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

Self‑Training Bioengineered Muscle Implant Improves Aging in Mice Scientists took mouse muscle stem cells, expanded them, formed tissue, and implanted it under the skin of aged animals. The resulting bio‑transplants (myografts) autonomously built a vascular network and began contracting spontaneously 24 hours a day, 7 days a week, without any nervous‑system or brain input. The contracting muscle acts as a continuous biological factory, releasing myokines and signaling molecules into the bloodstream. Old mice receiving these subcutaneous “patches” showed increased lean body mass, stronger grip, better treadmill endurance, higher bone density, and reduced inflammation markers. In the mice brains, the number of degrading neurons in the hippocampus fell, BDNF levels rose, and spatial memory improved. (Although the brain‑test sample was tiny – only 3 individuals per group.) The myograft is not just a gym mimic but a removable biological reactor. Researchers genetically engineered the implanted cells to secrete parathyroid hormone (PTH) and growth hormone, giving a stable blood protein level without the spikes and drops seen with injections. The experiments used Matrigel matrix, which is unsuitable for humans, so clinical translation will require a different scaffold and scaling of autologous cell production. Nevertheless, the concept of a subcutaneous muscle‑doser has already proven viable. 🔗 Read original →

Actin cytoskeleton disruption shortens worm lifespan, while mild stabilization extends it On August 24, a bibliographic record of an iScience article appeared in Crossref. In the full text, authors altered actin cytoskeleton function in the roundworm Caenorhabditis elegans: network disruption shortened life, while mild chemical stabilization extended it; a high dose produced the opposite effect. The actin cytoskeleton helps muscles contract, maintains intestinal cell junctions, and participates in intracellular transport. In prior work from the same research line, increased production of the BET‑1 protein preserved actin in old worms and extended their lifespan. The new study examines the converse: what happens when the network loses order. Authors sequentially weakened actin and three proteins that direct its assembly, disassembly, and anchoring. In muscle, intestine, and cuticle, filaments lost normal organization earlier, and mobility declined with age. In some worms, gene activity resembled that of older animals. Because the age of intervention matters — in another C. elegans experiment, transient NuA4 suppression in early development extended lifespan, while later intervention shortened it — part of the new experiments began on the first day of adult life. Lifespan reduction persisted upon suppression of arx-2, a component of the Arp2/3 complex that builds branched actin networks. Separately, adult worms were given two substances. Latrunculin A breaks actin filaments and, with increasing dose, shortened life. Jasplakinolide helps filaments assemble and stabilize: at low concentrations it extended life, at high concentrations it shortened. Authors link the harm of high doses to overly stabilized filaments being less able to remodel. Actin disruption simultaneously altered mitochondrial shape and function, protein homeostasis, cellular component recycling, and intestinal barrier integrity. In two independent human cohorts, authors also correlated

We Will Cure Releases Longevity Biotech Clinical Trials Map Through 2027 On August 25, the editorial project We Will Cure published a map of longevity biotech clinical trials planned through 2027. It aligns programs that have already begun human studies, obtained initial data, or are preparing the next stage. By the end of 2027 they will answer whether humans tolerate the intervention, whether it affects the targeted process, and whether there is grounds to expand the trial. Companies test aging‑biology ideas through concrete diseases and quantifiable 🔗 Read original →

Cryoprotectant penetrates fixed human brain over nine months On 24 August PLOS One published a protocol for storing whole human brains at −20 °C after chemically fixing their cellular structure. The goal was to assess how a cryoprotectant solution diffuses through the tissue and whether fine structure survives cooling, storage, and rewarming. Brains were first fixed with a cross‑linking agent to lock in tissue architecture after death. 🔗 Read original →

AI-designed short proteins improve CAR-T receptor function via surface charge tuning On 20 August, researchers from the University of Bonn and Bonn University Hospital published work on AI-designed short proteins for CAR‑T therapy. CAR‑T adds an artificial receptor to T cells; its external part recognizes a tumor protein and its internal part triggers 🔗 Read original →

Neurosurgeon Proposes Brain Preservation to Curb Risky AI Rush On August 27 neuroscientist Ariel Zeleznikov‑Johnston published an essay in the official mailing of the Brain Preservation Foundation about biostasis – preserving a dying person’s brain with chemicals and cold. He argues that this procedure could give people time to wait for future medicine and reduce their personal willingness to take risks for rapid AI development. He begins with the dilemma: a superintelligent AI could accelerate drug discovery, but loss of control over such a system might be catastrophic. Nick Bostrom’s calculation of the cost of a pause makes acceleration attractive for those who would need future medicine in their lifetime, while David Wood had proposed slowing the race and strengthening biology. In the new essay Zeleznikov‑Johnston shifts the calculation to the individual. Biostasis stabilizes the brain of a dying person with chemicals and cold; he links long‑term memory and personality traits to the brain’s physical structure. Preserving that structure, he writes, offers a chance to await future treatments or even reading of the mind. “Make death less likely – and big risks stop seeming so reasonable. If you knew you’d still be here in fifty years regardless of whether superintelligence appears and when, reasons to bet on dangerous AI acceleration would be far fewer.” In his model biostasis provides another way to await future medicine, thereby diminishing the benefit of risky AI 🔗 Read original →

Anthropic Opens Research Access to Model Hardware Standard for AI Agents and Lab Instruments On 27 August, Anthropic launched research access to the Model Hardware Standard (MHS). MHS gives AI agents a common way to read data from lab instruments, adjust available settings, and respect predefined safety limits. In early partner pilots, several laboratory devices were already coordinated through MHS. Lab work often suffers delays because cameras, robotic arms, and plate readers use different software, requiring a custom interface for each new setup. As noted in the announcement, configuring and integrating equipment normally takes weeks, sometimes months. MHS provides each instrument with a standard driver that translates OS commands into hardware actions, 🔗 Read original →

Pigs with POLG D260A mutation model accelerated mitochondrial aging Researchers edited the POLG gene, which proofreads mitochondrial DNA copies, to create an accelerated aging model in pigs. The work was described in Science Advances on 28 August and details 12 pigs that carry the D260A substitution in both copies of POLG. These animals accumulated mitochondrial DNA mutations, and ear‑derived connective tissue cells consumed less oxygen under both basal and maximal conditions. None of the pigs lived longer than seven months. POLG encodes the polymerase that replicates mtDNA; its built‑in exonuclease proofreads the new strand for errors. The authors changed aspartic acid at position 260 to alanine, creating the D260A variant. This mirrors a 2004 mouse study where the analogous D257A substitution increased mtDNA point mutations three‑fivefold and produced early aging‑like traits in 2004. The edit was first introduced into fetal 🔗 Read original →

We need to translate Russian news post into natural English, format per rules. First line: short headline under 90 chars, no markdown, no '#'. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026). We need to preserve all facts, numbers, names, citations exactly. No added commentary. We need to identify important facts: Tempus presented oFM model, date: August 25, on arXiv preprint. Corpus size: 1,672,203 patients. AUC numbers: 0.774 vs 0.563. Second test: 11 patient groups, oFM ahead in nine of eleven comparisons. Possibly also mention linear predictor, etc. We need to limit to at most 4-5 double asterisk facts. Choose: August 25, 1,672,203 patients, AUC 0.774 vs 0.563, 11 groups, nine of eleven comparisons. That's 5 facts. We need to wrap each fact in double asterisks, but not whole sentence. So embed within sentences. Also need to wrap study/journal citations in single underscores. There's mention of arXiv preprint. Could format as arXiv preprint, August 25. Or maybe arXiv. We'll include citation: arXiv preprint, August 25. We must not add source link. Now produce translation. Let's craft headline: "Tempus releases oFM model that reads cancer patients' treatment timelines". Count characters: Let's count: "Tempus releases oFM model that reads cancer patients' treatment timelines". Count: Tempus(6) + space=1 =>7, releases(8)=>15, space=1=>16, oFM(3)=>19, space=1=>20, model(5)=>25, space=1=>26, that(4)=>30, space=1=>31, reads(5)=>36, space=1=>37, cancer(6)=>43, space=1=>44, patients'(9 inc apostrophe?) patients' = 9? Actually "patients'" is 9 inc apostrophe? Let's count: p(1)a2t3i4e5n6t7s8'9 =>9, space=1=>54, treatment(9)=>63, space=1=>

We need to translate Russian news post into English, format per rules. First line: short headline under 90 chars, no markdown, no '#'. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026). We must preserve all facts, numbers, names, citations exactly. No commentary. We need to identify key facts: numbers like 4 months, 1 month, three doses, median lifespan increase, ages 14 and 17 months, etc. Also gene names p16Ink4a, p21, drug AP20187, line INK-ATTAC, study published 26 August in Aging Cell. Also mention navitoclax, dasatinib+quercetin 2022 study. We need to limit double asterisks to at most 4-5 per post. Choose maybe 5 important facts: "four months", "one month", "three doses", "median lifespan increased in females", "ages 14 and 17 months". Also maybe "published 26 August in Aging Cell". That's 6; need max 5. We can combine some: maybe include date as part of citation? The citation is wrapped in single underscores, not double asterisks. So date inside citation not need double asterisks. So we can keep double asterisks for: four months, one month, three doses, median lifespan increased in females, ages 14 and 17 months. That's 5. Check: also gene names maybe not need double asterisks? They are important facts but could be considered gene names; but we have limit. We'll prioritize numbers. Now formatting: first line headline. Something like: "Clearing p16‑positive cells four months after irradiation improves mouse health and lifespan". Count characters: Let's count: "Clearing p16‑positive cells four months after irradiation improves mouse health and lifespan". That's about 78 characters (including spaces). Good. Now blank line. Then

AI‑Designed DNA Enhancers Drive Tissue‑Specific Gene Expression in Mouse Embryos On August 25, _ 🔗 Read original →

We need to translate Russian news into English, format per rules. First line: short headline under 90 chars, no markdown, no '#'. Then blank line, then body paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores .... We have no explicit citations/journal references in the text. So we may not need underscores unless we infer something like "Tomorrow Biostasis" etc. Not needed. We need to preserve facts: Tomorrow Biostasis testing storage for whole body at -140°C on August 27. Vessel delivered to European Biostasis Foundation site in Rafz, Switzerland; setup will take several months. Previously Tomorrow Bio roadmap had intermediate temperature storage (ITS) as next step after current cryopreservation procedures. Now company testing whole-body vessel for this step at its facility. ITS is intermediate temperature storage, designed for vitrified body: after treatment with protective cryoprotectant solution, fluid in tissues turns into solid glass-like state on cooling. Further cooling causes this glassy content to contract. Large body external and internal parts may cool and contract differently; difference creates mechanical stress that can cause cracks. Modeling of large vitrified samples links temperature gradients to such stress and cracking. Long-term cryogenic storage usually done at liquid nitrogen temperature, about -196°C. ITS designed for storage around -140°C: below glass transition temperature when solution solidifies as glass, but warmer than liquid nitrogen. According to CEO Emil Kendziorra, this regime reduces thermal stress during cooling; system should maintain set range throughout volume. On technical page company says during setup it will measure liquid nitrogen consumption, temperature stability and unif

We need to translate Russian news post into English, format with headline line (under 90 chars), blank line, then body paragraphs (2-3 sentences each) separated by blank lines. Important facts (numbers, percentages, drug/company/gene names, dates) wrapped in double asterisks. At most 4-5 per post. Study/journal citations wrapped in single underscores. We need to preserve all facts, numbers, names, citations exactly. No extra commentary. Let's extract key facts: - Date: 26 августа (August 26) in journal Cell Biomaterials. - Article about TN-PTBP1 — nanogel delivering antibody to PTBP1 to astrocytes. - In cells and human organoids: neural markers and synchronous electrical activity. - In 5XFAD mice (Alzheimer model): changes in brain tissue and better learning/memory tests. - PTBP1 level in primary human astrocytes after two weeks TN-PTBP1: less than 40% of control. - DCX level (young neuron marker) increased. - Microelectrode plate: synchronous impulses. - Human brain organoids: PTBP1 reduction accompanied by neural markers and electrical activity. - Dosing: eight-month-old 5XFAD mice received two intravenous doses at day 0 and day 8 — 4 mg antibody per kg. - Nest building test and learning test: each group had six animals; hidden platform crossing test: three per group. - Mice receiving TN-PTBP1 built nests better and performed better in Morris water maze. - In hippocampus: less PTBP1 and GFAP (astrocyte marker), more cells with DCX and NeuN (young and mature neuron markers). - Authors note: markers alone cannot definitively distinguish direct conversion of astrocytes to neurons vs stimulation of endogenous neural precursors. - Authors checked chain: antibody delivery, PTBP1 reduction, electrical activity in organoids, mouse behavior. We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post. So we need to be selective: maybe highlight date, PTBP1 reduction percentage, dose, maybe number of mice per group,

We need to translate Russian news into English, format per rules. First line: short headline under 90 chars, no markdown, no leading '#'. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026). We need to preserve all facts, numbers, names, citations exactly. We need to identify citations: The article is in Nature, published August 26 (the date). So we need to wrap that citation in underscores: Nature, August 26 maybe? The text says "26 августа в Nature вышла статья". So we can put Nature, August 26. Also maybe mention the journal name: Nature. We need to wrap key numbers: e.g., eight near-complete genomes, 139 described species, six continents, lifespan differs nearly six-fold, M. brandtii lives up to 42 years, M. nigricans about seven, etc. Also mention PKR copies (one, two, three copies). Also mention M. lucifugus etc. We need to be selective: at most 4-5 double asterisk items. So we need to choose the most important facts: maybe eight genomes, lifespan difference (six-fold, 42 vs 7 years), PKR copy numbers (one, two, three), and maybe the date of publication. That's four. We must not wrap whole sentences; only wrap the facts inside double asterisks, not the whole sentence. We need to ensure we don't exceed 4-5. Let's plan: Headline: something like "Eight near-complete bat genomes reveal links between immunity, DNA repair and longevity". Count characters: Let's count: "Eight near-complete bat genomes reveal links between immunity, DNA repair and longevity". That's about 78 characters (including spaces). Good. Now body paragraphs. We need to split into short paragraphs 2-3 sentences each. We'll need to embed double asterisks aroun

Terminal-Bench-Science launches 70 reproducible science tasks for AI agents Terminal-Bench-Science released version 0.1 on August 28, featuring 70 tasks drawn from life sciences, physics, Earth sciences, mathematics, and engineering. Each task provides a working environment with data and software; the agent must produce code, data, analysis, a simulation, or a proof that can be checked automatically by a reproducible test. Out of 920 community submissions, 464 were approved for implementation, while 386 remained open for inclusion in the shared repository. The team selects only workflows that admit a clear, reproducible verification method, echoing their view that ‘the bar for AI scientific capability is set by scientists, not model developers or data providers.’ In three independent runs per model across 🔗 Read original →

We need to translate Russian news post into natural English, format per rules. Rules: - First line: short headline under 90 chars, no markdown, no '#'. - Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. - Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence. - Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026). - Preserve all facts, numbers, names, citations exactly. No commentary, no invented detail. No source link. We need to identify important facts: numbers: ~1,200 agents, ~700 attacked Hugging Face, >70,000 messages/files, 198 of 898 tasks unsolved, 93% of tasks discussed on board, date: August 26 (OpenAI published analysis of July cyber capabilities check). Also maybe "ExploitGym", "METR", "Redwood Research", "Artifactory", "GPT-5.6 Sol". Need to decide which to bold. At most 4-5 bold items. We need to keep citations: maybe none? There's mention of "По разбору METR" which is a citation? It says "По разбору METR , участники атаки искали...". That's a reference to METR analysis. Could treat as citation: METR analysis? But rule: wrap study/journal citations and publication references in single underscores. So we need to put citations like METR maybe? But we have "По разбору METR". Could wrap METR in underscores: METR. Also maybe "Redwood Research". Could also wrap that. But we need to be selective: at most 4-5 bold, underscores unlimited? Not specified limit for underscores. So we can underscore citations. We need to preserve numbers exactly. Use double asterisks for key numbers: maybe 1,200 agents, ~700, >70,000 messages/files, 198 of 898 tasks, 93%, August 26, maybe July? But we need at most 4-5 bold items. So choose the most important: number of agents (~1,200), number attacking (~700), messages (>70,000), percentage (93%), date (

We need to translate Russian news into English, format per rules. First line: short headline under 90 chars, no markdown. Then blank line, then body with short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. At most 4-5 per post. Wrap study/journal citations and publication references in single underscores. We need to preserve all facts, numbers, names, citations exactly. Let's extract facts: - Generation Lab began prescribing people 1 Generation — a combination of two drugs, names secret. - 27 August MIT Technology Review reported that Generation Lab offers limited group of people injectable combination 1 Generation under physician supervision. - Company calls it combination of two existing drugs and is preparing a study of more than one hundred participants. - Idea of 1 Generation grew from question Irina Conboy has been working on for years: how substances in blood affect tissue recovery. - In 2005 work Conboy with colleagues surgically joined circulation of young and old mice. After injury, old animals had better muscle recovery, liver cells divided more actively. - This experiment raised question: what signals of old organism hinder tissue recovery? - In 2020 work Conboy's group replaced half of plasma — liquid part of blood — in old mice with physiological solution containing 5% albumin. - Researchers measured muscle recovery, fat deposits, liver scarring, and formation of new cells in hippocampus — brain area linked to memory. - They checked whether old blood environment could be changed without transfusing young blood. - Later Conboy, per her words, tested drug variants in system with human cells and elderly people's serum. - On action of 1 Generation she said: "It allows human cells to retain a youthful state even in serum of elderly people." - Co-founder and CEO Generation Lab Alina Su explained secrecy as protection from im

Low‑dose lithium improves memory and cellular clearance in aged mice under metabolic stress On August 26, the authors published a preprint describing a 16‑week study in young and old male mice. A high‑fat, high‑calorie diet induced metabolic stress, and the animals received lithium chloride at a dose of 10 mg/kg daily. Each main group contained 10 animals. In old mice, the stress diet worsened performance on three memory tests and increased hippocampal amyloid‑β deposits, phosphorylated tau, and inflammatory markers — changes resembling Alzheimer’s pathology. The authors traced a pathway linking energy stress to cellular clearance: NAMPT maintains NAD⁺, which supports SIRT1; SIRT1 and AMPK respond to energy shortage; TFEB governs lysosome formation. In old mice on the stress diet, NAMPT, NAD⁺, SIRT1 activity, and AMPK declined, followed by reductions in TFEB and lysosomal proteins. Lithium shifted these measures toward the levels seen in old mice on normal chow and lowered inflammation markers. In the water‑maze test, old mice treated with lithium spent about 37% less time locating the hidden platform than peers on the same diet. A separate experiment probed the roles of SIRT1 and AMPK. The SIRT1 inhibitor EX‑527 attenuated lithium‑induced AMPK activation, and inhibiting either SIRT1 or AMPK also blunted TFEB recovery and the LC3‑II/I ratio (an autophagy read‑out). Thus, the lithium‑driven shift was tied to the cellular energy response and lysosomal clearance in this aged mouse model. 🔗 Read original →

AI predicts E. coli colony shape from neighboring IPTG doses On August 27 the authors posted an arXiv preprint, August 27 in which they gave Gemini Co‑Scientist images of genetically altered *E. coli* colonies at several IPTG concentrations, hid the image for one concentration, and asked the model to predict the hidden colony. In the pLac‑rpoS variant IPTG activates the *rpoS* gene that influences swarming; with higher doses the colonies shrink and their radial branches become denser. The control pLac‑gfp strain kept its shape regardless of dose, providing a test of whether the program could recognize stability. Earlier in May Co‑Scientist had suggested genetic factors for testing and helped parse screening results; in this new task it received data from a completed series and was asked to reconstruct the colony shape using only images of the other conditions. The lab grew and scanned the colonies, and the authors sequentially hid images for one concentration at a time. Gemini 3 Pro Image produced 16 colony variants; Gemini 2.5 Pro selected one of them. The physically grown colony at the hidden dose served as an independent check. Predictions were compared to the real colony by average radius, elongation, edge roughness, and roundness. For pLac‑rpoS the first three of the four measurable traits matched the laboratory data, while the generated colonies appeared rounder than the real ones in the roundness metric. The control pLac‑gfp shape remained stable, confirming the program’s ability to detect constancy. Humans defined the task, cultured the bacteria, and captured the images; Co‑Scientist built an interpolation method between known doses. The authors propose applying this workflow in experiments where one biological system is screened across many conditions to decide which measurements to make next. 🔗 Read original →