: Aviva Lev-Ari, PhD, RN
DASI Simulations previously gained FDA approval for PrecisionTAVI, an advanced AI model capable of predicting certain patient outcomes before patients undergo TAVR.
DASI Simulations previously gained FDA approval for PrecisionTAVI, an advanced AI model capable of predicting certain patient outcomes before patients undergo TAVR.
“Our mission—to provide an AI-powered structural heart platform that allows physicians to be more efficient and use their expertise more effectively—is moving forward with great momentum,” Teri Sirset, founder and CEO of DASI Simulations, said in a statement.
The DASI Dimensions approval is just the latest example of cardiology’s role as a significant leader in the development and use of healthcare AI. Cardiology ranks No. 2 among all specialties when it comes to cleared AI algorithms, trailing only radiology.
The Landscape of other players in AI and Medical Imaging
Siemens Healthineers, Nanox.AI and AISAP have all gained key FDA clearances/approvals since Aug. 1, suggesting this trend is not slowing down anytime soon. The FDA even named a cardiologist—digital health specialist Ami B. Bhatt, MD—as the first chair of its new Digital Health Advisory Committee, highlighting the prominent roles cardiologists are having in AI-related conversations on a regular basis.
SOURCE
DASI’S simulations will help develop a new workflow in every hospital and become an integral tool for heart teams in the USA and potentially worldwide.
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This year’s Nobel Prize laureates in chemistry Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures.
In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.
Read more about their story: https://bit.ly/4diKiJ2