“The discovery of Yamanaka factors revolutionized biology, enabling us to grow human tissue and bringing us closer to the development of personalized regenerative medicine.

Thanks to this, we now have a technology that allows us to reprogram aging cells, "refreshing" their function and returning them to a more youthful state. There is reason to believe that this partial cellular rejuvenation can slow down the aging of the entire organism—this hypothesis has been confirmed in mice.

Unfortunately, like any effective biotechnology, it carries certain risks. For example, c-Myc, one of the transcription factors in the "Yamanaka cocktail," is an oncogene that signals cells to divide, which risks becoming uncontrolled. At the same time, without it, reprogramming with current approaches is slow and ineffective.

Nevertheless, this in no way diminishes the potential of Yamanaka factors; rather, it motivates us to seek ways to make the technology as safe as possible while maintaining its effectiveness. We are trying to eliminate individual transcription factors, modify existing ones, and identify entirely new ones. We are also testing factor enhancement options that allow us to reduce their dosage.

You can read more about this topic in a recent TechInsider article, based on expert commentary from Roman Litvinov. I highly recommend reading this highly relevant material.” - V.Kovalev

https://www.techinsider.ru/science/1738137-molekuly-vechnoi-molodosti-mojno-li-zastavit-vzrosluyu-kletku-snova-stat-rebenkom/

https://biotic.org/research/spudcell/

A synthetic cell created virtually from scratch

SpudCell (as it's called) is capable of performing three key functions characteristic of living cells: absorbing nutrients, growing, and dividing. After each feeding, it can reproduce for approximately five generations in a row. One division takes about 12 hours at a temperature of 30°C. For comparison, the common bacterium Escherichia coli divides approximately every 30 minutes.

The structure of the artificial cell is significantly simpler than its natural counterparts. It consists of only 150-200 different molecules, while real cells contain millions or even billions of molecules. Its genome is also significantly smaller: approximately 90,000 base pairs versus approximately 4.6 million for E. coli.

Despite its superficial resemblance to bacteria, SpudCell functions differently from natural cells. For example, it lacks a cytoskeleton—an internal system of protein structures that helps cells maintain their shape and divide. Instead, division occurs through the accumulation of proteins near the cell membrane, which mechanically force it to split into two parts.

SpudCell is not yet capable of independently producing ribosomes—the molecular complexes responsible for protein synthesis. Therefore, with each feeding, it must be supplemented with ready-made ribosomes obtained from E. coli bacteria. Without these, the artificial cell will not be able to continue to exist and reproduce.

The researchers also demonstrated that the artificial cells can be subject to natural selection. When a change was introduced into the genome that increased the production of one of the growth proteins, these cells began to grow and divide faster than the rest. However, this is not yet considered full-fledged evolution, as the change was human-made and did not arise by chance.

According to the authors, the current version of SpudCell is practically useless from a practical standpoint. It does not produce useful substances, does not perform specialized functions, and cannot exist independently outside of a laboratory setting. However, the researchers view it as a basic platform that can be programmed in the future to solve various problems.

Neuralink successfully performed the first implant surgery using a new method.

Whereas previously, surgeons would cut and partially remove the dura mater covering the brain, electrodes are now inserted directly through it, without disrupting its integrity. This should make the surgery less traumatic, safer, and easier to implement on a large scale.

The dura mater is a strong, protective membrane beneath the skull. It is more than 10 times thicker than Neuralink's ultra-thin electrodes, which are thinner than a human hair. To learn how to pierce this membrane without damaging the brain, engineers developed a new needle for a surgical robot.

The main challenge is that the brain constantly pulsates and shifts slightly, and a dense network of blood vessels runs beneath the dura mater. Since the dura mater itself obscures the view, there is a risk of accidentally damaging a vessel during electrode insertion.

To address this issue, Neuralink created artificial dura mater models on which to repeatedly test the new technology. In addition, the company has implemented two imaging systems. The first uses the fluorescent dye indocyanine green (ICG), which allows for real-time visualization of blood vessel locations. The second is based on optical coherence tomography (OCT) and measures the distance to the brain's surface, accounting for its constant movement during a heartbeat.

Stopping the removal of the dura mater eliminates one of the most complex steps of the surgery. This should make the procedure more standardized, safer, and more suitable for automation by the Neuralink robotic system.

The first such operation was performed in May 2026 as part of a clinical trial. Within an hour after the surgery, the patient was able to control a computer cursor with his mind, and his recovery is proceeding normally.

The primary goal of this development is not to increase the speed of the implant itself, but to simplify the installation procedure. Neuralink believes that surgery remains the main obstacle to the widespread adoption of neural interfaces. If implantation can be made simpler, faster, and safer, such systems will be easier to use in a larger number of patients.

AI in drug discovery will accelerate errors if cellular and animal models poorly predict human research, warns Jack Scannell.

Decoding Bio published a conversation with Jack Scannell, the author of Eroom's Law: a pharmaceutical version of Moore's Law, where each period yields fewer new drugs per dollar of research. His main thesis: AI accelerates drug discovery when the initial biological models are related to the human disease. With a weak model, the machine generates convincing answers more quickly from poor assumptions.

In 2012, Scannell and his colleagues described Eroom's Law: since 1950, pharma has produced fewer new drugs per billion dollars of research, even though DNA sequencing, structural biology, computation, and screening have become more powerful. In a new interview with Decoding Bio on July 6, he applies this diagnosis to the current wave of AI in biotech.

Scannell distinguishes between two things that are easily confused. Throughput is how many molecules, targets, and hypotheses a system can process. Predictive validity is the degree to which a cell line, mouse model, organoid, blood test, or computer simulation predicts what will happen in humans.

AI dramatically increases the first dimension: it sorts through molecules, builds protein models, searches for relationships in tables, and helps the lab move faster. A weak disease model remains weak. If a cell test is easy to automate but poorly correlates with the real disease, AI will massively multiply this error.

Feeding AI data from poor biological models simply increases the number of incorrect answers it can generate per second.

According to Scannell, about 90% of drug projects that look good in mice and cell lines then fail in humans. He considers this gap between the model and the human subject one of the main sources of declining pharmaceutical yield.

Scannell proposes a different approach: first, you need biology that can relate to humans, then scale up. A good scenario is when a team builds a realistic test, takes human data, understands the model's weaknesses, and then uses AI to generate more statistics, variants, and solutions.

His work on predictive validity involves mathematics that doesn't mesh well with the industrial obsession with scale: a small improvement in the relationship between a model and a clinical outcome can be worth more than a huge increase in the number of tested candidates. In an interview, this is boiled down to a formula: a slightly more accurate model is much more valuable than a slightly less accurate one. Therefore, the race for the number of molecules can lose out to the tedious verification of what exactly the model measures.

For geroscience, this is a particularly painful filter. Aging and age-related diseases are difficult to model: a mouse has a short lifespan, a cellular senescence marker captures a single cell mode, an organoid represents a piece of tissue, a biomarker substitutes years of life with an indirect signal. AI can help if it brings these models closer to human reality. It corrupts thinking if it makes old proxy results faster, more beautiful, and cheaper.

Therefore, the main question for companies promising AI drug discovery is simpler than their pitches. What human outcome does your system predict? What data has it been tested on? Where has the model already failed? What has changed in laboratory biology besides computational speed?

By this criterion, the center of gravity lies where the molecule meets the patient or rigorous testing. Insilico is already evaluating 30 AI candidates and three programs in Phase II; some molecules are progressing through clinical registries, doses, safety, and endpoints. VeriSIM Life, together with the FDA's research center, is testing mechanistic AI for toxicity and dosing, where data, error margins, and applicability to a specific solution are crucial.

A weak answer betrays the same old pharmaceutical self-deception in a new package: more data, more candidates, more automation—and the same failure when it meets a human.

🤩 Is Brian Johnson's Stomach Eating Itself?
The renowned biohacker admitted to having an incurable disease, autoimmune gastritis (AIG), a condition in which the immune system attacks the stomach's own lining. This leads to atrophy of the stomach wall and destruction of the glands that produce hydrochloric acid and intrinsic factor. This disrupts digestion, but most importantly, the absorption of iron, vitamin B12, folate, calcium, and other essential nutrients.

🧬 While in some cases this immune system behavior can be triggered by H. pylori, AIG most often occurs independently and is likely linked to genetics. This is also indicated by the fact that it often coexists with other autoimmune diseases—Brian, for example, had thyroid disease in his youth. So, while there are some questions about follistatin and other strange interventions, it's unlikely that Johnson developed AIH due to his addiction to dietary supplements (of the medications, a link has only been established for checkpoint inhibitors, and they are not in his protocol).

🔺 This disease is life-threatening, not because of the destruction of the stomach, but because of the development of pernicious anemia. A deficiency of B12 leads to malformations of blood cells, as well as nervous system disorders, including paralysis, psychosis, and death. But if you start taking iron, folic acid, B12, and other essential nutrients promptly and consistently, you can live a long and happy life. In this regard, Johnson's regular checkups were a benefit – he was diagnosed early, based on low ferritin levels, before irreversible brain damage developed.

🌿 Another threat comes from neoplasms. Reduced gastric acidity activates the growth and division of gastrin-producing glandular cells, leading to the formation of multiple neuroendocrine tumors (NETs) in the gastrointestinal tract. These tumors rarely become malignant or metastasize, but they cause significant problems due to their hormone secretion. To treat them, Brian will need periodic endoscopies and their removal. As for stomach cancer, the risk of adenocarcinoma in AIH, if increased, is very small, and most likely a side effect of pernicious anemia. Therefore, he won't die of cancer if he gets B12 and iron.

💉 It's difficult to say how many people are actually susceptible to this disease; estimates range from 0.5 to 4%. Women, people over 60, and those with other autoimmune diseases (type 1 diabetes, thyroiditis, vitiligo, etc.) are more likely to be affected. Moreover, according to some data, up to half of people with iron deficiency anemia of unknown etiology may actually have AIH. To assess your risk, you can use the unofficial list of red flags I took from this article (see the image attached to the post). If you consistently experience several of these symptoms, have low ferritin, or other signs of iron deficiency, it may be worth consulting a doctor for a detailed examination.

💊 In his post, Johnson says, "You keep telling me to "party" and live "the fullest," but if I listened to you, I'd be dead by now:

You too may have hidden health problems that are undiagnosed and can be worsened by an unhealthy lifestyle, even if you don't know it. The absence of symptoms doesn't mean you're healthy.

In reality, it could be the other way around. A vegetarian diet (rich in polyphenols and curcumin), metformin, and high doses of calcium—all of these factors in themselves reduce the absorption of iron and vitamin B12, worsening the course of AIH and increasing the risk of severe complications. This same diet forced Johnson to take B12 and iron supplements, which masked the AIH and likely delayed diagnosis. But most importantly, who knows if he would have had this gastritis at all if not for the stem cell transplant and his son's blood transfusion? And we still don't know what his attempt to "cure" himself will lead to.
Think about it.

Harvard/Zitnik Lab presented ATHENA-R1: an AI agent selects treatments from FDA-approved drugs and explains its decision using verifiable sources.

The system operates as a search chain: it decides what data is needed, invokes biomedical tools, and compiles a response with a visible evidence trail. The authors tested it on drug problems, patient scenarios, expert assessments of rare diseases, and historical data from 5.4 million patients.

In medicine, choosing the right drug from existing options is often difficult. The patient has a disease, age, kidneys, liver, other medications, contraindications, risk of side effects, and incomplete recommendations. In such a task, confident text is dangerous: the doctor needs to see the origin of each step.

On June 27, the team of Shanghua Gao and Marinka Zitnik from Harvard Medical School published the ATHENA-R1 preprint. The model, based on Qwen3-8B, was trained to work with 212 biomedical tools. These tools access open sources like the FDA's drug label database, Open Targets, and Human Phenotype Ontology, an ontology that links human signs and symptoms to diseases.

The project's website provides a simple example: an elderly patient with diabetes, hypertension, and early chronic kidney disease is taking metformin. ATHENA-R1 must check dosages for reduced kidney function, interactions with other medications, warnings from the label, and suitable alternatives. Finally, it provides a recommendation and a reasoning trail: which sources it accessed and what it learned from each.

The authors call this treatment reasoning. The Russian translation is that the system selects a therapy step by step based on the patient's limitations. This is a familiar problem, amplified for future anti-aging medicine: geroscience drugs, senolytics, mTOR modulators, GLP-1 agents, anti-inflammatory regimens, and cell therapies will all face comorbidities, drug interactions, and weak endpoints.

A few days ago, MIRA and AMIE tested medical AI agents in a virtual clinic: one agent worked in an electronic health record sandbox, while the other guided an actor patient through three outpatient visits. ATHENA-R1 takes the next step in the same story: drug selection, dosage, and constraints from external sources, followed by a visible trace of how the model arrived at its answer.

In the preprint, ATHENA-R1 scored 94.7% on 3,168 drug-data tasks and 82.9% on 456 patient-specific scenarios. GPT-5 scored 76.9% and 72.2% in the same open evaluations. To reduce the risk of memorization, the authors based some of the tests on FDA-approved drugs approved in 2024, and excluded drugs approved after 2023 from the training.

The team recruited experts through 28 rare disease organizations; Twenty-three raters blindly compared 110 ATHENA-R1 responses with responses from other models and favored ATHENA-R1 more often across eight criteria, including accuracy, clinical relevance, and clarity of the chain. The team then took ATHENA-R1's adverse event hypotheses and tested them on Clalit Health Services' electronic medical data: three of the six predictions yielded statistically significant increases in risk in the relevant patient groups.

The system's status is limited. The project's GitHub page describes ATHENA-R1 as a research artifact for studying treatment reasoning and decision support; it has not been approved for clinical use. Retrospective validation reveals associations in past data, and the benefit of prescribing treatment based on model advice should be verified by future studies.

Medical AI is gradually moving away from "memory-based" responses to a procedure in which the model must know what to look for, where to check, and how to show a trace. For longevity, such a procedure is more beneficial than yet another confident assistant: the fight against aging will rest on the ability to safely select interventions for living, complex individuals already undergoing treatment.

A portion of the tech elite is starting to serve up a selection of embryos and future editing of heredity as a way to catch up with superintelligence. On April 16, Mother Jones published a large exposé on how former MIRI researcher Tsvi Benson-Tilsen, the Berkeley Genomics project, and a circle of related investors are linking the fear of AGI (Artificial General Intelligence) with the market for "enhanced children." Here, embryo selection, discussions of future heredity editing, and the money of people invested in both AI and genomics startups converge. AGI refers to an artificial general intelligence system that can solve a wide range of tasks at or beyond the level of human capabilities. In the AI-risk environment, the fear is formulated as follows: the next step for such a system may be to become superhuman, and humans will no longer understand its goals and lose control. Benson-Tilsen worked for seven years at the Machine Intelligence Research Institute, where they tried to solve this very problem. According to Mother Jones, he came to the conclusion that he himself was not capable of solving it, and that others had not succeeded either. His response now is biological. At the end of 2024, he launched the Berkeley Genomics Project. On the project's website, the mission is stated directly: to open the path to safe and accessible heritable genome engineering, i.e., to modify DNA in an embryo or reproductive cells so that these edits are inherited not only by the child but also by their descendants. Among the promised benefits listed are protection from diseases, protection from severe mental disorders, a longer life and more healthy years, and a "more capable mind." The political continuation is also stated: the USA should lead in this technology. It is essential to distinguish between two things. Today, the market primarily sells embryo selection: during IVF, several embryos are obtained, their DNA is checked, and parents are helped to choose one for transfer. For some severe hereditary diseases, such a check is understandable. For complex traits like intelligence, everything is much weaker. They depend on many genes, as well as the environment, nutrition, family, and school. A calculation in Cell in 2019 gave an average expected gain of about 2.5 IQ points when choosing from five embryos. This is little. The history of SAT scores has shown how quickly the conversation about "innate abilities" begins to confuse heredity with language, school, and environment. Until the full-fledged "construction of geniuses," the market has not yet reached. But the language of the field has already moved further than practice. Brian Armstrong described a future IVF clinic in his post on X as a "Gattaca screen": "The IVF clinic of the future will combine several technologies: the production of egg cells from skin or blood, the selection of an embryo that best fits the parents' request, ideally from thousands of options, editing the embryo for disease prevention or improvement, and artificial wombs." In this formula, an entire conveyor belt is assembled. First, egg cells are made from skin or blood cells. Then, many embryos are created. Then, they are compared according to genetic probabilities, one is chosen, additional edits are made, and the pregnancy is carried out in an artificial womb. Moreover, the artificial womb is already being assembled: there are models of embryos, artificial placentas, and a hermetically sealed environment for the development of the fetus outside the body. A set of controversial technologies is already being presented as a single product roadmap. The topic goes beyond the debate about fertility and disease prevention. The same tech elite networks are simultaneously fueling the AGI agenda, financing AI, and starting to justify heritable human upgrades as a response to the risk from these systems. Yesterday, the market was selling wealthy parents a slightly more advantageous choice between embryos — we already had a sepa…

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A major review: weight loss, heart protection, and long life in obesity medications differ. On July 8, BMJ published a review of 262 randomized trials - studies where participants are randomly assigned a medication or a comparator - with nearly 100,000 participants. The medications differ significantly in terms of weight, cardiac outcomes, side effects, and quality of life. Subcutaneous semaglutide was the only medication with a convincing reduction in overall mortality; these data came mainly from studies of people with already high cardiovascular risk. The market has become accustomed to comparing such medications by the number of kilograms lost. For someone who wants to live longer, this scale is too crude. Weight may decrease quickly, but the data on survival, heart function, and well-being remain different for each medication. The review authors compared 19 medications and found: in terms of weight loss, tirzepatide and the combination of cagrilintide with semaglutide led. Subcutaneous semaglutide reduced body mass by approximately 9.8% more strongly than a single lifestyle change. But among all medications, only it showed a convincing reduction in overall mortality: by 19% in 17 trials with 25,264 participants. The risk of myocardial infarction was 28% lower. The reduction in mortality in this review refers to subcutaneous semaglutide in people with existing cardiovascular diseases. For people without such diseases and for medications with similar effects, separate data are needed: absolute risks and outcomes may differ. Tirzepatide, for example, reduced the risk of heart failure, and the data on its effect on overall mortality have lower confidence. Weight loss and quality of life assessments differed. The authors considered a clinically significant difference to be 10 points on the well-being scale. All medications remained below this threshold; subcutaneous semaglutide had a score of 2.9 points. In the study of body composition with fat, tirzepatide also reduced lean mass - tissues that include muscles, water, and other components of the body. For each medication, one must ask which specific outcome it changes: weight, myocardial infarction, mortality, ability to move, or everyday well-being. Then - in whom exactly was this tested and how long was it observed. The scale on the weights answers its own question; prolonging life requires data on mortality and organ function. Source: BMJ Telegraph.

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Professor of Yale Samuel Moyn suggests giving the young more votes: the debate on aging has already become a debate on rights. In July interviews about the book "Gerontocracy in America", Moyn asserts that power, money, and voters in the US are too skewed towards older generations. Among his ideas are age limits for part of the positions and different political weight for people with different remaining life expectancy. On July 13, The New York Times made a separate video report on this debate. Yale law and history professor Samuel Moyn has just released the book "Gerontocracy in America", around which the discussion has unfolded. Gerontocracy, in his definition, is the concentration of power among older generations through money, property, and the setup of elections. In a July 9 interview with Yale University, Moyn cites such figures: the age of half of the members of the US Congress is over 60, half of the voters are 52 and older, and workers over 55 make up about a quarter of the workforce, compared to 10% in 1990. He links this picture to the fact that it is harder for young people to get housing, jobs, and political representation. Moyn does not limit himself to changing faces in Congress. He suggests discussing age limits for part of the positions, compulsory voting, and a different way of counting votes. In a debate with political scientist Yascha Mounk, he explains his logic as follows: a person who will live longer with the consequences of a law has a greater stake in it. Hence the idea of giving young people more political weight. Here, the debate on aging turns into a debate on the value of remaining life. Moyn wants to protect future generations from decisions that they will live with longer than everyone else. But age poorly separates power from powerlessness. He himself acknowledges that elderly people without means also suffer from the current system. Wealth and influence are concentrated among a minority, and age often coincides with this concentration. Radical life extension makes his argument more dangerous and clearer. If political weight depends on life expectancy, the state gets a reason to reduce a person's vote precisely because they will live longer. Health and survival become the basis for reducing political rights. The same problem has already arisen in the debate on space resources, which can determine human lifespan. There, the rich can buy more time to live. With Moyn, lifespan becomes the criterion by which the state gives different political weight. In both cases, access to rights depends on the length of life. Gerontocracy arises where power is not changing, wealth is accumulating, and access to decisions is closed. Young people need representation and protection of the future; older people need full rights and reliable social protection. The struggle against death requires a common right to a long life, rather than the distribution of civil rights by age.

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#digest for July 14 Full article • The Alzheimer’s Association will invest $100 million to investigate: does the medication add protection against dementia to the prevention program • At the ICML workshop, Bruno was presented - an AI assistant that is supposed to keep track of the work of a scientific group • Takeshi Kozai: a neurointerface in 20 years should preserve living tissue around the electrode • JAMA: in Americans of the same age, dementia over 40 years has become approximately two-thirds less common • A large review: weight loss, heart protection, and long life in obesity medications diverge • Some techno-elite are starting to offer embryo selection and future gene editing as a way to catch up with superintelligence • Professor Samuel Moyn of Yale proposes giving the young more votes: the debate about aging has already become a debate about rights Telegraph Digest @UkhvatNews - July 14 When talking about the "wave of dementia", people usually combine two different facts into one sentence. The first fact: there are more people living up to 80, 90, and 100 years. The second: the likelihood of losing memory, orientation, and independence increases with age. From this, it is easy…

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Demis Hassabis proposes creating a US agency to test advanced AI systems before release. The head of Google DeepMind suggests creating a US organization that would determine the threshold for advanced AI and test such models before release. The initial tests would be voluntary; if the methodology proves effective and reliable, Hassabis proposes making passing the test a condition for a model to operate on the American market. On July 14, Hassabis published an essay on rules for advanced AI systems. In his scheme, a model is considered "advanced" if it achieves a set result in test tasks. The new organization would set this result and regularly change the tasks as the capabilities of the models grow. This would determine which laboratories would be subject to a special testing regime. Hassabis takes FINRA, a private US organization that oversees brokerage firms under the supervision of the US Securities and Exchange Commission, as a model. According to his idea, the council of the new agency should include independent technical specialists, as well as representatives of the state, industry, and open-source software developers. "Initially, laboratories would voluntarily submit models to the agency for testing no later than 30 days before release." The tests should assess cyber risks, biological threats, and other high-risk areas; Hassabis separately considers nuclear risks as potential threats. Separate tests could look for attempts to bypass built-in restrictions or signs of deception. If the methodology proves effective and reliable, Hassabis proposes making passing the test a condition for releasing an advanced model in the US. CAISI, a center at the US National Institute of Standards and Technology, has already gained access to Google DeepMind models before public release to assess bio-risks, cyber threats, and risks to critical infrastructure. In Hassabis' proposal, the new agency would itself establish the threshold beyond which a model would be subject to testing. If testing becomes mandatory, the agency would determine the threshold for an "advanced" model and the set of tasks it must pass to enter the US market. Hassabis allows for the possibility of coordinating the slowing down of development between laboratories with models of the advanced class if the situation requires it. Large-scale testing requires computational power and specialists, so Hassabis expects funding from the industry. The first methodologies would be developed in consultation with advanced-class laboratories. Then, the agency should build the capacity to create independent tests, with which developers are not familiar in advance. Such tasks reduce the chance of tailoring a model to known questions. The budget, council composition, and ability to create its own tests would determine the independence of the future agency. Under universal AI, Hassabis understands a system with a human set of cognitive abilities and expects it to accelerate science, medicine, and drug discovery. His proposal provides for testing advanced models before releasing them to the US market.

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In long-lived mammals, the SIRT6 protein has more sites for phosphate marks than in short-lived species. The group of Vera Gorbunova and Andrei Seluanov linked this feature to a stronger interaction between SIRT6 - a protein involved in DNA repair - and PARP1. PARP1 recognizes DNA damage and assembles repair proteins. In human cells, mimicking one phosphate mark helped cells survive oxidative damage. In an article published on July 8, researchers studied the flexible C-terminus of SIRT6. Phosphorylation is the addition of a phosphate group to a protein, which changes the protein's electric charge and its interaction with other proteins. The authors compared the SIRT6 sequence of more than 150 mammals with their maximum lifespan. In longer-lived species, this tail had more potential sites for phosphate marks. One of these sites, T294, is present in humans and absent in mice. Using the CRISPR method of precise DNA editing, researchers replaced T294 in human fibroblasts, cells of connective tissue. The T294E variant mimics phosphorylation: after treatment with hydrogen peroxide, such cells survived better. The T294A variant excludes phosphorylation at this position; SIRT6 with this substitution bound weaker to PARP1. The comparison of species linked the number of potential phosphorylation sites to maximum lifespan. The authors counted these sites in sequences; they measured the amount of phosphorylated SIRT6 only in cells of several species. After simultaneously accounting for body mass and species relatedness, the connection between T294 and longevity became insufficiently convincing for a confident conclusion. Therefore, further verification requires mice with a point substitution in the Sirt6 gene and simultaneous measurement of health and lifespan. In a February preprint from the same laboratory, researchers introduced in mice a constant mimicry of phosphorylation at another SIRT6 site, S10E. After irradiation, DNA repair in these mice was better. The median lifespan of males decreased by 10%. After irradiation, in the blood cells of S10E mice, LINE1 expression was higher; in the intestine and brain of individual groups, the authors saw a tendency towards its increase. LINE1 is mobile repetitive fragments of the genome: they create new DNA copies, and accumulated LINE1 DNA in the cytoplasm triggers an inflammatory response. The authors suggest that weakening this control shortened the life of S10E males. For T294, the experiment on mice should immediately measure DNA repair, LINE1 suppression, inflammation, and lifespan. Such a set of results will show whether enhanced DNA repair maintains protection from LINE1 and whether T294 changes lifespan.

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Chai Discovery has raised $400 million: AI-designed molecules enter research programs at Pfizer and Novartis. On July 14, Chai Discovery announced a $400 million Series C round at a valuation of $3.8 billion. The funds will go towards computations, data, research, and product development; previously, Pfizer and Novartis had signed separate agreements with the company. The search for a new protein drug begins with a question: which molecule is worth synthesizing and testing in the first place? An antibody is a protein that must recognize and bind to a specific biological target. There are too many amino acid sequence variants to blindly test them in a laboratory. Chai builds models that predict molecular interactions and propose protein candidates with specified properties. These models help select variants for initial laboratory experiments; then, candidates undergo target binding testing, animal testing, and human trials. In June, Biohub models had already yielded binding proteins that had reached laboratory testing. Chai sells pharmaceutical companies the same early stage of molecule search as a working tool. On June 5, Pfizer signed a licensing agreement with Chai. The company will receive early access to Chai-3 and a separate model that uses Pfizer's closed data and is tailored to its own way of searching for drugs. Pfizer is connecting molecular AI to its closed data and development workflows. On July 13, the day before the round announcement, Novartis announced a collaboration with Chai to search for therapeutic antibodies for several targets at once. Prior to this, the companies had been working together technically for over a year; now, Novartis is gaining access to Chai-3 for its own therapeutic programs. The new Chai round will go towards computations, data, research, and product development. The company plans to expand its computational capabilities and the data on which it builds new model versions. The first test of these deals will be the candidates that pass laboratory testing and enter clinical programs.

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Limiting calories causes pancreatic cells to conserve calcium instead of a large overall insulin release. In mice that received 20% less food for eight weeks, pancreatic cells changed the rhythm of calcium impulses and withstood artificially induced stress longer. The work shows how a lower demand for insulin by the body can unload a specific type of cell. On June 18, in Cell Calcium, a team from Vienna and Vanderbilt University published a study on beta cells - pancreatic cells that release insulin. The authors sought to answer a simple question: how does a lower demand for insulin by the body change the functioning of these cells? A beta cell releases insulin after a calcium surge. Part of the calcium is stored in the endoplasmic reticulum - an internal reservoir of the cell that also helps to collect proteins. If the reservoir empties quickly, the cell loses its stable rhythm of operation. The authors fed young male mice 20% less food than usual for eight weeks, then observed calcium signals in fresh pancreatic sections. In animals with calorie restriction, beta cells sent shorter and more frequent impulses. Within the islet of Langerhans - a cluster of beta cells that usually release insulin together - their signals coincided in time less. For a healthy body, this is similar to a change in mode. Tissues became more sensitive to insulin and required less of it. Beta cells did not have to gather in a dense network for a simultaneous large release of the hormone. They could work more separately and expend calcium in small portions. Then, the researchers gave the cells a high dose of acetylcholine. It opens the IP3R channel in the endoplasmic reticulum and forces it to quickly release calcium. In control mice, the fluctuations soon subsided; in mice with calorie restriction, they continued longer. Their calcium reserve withstood the load better. This continues the work of the same group from 2024: then, it showed that calorie restriction in mice increases insulin sensitivity, maintains the state of beta cells, and reduces their turnover. The new article adds a possible mechanism: lower external demand changes the rhythm of calcium, cell synchrony, and the ability to withstand a sharp expenditure of the internal reserve. Synchrony depends on the state of the animal. In 2020, another group saw how calorie restriction restored the coordinated work of beta cells in prediabetic mice with obesity. Healthy mice in the new study required less insulin, so the signals within their islets became more separate. In these two models, beta cells adjusted their overall rhythm to the body's demand for insulin. The authors studied eight weeks of feeding in young male mice and acute stress in pancreatic sections. The lifespan of the animals, the risk of diabetes, and the work of beta cells in humans were not included in this study. In the CALERIE human trial, other effects of moderate calorie restriction have already been seen, such as a decrease in C3a - an inflammatory signal of the immune system. The study does not report on the calcium work of the pancreas: we analyzed it separately. In this model, protection arises before damage: tissues require less insulin, so beta cells less often expend the entire calcium reserve on peak loads.

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Anil Seth: the working space found in Claude shows reasoning, but does not prove experience. On July 15, a professor of cognitive and computational neuroscience responded to Anthropic's work, which found an internal common channel for intermediate thoughts in Claude. Seth acknowledges the result itself, but believes that the similarity to one of the theories of consciousness does not allow us to conclude that the language model feels anything. On July 6, Anthropic described a narrow section of computations within Claude, which it called J-space. The model holds a word or intermediate result there and then uses it in different tasks. Researchers replaced the internal representation of "spider" with "ant" - and Claude instead of eight named six legs. When they suppressed J-space, the model retained fluent speech and simple responses, but almost lost multi-step reasoning. Such a section is similar to what the theory of global workspace is looking for in the brain: information that becomes available to many systems at once. In everyday life, a person can hold a number, intention, or image in mind, tell about it, and apply it in a new action. Anthropic showed a functional analogue of this operation in Claude: a common internal representation can be read, modified, and causally linked to the model's response. Already on June 2, Google DeepMind, Anthropic, and Meta had referred the possible experience of models to a research task: DeepMind hired a philosopher, and Anthropic is leading a program to study model welfare. J-space gives this debate a measurable subject - an internal representation, the role of which can be changed in experience. In a column for The Guardian, Anil Seth draws a line between such access to information and subjective experience. The first answers the question of whether a system can hold a thought, report it, and use it for selection. The second is whether this system has its own "what it feels like": pain, color, fear, pleasure. Seth points to recurrent loops - feedback cycles in which signals repeatedly pass through brain circuits and support representation over time. In Claude, J-space processing fits into one pass through the network; Anthropic itself notes this distinction. Seth links consciousness to how a living nervous system works together with the body and the world, and not just with the calculations of a silicon program. Seth believes that similar computational organization is not enough to conclude experience. Anthropic limits its result to functional "conscious access": the model can hold content, report it, and use it in reasoning. The authors do not attribute subjective experience to Claude. The future transfer of personality will have to be tested along two different lines. The system must retain memory, goals, and accessible reasoning to continue acting like the original person. Separately, the theory of consciousness must explain what properties of the carrier support its internal experience. J-space provides a way to measure the first line in a language model; the second remains a subject of debate about the brain, body, and consciousness.

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AI 2027 underestimates synthetic biology: it may launch a self-replicating economy earlier than robotized factories. On July 14, an author from LessWrong proposed considering an earlier transition to the smallest systems capable of copying themselves from available raw materials. One of the co-authors of the scenario supported part of this criticism. The AI 2027 already has a distant variant with biological infrastructure. In the AI 2027 scenario, the US and China create special economic zones with simplified construction rules. In the American zones, Agent-5 - a superintelligence from the scenario - designs robots and, through humans, manages the construction of factories and laboratories; new factories produce even more robots. As a reference, the authors cite a car factory that in less than a year produces a mass of cars comparable to its own. Then they suggest that an autonomous robotic economy could reproduce itself faster than a year. In response to this scenario, Thomas B. suggests looking for the future leap below the factory scale. He suggests that it would be beneficial for the superintelligence to reduce the unit of self-replication to the limit and design organisms and nanotechnology systems that grow and copy themselves using the surrounding raw materials. The argument is based on the speed of experimentation. In Thomas B.'s opinion, the AI 2027 assumption about robots at the human level would mean automated laboratories without a shortage of qualified human labor. He suggests the next step: such laboratories could create many cheap living sensors and executive systems, conduct experiments with them in parallel, and give the results to simulations. Improved simulations help design the next version of organisms and devices. This research reduces the time for the next research. The dispute comes down to the speed of feedback. A factory needs buildings, machines, supplies, and large machines. A self-replicating biological system could become a sensor, an object of parallel experiments, and part of the next production system. Thomas B. suggests putting this possibility at the center of the scenario. AI 2027 itself is already moving in this direction. The authors cite plants, insects, and bacteria as benchmarks for reproduction rates. In a distant variant, they suggest an economy similar to an ecosystem of new algae: some organisms grow, others process them into materials for floating factories. The disagreement concerns the time of emergence of this biology. The main line first unfolds a robotic economy; Thomas B. considers biological self-replicating systems a likely early driver of acceleration. Thomas Larsen, one of the co-authors of AI 2027, in the comments, agreed with the main criticism and wrote that the finale was worth linking more strongly to nanotechnology. The dispute shifts the question about superintelligence from the number of robots to the length of the "experiment - data - new system" cycle: if this cycle can be compressed to the speed of reproduction of created organisms, factories will cease to be the only measure of production capacity.

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Dextran, albumin, and sialic acid helped preserve extracellular vesicles for a year at -20 °C. Chinese researchers have assembled a cryoprotective mixture, DDAS, for extracellular vesicles - tiny membrane particles that cells use to transport proteins and nucleic acids. After storage at -20 °C and repeated thawing, these therapy candidates retained more structure and biological activity than in a conventional buffer. Extracellular vesicles are being considered as a future means of delivering stem cell signals, proteins, and RNA to the body. Such a preparation must travel from the laboratory to the patient, survive storage, and remain the same preparation. When frozen, vesicles aggregate, their membranes rupture, and their contents leak out. Their action is determined by the molecular cargo: four microRNAs in vesicles from an aging liver enhanced metastases in mice. Therefore, for therapy, it is not enough to preserve the number of particles; it is necessary to preserve the specified composition and function. A conventional phosphate buffer poorly solves this problem. DMSO and glycerin can protect biomaterial, but they cannot be left in the preparation for direct intravenous injection: after thawing, additional purification is required. The same engineering problem already exists for platelets: in a laboratory test, a low-dose DMSO regimen without washing returned 94.4% of cells after thawing. The authors of DDAS checked whether it is possible to store vesicles so that after thawing, they can be administered without additional purification. On July 13, an early unedited version of the work on DDAS was published in the Journal of Nanobiotechnology. The mixture includes dextran, albumin, and sialic acid; the authors selected it by starting with a list of molecules found in human body fluids. Generative models narrowed down the list of candidates, and the composition was then tested on vesicles, cells, and mice. Vesicles from brain vascular endothelial cells were stored at -20 °C for a year. DDAS better preserved the number of particles, RNA, and membranes than a conventional buffer; by the 12th month, the authors recovered around 50% of nucleic acids. After five cycles of freezing and thawing, it also outperformed the buffer and a commercial cryoprotector. The authors suggest that dextran reduces particle collisions, albumin forms a protective film, and sialic acid gives the vesicle surface a negative charge. Vesicles stored in DDAS can be administered intravenously directly after thawing. In an experiment on six young male mice with deep burns, stem cell vesicles stored in this mixture accelerated wound closure more than the same vesicles from a conventional buffer. By the 12th day, the wounds of mice that received vesicles from DDAS were almost closed. Freezing damages the membranes of vesicles and their cargo; DDAS, according to the authors, allows them to be administered without repeated purification.

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