Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

Bacteria selected an enzyme that converts age-related protein damage back into lysine. On July 14, an article about CMLase, an enzyme that converts chemically modified lysine (CML) back into lysine, was published in Nature Communications. The authors made this reaction a condition for the growth of E. coli: cells with a working variant of the enzyme survived. Over five rounds of directed evolution, they tested more than 500 million variants. CML is a chemically modified lysine residue that accumulates on long-lived proteins. CMLase had already removed CML from model proteins and human lens, skin, and aorta samples outside the body. In the new article, the authors described how they found a variant of the enzyme that performs this task significantly better. The authors disabled lysine synthesis in E. coli: without this amino acid, the bacterium does not grow. Into the periplasm, the space between the two cell membranes, they directed a peptide of seven amino acids. One lysine in it was converted into CML. The entire peptide did not pass into the cytoplasm, where the bacterium receives nutrition. In the periplasm, variants of CMLase and trypsin, an enzyme that cleaves the protein chain after lysine, worked. Working CMLase converted CML back into lysine, trypsin cut the peptide, and short fragments entered the cell. The bacterium received lysine and formed a colony. The growth of the colony became a test of the entire biochemical chain. The starting protein the team searched for among 44,783 models of oxidases from AlphaFoldDB. They selected the form of the active center that accommodated the peptide with CML and found glycine oxidase CrGO from the thermophilic bacterium Calidithermus roseus. Then, random changes in this protein were selected by the cells themselves. After five rounds, CrGO-897 acquired 15 amino acid substitutions and a deletion of two amino acids. On peptide substrates, its catalytic efficiency increased more than tenfold compared to the original CrGO. When CML repair opened up access to lysine for the bacteria, the colonies themselves separated the working variants of the enzyme from hundreds of millions of others. 🔗 Read original →

Precigenetics demonstrated how to repeatedly measure a single living cell after exposure to a drug. On July 21, Precigenetics presented a technical demonstration of Cell Cinema, a system that repeatedly measures a single living cell after exposure to a drug and converts the sequence of observations into a series of data for models. Many cellular experiments provide an endpoint: the cell is fixed or destroyed, and the state of the population is measured after exposure. Such a snapshot can show differences between cells, but not the sequence of changes of a specific cell. Cell Cinema leaves the cell alive and repeatedly takes its three-dimensional image without staining markers; each pixel of such an image contains a wide spectrum of light. The program converts the measurement into a set of numbers describing the state of the cell. From a sequence of such sets, a history of changes of the same cell before, during, and after exposure is obtained. In the first public demonstration, the company showed SK-MEL-2 melanoma cells under the action of RSL3, a drug that inhibits the GPX4 protein and triggers ferroptosis - cell death due to iron-dependent lipid peroxidation. In a second experiment, vemurafenib changed the chemical state of A375 cells with a BRAF mutation. Processed images and constructed histories of cell changes are published on the page. Instead of a snapshot of a cell population, a history of one cell appears. After exposure to the drug, the system records a sequence of its measured states. According to the company's plan, such records will help search for early signs of toxicity, compare doses, and choose the next experiment based on the cell's response already seen. In the spring, MaxToki reconstructed the age trajectory from single-cell data of approximately 175 million cells. Cell Cinema provides different material for similar tasks: repeated observations of a single cell instead of comparing different cells from different sampling points. The company proposes testing these records for predictability: the early part of the cell's history should predict its later state in cells that were not used for training. Two more declared tests are to preserve the description of the state between runs and transfer it to new conditions. Thus, Cell Cinema compares its series of measurements with a single snapshot and cell analysis after the experiment. Aging damage and responses to therapy also unfold over time. Precigenetics plans to use such records to have models choose the next experiment based on the early response of a living cell. For intervention research, the question becomes practical: what early turn in the cell's history is worth checking with the next drug? 🔗 Read original →

Anthropic opened a call for AI research on rare genetic diseases on July 20. Accepted teams will receive up to $50,000 in Claude credits for six months; there are academic and biotech company tracks, the latter developing therapy. Rare diseases leave data in different places: with families - descriptions of symptoms, with laboratories - gene variants, with registries - patient histories, and with articles - descriptions of biological mechanisms. One disease can be called by different names, and one symptom can arise from different cellular disruptions. Therefore, a researcher must first bring these records to a common language and then search for connections between them. In the academic track, Anthropic offers to work with the Monarch Initiative, a project that collects data on diseases, genes, DNA variants, and disease signs. Its Mondo dictionary matches different names for the same diagnosis. The DisMech database links symptoms, genetic factors, disease mechanisms, and treatments with precise citations from scientific annotations. A participant can ask Claude to find diseases with a common gene or biological pathway. Then, an expert verifies the proposed connection and evidence in DisMech. The model suggests a hypothesis, and the expert checks which publications it is based on. Anthropic also offers to compare a patient's symptoms with a diagnosis and search for a possible mechanism in a genetic variant whose effect on the body is not yet clear. The first track returns the result to the common database, rather than leaving it in the correspondence of one team. The company plans to publish the results of academic projects on the Monarch website. The next researcher will be able to see the connection, its sources, and verification, rather than starting the search from scratch. The second track is intended for early therapy development. Anthropic lists among the tasks the calculation of the starting dose based on models of how the drug moves through the body, the search for measurable biomarkers in registries and case descriptions, and the preparation of parts of the dossier for the regulator. Here, the model prepares material for the researcher, clinician, or regulator to decide on. The grant combines access to the model with data, rules for matching them, and verification of each hypothesis. This is exactly the sequence needed where information about a rare disease is scattered between families, laboratories, registries, and articles. 🔗 Read original →

Ed Boyden has proposed an institute where late success funds new risky research. In a July 23rd posted talk, "Incentives in Science", neurobiologist Ed Boyden described an independent organization for long fundamental projects. It supports early ideas, helps develop lucky tools, and receives from successful participants money, time, or mentorship for the next wave. On July 23rd, Boyden posted a talk with a proposal to gather in one organization the path from a risky idea to its application. His laboratory has spent twenty years creating ways to map the brain and control the activity of nerve cells. Such tools require a long time to work before they become of interest to grantmakers, investors, or users. Boyden suggests starting with graduate students and authors of early prototypes who take on fundamental tasks. A participant makes a commitment from the very beginning: if their work leads them to success, they will pass on to the next wave a part of their future capital, time, or mentorship. This promise does not depend on how exactly the path of a specific project unfolds. At the next stage, a successful tool receives application and scale: it can be developed by a startup or a targeted research organization created for one big task. Then, those whose work has reached late success fulfill their promise to new authors of early projects. The money and experience are returned not to one project, but to the next circle of attempts. Boyden relies on the trajectory of two methods from his laboratory: optogenetics, which allows controlling nerve cells with light, and expansion microscopy, which physically expands a sample in a polymer network so that an ordinary light microscope can see fine structures. A tool created for one scientific task can outlive it and become the basis for another. According to this scheme, it is precisely such late success that pays for the next early attempt. 🔗 Read original →

CuspAI has raised $450 million and is launching a network of 45+ partners to search for materials. On July 20, Cambridge-based CuspAI announced a $450 million Series B round at a $2.6 billion valuation and launched the AI Materials Foundry. The network includes over 45 companies and research organizations, among them NVIDIA, Meta, Samsung, Hyundai, Applied Materials, and Lam Research. For a chip or battery, it's not enough to find a substance with the desired property; it also needs to be obtained in a laboratory, have its properties measured, and be manufactured on a manufacturer's equipment from available raw materials. At each such step, the chemical formula is transformed into a specific material or discarded. CuspAI calls the AI Materials Foundry a network of data, laboratories, computational resources, and scientific expertise. The company wants to connect agent-based AI with industry data and customer tasks. The idea of the network is to search for materials immediately, taking into account where and how they will be produced. In such work, production data and customer requirements are important alongside calculations: they determine which material is generally suitable. CuspAI is gathering organizations with computational resources, scientific expertise, and manufacturer tasks in the Foundry. The round was led by Kleiner Perkins and NEA, with participation from Bezos Expeditions. As The Guardian writes, CuspAI is linking the network's work to semiconductors, energy grids, and batteries. The company plans to expand its work in the US, the Asia-Pacific region, and Europe. The $450 million gives CuspAI the opportunity to gather material searches around laboratories and production tasks, rather than around a single calculation. Now, the value of the Foundry will be determined by whether its participants can take a candidate from calculations to a laboratory sample and the requirements of specific production. 🔗 Read original →

On July 16, ProofAtlas published verifiable code in Lean, evaluating the time it takes for almost all Collatz trajectories to drop. On the same day, ProofAtlas released two formal declarations and tied them to a specific version of the source code. One of them asserts: for almost all positive starting numbers, the Collatz trajectory drops below a growing threshold in no more than 436 * log N ordinary steps. The Collatz problem sets a simple rule for an integer N: an odd number is replaced by 3N + 1, an even number is divided by two, and the process is repeated. The resulting sequence sometimes grows for a long time; therefore, it is difficult to estimate when it will first drop noticeably below the starting point. "Almost all" has a precise meaning here. Among numbers from 1 to X, the proportion of starts with such a drop tends to 100% as X grows. The threshold also grows with the starting point: for example, you can take √N. Then, for almost all N, the trajectory will drop below √N in 436 * log N steps. This constant counts each move, including divisions by two. In the same file, there is an estimate of 145 * log N for the Syracuse iteration. It immediately divides the result by all powers of two after the 3N + 1 operation until the next odd number. 436 and 145 cannot be compared directly: these are different ways of counting steps and different sets of starting points. Terence Tao proved a result of the same type for logarithmic density in 2019: the trajectory of almost every starting point reaches any growing threshold. ProofAtlas recorded a variant for ordinary natural density and ordinary Collatz steps in code that can be opened by a fixed version. The formal record gives the dispute a precise subject. Instead of retelling the result, one can check the formulation of the theorem, the condition on the threshold, and the way the steps are counted. 🔗 Read original →

NVIDIA has released the weights of JEPA-DNA, a DNA model that learns to predict the representation of an entire sequence. On July 16, NVIDIA published the weights, code, and reproduction parameters of JEPA-DNA. The method adds a task to the usual training of genomic models: to restore the internal numerical description of the entire sequence from a visible fragment. A genomic model reads DNA as a string of four letters. Usually, it restores closed letters or predicts the next one. However, the work of a segment is often determined by a combination of elements on a long sequence: a promoter informs the cell where to start reading a gene, and a splicing site helps assemble RNA. In the JEPA-DNA preprint, first published on February 19, the authors left the task of guessing nucleotides and added a second one. One encoder receives a sequence with closed fragments; another sees the complete sequence and creates its internal numerical description. The predictor learns to obtain this description from the available environment. According to the authors' idea, this goal should help the model consider the function of the segment together with the data on neighboring letters. The verification covered three original models: DNABERT-2, Nucleotide Transformer v3, and HyenaDNA. Nine tasks measured the quality of features after training a simple classifier, and eight more checked DNA variants for similarity of representations of the original and modified sequences. For HyenaDNA, the AUROC in the promoter recognition task increased from 0.686 to 0.763. In the transcription factor binding task, the same metric decreased from 0.698 to 0.638. The same backbone gives different results on different tasks: the usefulness of the JEPA goal needs to be checked in a specific setting, rather than inferred from the average increase. NVIDIA has released the weights of HyenaDNA, and the repository contains instructions for three sets of weights and launches on GFMBench. This set of tasks tests DNA models on promoters, splicing, and the consequences of genetic variants. The weights are available under a non-commercial license. A laboratory can compare the original HyenaDNA and the JEPA-DNA version on its own task with DNA sequences. The open weights and reproduction parameters allow for testing the method beyond the tables of the preprint. 🔗 Read original →

Canadian surgeons transplanted islet cells from a pancreas that had been perfused with blood for 124 minutes after circulatory death. On July 24, a Canadian team described a clinical case: after confirming circulatory death of the donor, they restored blood flow to the abdominal cavity, isolated 400,000 islet equivalents from the pancreas, and infused them into a 69-year-old patient with type 1 diabetes. Pancreatic islets produce insulin and are isolated from donor organs and transplanted into people with type 1 diabetes who have lost the ability to sense dangerous drops in glucose. After circulatory death, tissue rapidly depletes its energy stores, and the islets must then survive enzymatic and mechanical processing in the laboratory. In the July 24 article, Vancouver doctors described how, after a mandatory five-minute wait, they connected the donor's aorta and inferior vena cava to a normothermic regional perfusion system, which pumped oxygenated blood through the abdominal organs at body temperature for 124 minutes. The aorta leading to the brain was clamped by surgeons, allowing blood to return to the tissues before the pancreas was cooled. The donor's lactate level, which accumulates with oxygen deficiency, decreased, and glucose levels remained stable. The pancreas was then cooled and transferred to the laboratory. From the organ, 400,000 islet equivalents were obtained - a standard measure of islet tissue volume. The cells preserved 92% viability, which was sufficient for a single infusion. The donor had a low body mass index, which usually reduces the yield of islet tissue. Four weeks after transplantation, the patient reduced her daily insulin dose from approximately 21 to 6-7 units. The time spent with glucose in a safe range increased from 74% to 96%, and the time spent below 3.9 mmol/L decreased from 24% to 3%. C-peptide in her blood increased from 184 to 832 pmol/L, a marker that appears along with insulin produced by the patient's own tissue. Normothermic regional perfusion is already used for liver and kidney retrieval after circulatory death. In Vancouver, doctors applied it to the pancreas: restored oxygen to the organ before cooling, isolated islet cells, and transplanted them into the patient. 🔗 Read original →

In Alzheimer's disease, active and less active DNA segments are poorly separated in brain cells. In a Science article published on July 23, teams from Carnegie Mellon, the University of Pittsburgh, and the University of Washington measured gene activity and three-dimensional DNA contacts in the same brain cells. The Hicformer model showed that these contacts help predict disease-related changes in gene function. DNA in the nucleus is not just a long thread; segments with frequently read genes are usually located near other active segments, while less active segments are gathered separately. The fragments of DNA that end up next to each other determine whether a regulatory segment can influence gene function. The authors applied GAGE-seq to single cells from the post-mortem prefrontal cortex of people with Alzheimer's disease and those without the diagnosis. The method simultaneously reads gene activity and contacts between distant chromatin segments - the substance that chromosomes are made of. Then, a spatial map of the tissue showed where in the cortex cells with different gene functions and DNA layouts were located. In people with Alzheimer's disease, large active and less active zones of the genome were poorly separated from each other. The authors call this a mixing of compartments. In several cell types, they also found fewer close DNA contacts and more distant ones. The connections between genes and neighboring regulatory segments weakened; at the same time, in neurons, the function of genes associated with synapses decreased, and in microglia - the immune cells of the brain - stress, metabolism, and cellular aging programs changed. Hicformer combines DNA sequence, its overall layout, and local contacts to predict gene activity in different cell types. According to the authors, without knowledge of three-dimensional contacts, the model was worse at finding disease-related changes in gene activity. That is, the DNA contact map adds another way to search for cells and regulatory segments for future experiments to the list of included and excluded genes. Amyloid plaques and tau tangles remain noticeable signs of the disease. This work shows another level of its picture: in cells, not only the set of working genes changes, but also the arrangement of DNA segments that can control their function. 🔗 Read original →

After 30 minutes at -6 °C, 24 out of 30 newborn mice survived. On July 20, a study on supercooling was published in Scientific Reports: water in the body remains liquid at a temperature below zero. After half an hour at -6 °C, 24 out of 30 mice survived; after the same amount of time at 0-4 °C, none of the 28 mice survived. The authors of the article worked with mice aged one to five days. Each weighed about a gram and quickly lost heat. They compared 30 minutes of normal cooling at 0-4 °C with 30 minutes of supercooling at -6 °C. After normal cooling, all 28 mice died, while after supercooling, 24 out of 30 survived the first day. During supercooling, water remains liquid below the freezing point. Ice damages cells with growing crystals, so the researchers kept the mice dry in open plastic bags and cooled them with air: this way, there are fewer places where crystals can start to grow. Then, the animals were gradually warmed up in an incubator, oxygen was supplied, and breathing was manually stimulated. According to the authors' hypothesis, at -6 °C, stronger suppression of metabolism helps cells survive storage. In 2019, the same approach helped preserve human livers at -4 °C: after machine perfusion, they remained viable outside the body for 27 hours longer. Two days earlier, pig kidneys after three days at -4 °C started working again after transplantation. The liver and kidney are stored separately from the body; here, the researchers tested a short pause below zero in a whole mammal. The surviving mice were observed for up to three months. Until a month old, they gained weight at the same rate as the control animals. Then, the authors checked coordination, running endurance, sensitivity, gait, and learning of a fear response; the article and its abstract report that no differences with the control were found in these tests. In one experiment with a whole organism, the researchers checked whether it is possible to keep the body below zero without ice, warm it up, and monitor the return of breathing, movement, and development. For biostasis, this is the next step after preserving individual organs. 🔗 Read original →

On July 21, Brian Johnson announced that induced pluripotent stem cells (iPSCs) had been derived from the cells of his blood. He referred to the culture as a "newborn clone" and linked it to future body repair: from testing therapies to growing tissues and organs. In the post, Johnson writes: "I just cloned myself... newborn." This refers to a cell line in a Petri dish: his blood cells had been reprogrammed into iPSCs. Since different cell types can be obtained from iPSCs in the laboratory, Johnson associates his line with testing therapies, growing tissues and organs for transplantation, and introducing "young cells." In a 2007 study, a group led by Shinya Yamanaka obtained such cells from adult fibroblasts - cells of connective tissue. The scientists applied four transcription factors, proteins that change gene function. After reprogramming, the cells acquired properties that allowed them to be used to obtain tissues of the organism in experiments. To obtain a specific tissue, the cells are directed towards the necessary specialization, the tissue is grown, and it is checked whether it works in the body. One original line can serve both as a disease model and as material for cell therapy: in these cases, it is given different tasks. This chain has already reached early clinical trials for nerve tissue: in a phase 1 study, four people with cervical spine injuries were administered two million neural precursors grown from iPSCs. The safety of the transplant was being tested. Johnson suggests considering this cell line as a personal reserve for body repair. The material was taken from himself, and he describes the replacement according to functions: vision, movement, blood formation, and organ function. According to his model, the body can be repaired consecutively, using one's own cellular material for each new task. 🔗 Read original →

The NIH has launched the Bio Genesis Mission, a program that connects AI, medical data, and clinical research. On July 22, the US National Institutes of Health announced the Bio Genesis Mission, its biomedical contribution to the federal Genesis Mission. The NIH aims to cut in half the time it takes to go from a scientific discovery to its impact on health over the next five to ten years, with the mission already accounting for more than $1.2 billion in commitments for the 2026 fiscal year and planned funding for 2027. To bring new therapies to patients, researchers need to match molecular and genetic information, data on observed signs of disease and patient health, select a candidate, and test it in a clinical trial. The Bio Genesis Mission is intended to bring this path - from data to the clinic - within a single program. The overall Genesis Mission has already selected 278 projects for grant negotiations, in which computations generate hypotheses and laboratories test them on samples; Bio Genesis adds clinical research to this chain, where changes in human health can be seen. The NIH describes six areas of focus for the mission: predicting the behavior of living systems, biomanufacturing, early detection of biological threats, childhood cancer, the development and clinical application of drugs, and the search for the causes of chronic diseases. These tasks define what the NIH plans to combine data, computations, and clinical infrastructure for. For drugs, the plan is described in more detail. According to a White House statement, the Department of Health and Human Services, the Department of Energy, and the Department of Defense will create an infrastructure that combines molecular, genetic, clinical data, and information from everyday medical practice. On this basis, AI should search for new applications of already known drugs and help bring new therapies to the clinic. NIH Director Jay Bhattacharya explained the idea through a patient: "People with cancer, chronic, or rare diseases feel the timeline of research as years of waiting for an answer." The entire Genesis Mission spans more than 15 federal agencies and relies on a common platform with data, computations, and AI tools. In the NIH director's statement, Bio Genesis links shared resources to the search for the causes of chronic diseases, the development of drugs, and their testing in humans. 🔗 Read original →

Sam Altman believes that the singularity has already begun and described a chain where robots build new data centers. On July 25, in an interview with Ti Mors, OpenAI CEO Sam Altman called the current moment the singularity. He suggested imagining automated production, where a data center controls robots, and the robots build the next center. Altman calls the singularity an already ongoing "crazy exponent": there is no single breakthrough moment, but the current period, he says, determines where the curve will go. Ten years ago, this perspective seemed distant and unlikely to him; now he says that humanity is already inside the process. The next stage, he associates with the physical infrastructure of computing. In a fully automated chain, a data center spends part of its power to control robots. The robots build another center, and its power returns to the same cycle. Altman suggests looking at this possibility as follows: "Too much attention is paid to algorithms that improve algorithms, and too little to data centers capable of creating new data centers." This scheme runs into electricity, microchips, and construction. The International Energy Agency compares the consumption of a typical AI data center to the consumption of 100,000 households. In its basic scenario, global data center consumption will grow from 415 TWh in 2024 to approximately 945 TWh by 2030. According to Altman's model, computations become part of the production of their own material base. In the same conversation, he named transistors and then electricity as the main limitations: they determine whether a new data center can launch another round of construction. 🔗 Read original →

1000ExM increases the bio-sample by a thousand times; calculation shows differentiation of neighboring amino acids. On July 21, Helena Hu, Ed Boyden, and co-authors published 1000ExM on bioRxiv - a method that increases the fixed biological sample approximately a thousand times in length. The authors checked the preservation of structure on several proteins and a peptide, and then modeled protein recognition according to such a spatial map. Light microscopy merges close points into one spot. Neighboring amino acids in a protein chain are separated by about 0.38 nanometers; conventional optics cannot see such a distance. 1000ExM expands molecules to a scale visible to conventional microscopes. Researchers chemically attach side chains of amino acids to a swelling gel, cut the protein chain between the attachment points, and add water. The gel expands the attached fragments. Four polymer networks repeat this expansion: the sample grows approximately a thousand times along each axis, and its volume - approximately a billion times. The distance of 0.38 nanometers is transformed into approximately 380 nanometers - which can already be distinguished by a confocal microscope. Early variants of expansion microscopy provided a total linear magnification of about 16-22 times. After each round, they added a neutral gel to hold the stretched sample. In Hu's and colleagues' work, the next charged network is formed directly inside the already swollen gel. This allowed the expansion to be repeated four times. In the Eon experiment, neuron microcultures were expanded approximately 20 times to match their wiring with recorded activity. 1000ExM continues the same trend at the level of protein labels: after expansion, their geometry in the model becomes distinguishable by conventional optics. The authors compared the resulting maps with the known structure of nanotubes, the mCLING peptide, and GFP - the green fluorescent protein. Thus, they checked whether the labels preserve the geometry of the original molecule. Then, in a calculation for 23,391 canonical human proteins, they took into account missed labels and measured gel deformation; for most proteins, the label pattern turned out to be unique. In the calculation, the label pattern becomes a signature of the protein. Fragments with attached side chains leave spatial points; their arrangement can distinguish proteins. The gel increased the original distances to the scale of conventional confocal optics. On individual molecules, the authors checked the preservation of label geometry. According to their model, such signatures can be obtained inside cells and tissues. 🔗 Read original →

Infinita has launched a council in Montana that accepts therapies after the first phase of clinical trials. The Montana ETRB will review access to experimental therapies before the usual FDA approval. A developer can submit an application for $12,500 after the first phase - the first human trial, where safety is primarily checked. On July 25, Infinita announced the launch of the Montana Experimental Treatment Review Board, or ETRB. In the announcement, the company stated its task directly: "Infinita has formed the first ETRB under Montana law. For $12,500, you can submit a therapy to our board after the first phase of clinical trials." A public entry point for early access to therapy has appeared: the board's website already leads to a submission form. The board checks if the safety of the intervention is sufficiently described, how the patient will be monitored, and how informed consent is formalized. Among the listed participants are aging biologists Matt Kaeberlein and Felipe Sierra, bioethicist Jessica Flanigan, Jamie Justice from XPRIZE, and oncologist James Burke. Montana's SB 535 law provides this route. It allows consideration of drugs, biological products, devices, and other interventions after a successfully completed first phase, if they are still being studied in an FDA-approved study or have a documented safety history. The patient discusses available approved options with their treating physician, receives their recommendation, and signs consent. The law divides the work between the board, clinic, and state. According to the Montana Department of Health's draft rules, the board reviews the protocol, safety data, monitoring plan, and patient consent. A licensed experimental center performs the treatment and reports serious adverse events to the department within five days. In June, Niklas Angering described the Próspera and Montana SB 535 connection as a future route: first, early safety testing in humans, then access through an American state. Now, this scheme has a board with a submission form and named participants. The developer collects a dossier on the therapy, the board reviews it, the clinic treats the patient, and the state receives safety reports. 🔗 Read original →

The Biomarkers of Aging Consortium will devote an entire session to replacement in aging, with the Replacement in Aging program scheduled for October 5 in Boston. The program will discuss cellular and tissue replacement, organ engineering, transplantation, immune system renewal, and neuroregeneration. On the session page, the organizers ask: can an aging organism be maintained by replacing failing parts with young, grown, or synthetic components? The discussion will take place throughout the day, from 9:00 to 18:00. The program features Vadim Gladyshev, Anthony Atala, Linda Griffith, and Hugh Herr. Transplantation, tissue engineering, immune system renewal, and neuroregeneration work with different parts of the aging organism. The session brings these areas together around one question: what part of the body can be replaced to restore the necessary function? In a separate operation, the focus is usually on whether a specific organ or tissue is working. An anti-aging strategy requires a longer check: what happens to cells, tissue, organs, and people over time. In their May roadmap on replacement-based interventions, Gladyshev, Atala, and colleagues propose evaluating replacement at each of these levels. The perspective in Aging Cell, on which this roadmap is based, distinguishes between the short-term outcome of a procedure and long-term functional restoration. Replacement can be evaluated as a strategy when measurements show: has the function returned and is it being maintained? Therefore, the biomarkers conference places cell, tissue, organ, and function replacement alongside aging measurement and intervention testing. 🔗 Read original →

Insilico Medicine selected the AI-created molecule ISM9528 for pain treatment on July 22. Insilico Medicine named ISM9528 a preclinical candidate for pain treatment. The company plans to start the first human trial in 2027; the molecule is intended to be taken orally. In the Insilico release, the drug target is designated as Target Z. This is a provisional name for the biological target that the molecule is supposed to act on. The PandaOmics platform matched gene and protein function data, scientific publications, and other biological information, and then highlighted Target Z as a potential target for pain. After that, the team checked how this target is represented in pain-related cell types in humans and animals. Then, Chemistry42 proposed options for the chemical structure. The team selected molecules based on their effect on Target Z, ability to penetrate the blood-brain barrier, and properties needed for a future drug. The blood-brain barrier is a filter in brain vessels that only allows certain substances to pass through. After several cycles of selection, Insilico chose the molecule ISM9528. In animal studies, the company compared ISM9528 to pregabalin, a drug for neuropathic pain. In a nerve damage model in rats, the average dose of ISM9528, according to Insilico, relieved pain for up to six hours, just like pregabalin. In a postoperative pain model, the company reports that the effect began within half an hour and was stronger than that of an equal dose of pregabalin. Drug Target Review recounts these models and the plan for the first human trial in 2027. Insilico already has a program that has progressed further: rontosertib, for which AI chose the target TNIK and designed the molecule, showed results in Phase IIa for pulmonary fibrosis. ISM9528 is currently at the very first stage of this process. ISM9528 became Insilico's 31st preclinical candidate since 2021. The company reports that 13 previous candidates have received permission to start clinical trials. 🔗 Read original →

The Pan-human Azimuth project has unified data from 23 tissues into a single hierarchy of cell types. On July 21, the HuBMAP team released the Pan-human Azimuth preprint. The neural network annotates individual human cell profiles according to a unified hierarchy; to train it, the authors collected 27.04 million profiles and launched a cloud service, as well as tools for R and Python. Single-cell atlases show which genes are active in each cell, and based on this, a biologist determines the cell's type and state. However, one atlas may call a cell by one name, and another by a different name. When comparing organs, the difference in names can be easily mistaken for a difference in biology. In the preprint by Surava Sarkar and colleagues, the original names from different datasets were manually matched to a single tree of cell types. The authors checked questionable assignments based on gene activity and trained a classifier on this annotation. It reads a table of RNA activity, assigns a cell to one of the eight levels of the hierarchy, and shows the confidence of the response. Empty droplets and background RNA received a separate class. Now, similar cells from different organs receive names according to the same rules. The authors tested the classifier on 1.1 million profiles from Tabula Sapiens v2: the data came from 24 donors and covered 28 tissues; about 600,000 profiles from nine donors the model saw for the first time after training. In the same way, it annotated 85.9 million cells from scBaseCamp. On spatial kidney slices, the classifier reproduced the pattern of cortical matter and separated healthy and sclerotic glomeruli in agreement with the annotation of pathologists. The unified hierarchy allows querying data about a specific cellular state immediately in several tissues and in different people. For example, it is possible to check if the same state of fibroblasts, immune cells, or epithelium is repeated in different organs. The atlas of 7 million mouse cells has already shown synchronized age shifts between organs; Pan-human Azimuth gives human data a common language for such verification. The project documentation calls this approach comparing a new sample to a collected reference book. Pan-human Azimuth turns the chaos of cell names into a reproducible procedure. After such annotation, inter-tissue comparison can be checked on tens of millions of profiles. 🔗 Read original →

The Verge: Genesis Mission has selected 278 AI projects, and laboratories and universities are to verify their hypotheses. On July 24, The Verge published an analysis of the first projects of the American Genesis Mission - a government AI program for science. Robert Hart links it to the new White House plan and asks if science has enough people and places to test machine proposals. On July 22, the US Department of Energy selected 278 Genesis Mission projects for grant negotiations. Teams from national laboratories, universities, companies, and non-profit organizations will gain access to computational power, models, and programs for research. The White House plan "Science: A New Golden Age," published on July 21, suggests relying on individual researchers, new organizations, and partnerships with companies. The grant provides the team with calculations and models; hypothesis verification remains the work of researchers and laboratories. AI can propose a molecule, material, connection in data, or experiment scheme. A researcher sets up a control experiment, a laboratory obtains a measurement, and another group repeats it on new samples. In The Verge's analysis, physicist Andreas Karth describes the risk: AI will produce many plausible options, and there will be fewer people with experience to filter them. "AI may have several big ideas buried under mountains of useless material, and there will be far fewer people with experience to distinguish one from another." In biomedicine, after laboratory experiments come clinical trials: they determine whether a person's condition changes. Each transition from model to experiment, repetition, and clinic filters out some convincingly sounding hypotheses. The debate about Genesis Mission concerns this entire chain: calculations accelerate the search for options, and universities and laboratories teach people to set up experiments, verify results, and prepare the next generation of researchers. 🔗 Read original →

Asimov is creating a system at Boston University to test whether a therapeutic protein can be manufactured. On July 24, Asimov announced that it will design variants of therapeutic proteins on a computer, produce them in the DAMP laboratory at Boston University, and measure the properties of the finished product. These results will be fed back into the model, which will select the next sequence. A therapeutic protein starts with a sequence of amino acids, but that's not the end of the work. It still needs to be produced in cells, purified, and tested. Sometimes, it's at this stage that it becomes clear that a design that was successful on the computer is difficult to turn into a product. In the July 24 announcement, Asimov describes the sequence of actions: the company designs protein variants, the laboratory produces them and measures the manufacturing-related properties, and the model receives the results. The result of each experiment will become data for selecting the next batch of variants. The manufacturability of the protein will thus be taken into account in the decision on its sequence even before the next cycle of experiments. DAMP is the design, automation, manufacturing, and processes laboratory at Boston University. It already performs remotely ordered experiments with biological materials, chemicals, and liquids. Asimov will create a section there for working with therapeutic proteins and will become one of the laboratory's first users. The BU laboratory is part of the Programmable Cloud Laboratory Test Bed network. On July 22, the US National Science Foundation allocated $380 million over four years to the network for 20 laboratory nodes; the Astera Institute adds up to $20 million. BU will receive up to $20 million for its node. The network is expected to allow laboratories to use common methods and experiment results. Asimov is linking protein design to what happens during its actual production. A protein that can be produced and measured provides the model with an example for the next selection; a protein with poor manufacturing properties helps to weed out similar sequences earlier. 🔗 Read original →