Navitoclax reduces scar size in aged mice after heart attack

In the preprint, Navitoclax reduced scar formation in 15‑month‑old mice following myocardial infarction. The authors tested the drug in middle‑aged male mice after ischemia‑reperfusion: the coronary artery was occluded for one hour, then reperfusion was restored.

Parallel to the mouse work, they re‑analyzed spatial

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AI‑Designed Drug Rentocertib Shows Anti‑Aging Effects in IPF Trial

The drug rentocertib, created by the AI‑driven company Insilico, was originally designed to treat idiopathic pulmonary fibrosis (IPF), a progressive lung‑scarring condition that usually leads to death within a few years. In addition to improving lung volume, the compound was found to lower systemic markers of biological aging across the whole body.

The first AI system mined medical records, blood analyses and scientific publications to pinpoint the

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We need to translate Russian news post into natural English, format per rules.

First line: short headline under 90 chars, no markdown.

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: dates: 8 September, 13 March, 1 September, maybe also mention of Millennium prize? Not a number. Also maybe mention of "OpenAI", "Levent Alpoge", "Tristan Bakmaster", "Sebastien Bubeck". Those are names; not numbers. Key numbers: maybe "8 September", "13 March", "1 September". Also maybe "thousand" (prize of millennium? Actually "премия тысячелетия" is Millennium Prize, $1 million? Not given). So we can bold dates.

We need at most 4-5 double asterisks. We'll choose: 8 September, 13 March, 1 September, maybe also Navier–Stokes? That's not a number. Could also bold Euler equation? Not needed. We'll keep three dates.

We need to wrap study/journal citations in single underscores. There's no explicit journal citation, but there is mention of "политике данных , обновлённой 13 марта" which is a policy update date, not a citation. There's also "OpenAI заявила , что её исследователи и программы-агенты не видели работ математиков ... до публикации". No journal. There's "В объявлении о решении Навье—Стокса OpenAI рассказала, что 1 сентября поручила агентам искать ответы на открытые задачи". Not a citation. There's no explicit study/journal. So maybe we don't need any underscores. But we could still wrap "политике данных" as not a citation. The rule: wrap study/journal citations and publication references in single underscores. If none, we can skip.

We need to ensure we don't wrap whole sentences in double asterisks. Only key facts.

We need to split into short paragraphs 2-3 sentences each.

Let's craft translation.

First, headline: something like "OpenAI says its models may have been improved by anonymized data from two mathematicians". Under 90 chars.

Check length: "OpenAI says its models may have been improved by anonymized data from two mathematicians" count: let's count roughly: OpenAI(5)+space+ says(4)=9, space+its(3)=13, space+models(6)=20, space+may(3)=24, space+have(4)=29, space+been(5)=35, space+improved(9)=45, space+by(2)=48, space+anonymized(11)=60, space+data(4)=65, space+from(4)=70, space+two(3)=74, space+mathematicians(13)=88. Good under 90.

Now blank line.

Now body paragraphs.…

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Higher infant cash payments linked to slower biological aging at age four

On September 8, Nature Human Behaviour published results from the US Baby’s First Years trial. Families with newborns were randomly assigned to receive either $333 or $20 per month. After four years, 735 children provided saliva samples for analysis.

Researchers measured DNA methylation and calculated the DunedinPACE score, which estimates the pace of aging from epigenetic marks. In the high‑payment group, DunedinPACE was lower by 0.17 standard deviation compared to the low‑payment group. No other epigenetic measures differed between the groups.

The trial enrolled a thousand mothers living below the US poverty line in 2018–2019; 400 received $333 and 600 received $20. Randomization ensured the only systematic difference was the assigned payment amount. By isolating income, the study shows that the early‑life environment can alter a DNA‑based aging biomarker in four‑year‑olds.

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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 facts, numbers, names, citations exactly. No added commentary.

We need to identify important facts: dates: 8 September (OpenAI published article), 166 pages, Lean formalization, 10,000 agents, 88 hours search, 17 hours verification, 1934 Jean Leray, 2000 Clay Mathematics Institute, etc. Also maybe "positive viscosity", "smooth external force", etc. But we need to be selective: at most 4-5 double asterisks.

We need to decide which facts are most important: maybe the date of publication, the 166-page length, the 10k agents, 88 hours, 17 hours verification, the 1934 Leray, 2000 Clay Institute. That's many. We need only 4-5.

We can choose: 8 September, 166 pages, ≈10,000 agents, 88 hours, 17 hours maybe that's 5. Or we could include the Clay Institute year 2000 as important. But we need to limit.

We need to wrap each fact individually with double asterisks, not whole sentences. So we can embed them within sentences.

We also need to wrap study/journal citations in single underscores. There's mention of a preprint by Levanta Alpöge and Tristan Buckmaster? Actually "В препринте Левента Альпёге и Тристана Бакмастера сингулярность строится для уравнения Эйлера, где вязкости нет." That's a citation but not a journal. Could treat as preprint by Levanta Alpöge and Tristan Buckmaster maybe. Also maybe we need to cite the article itself? Not needed.

We need to preserve the content exactly, just translate.

Let's draft translation.

Headline: something like "OpenAI claims proof of Navier–Stokes singularity with viscous fluid". Must be under 90 chars.

Let's count: "OpenAI claims proof of Navier–Stokes singularity with viscous fluid" length ~ 55. Good.

Now blank line.

Now body paragraphs.

Paragraph 1: OpenAI published article on September 8 with claimed proof of singularity in Navier–Stokes equations, released full 166-page text and Lean formalization. The Lean language automatically checks reasoning steps.

We need to embed double asterisks for key numbers: maybe September 8, 166 pages. Also maybe Lean is not a number, but it's a name; we might not need to asterisk. We'll keep numbers.

Paragraph 2: According to the claimed theorem, for any positive viscosity the authors prescribe a smooth external force acting in a limited region of space and time, yielding a smooth three‑dimensional flow with finite kinetic energy that becomes unboundedly fast in finite time. The flow starts from rest, forms a vortex around an axis where fluid spirals inward and moves along the axis; the vortex core radius shrinks faster than its height, so speed grows while the volume shrinks enough to keep energy finite.

We might include positive viscosity maybe not needed. Could include finite kinetic energy but that's descriptive.

Paragraph 3: To avoid a non‑smooth remainder at the core boundary, the authors add short‑wavelength velocity oscillations around the core; the background vortex shear amplifies these waves, and their average momentum transport cancels the remainder. Subsequent corrections and spatial localisation keep the external force smooth even as the maximum speed grows without bound.…

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Ovarian stiffness increases with age, linked to lower oocyte yield

A preprint published Preprint, September 8 measured ovarian tissue stiffness in 132 participants from fertility clinics. Researchers used shear‑wave elastography, an ultrasound technique that tracks tissue waves to gauge resistance to deformation. They compared the readings with ultrasound signs of tissue remodeling and with outcomes of IVF cycles.

In the first cohort, 16 women aged 33 or younger had an average stiffness of 13.3 kPa, while 16 women aged 37 or older averaged 18.8 kPa. With age, both the maximum value and the variability of the measurements increased. Mouse experiments from 2020 had suggested that old ovaries are about 2.5‑fold stiffer than young ones, and that collagenase can bring the tissue back toward youthful levels.

The age‑related rise in stiffness remained significant after adjusting for anti‑Müllerian hormone (AMH), a marker of follicular reserve, and for body‑mass index. A second cohort of 100 women aged 24‑45 showed the same three stiffness metrics climbing with age. These findings suggest that the ultrasonic measure reflects intrinsic tissue changes rather than hormonal or metabolic confounders.

To validate the ultrasound signal, researchers examined follicular fluid: the older group had lower levels of C6M, a fragment generated when collagen VI breaks down

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Zebrafish heart repair uses distinct cardiomyocyte layers for growth and regeneration

On September 8, biologists described in eLife a thin layer of cardiomyocytes in zebrafish hearts that helps assemble the muscle wall and coronary vessels during growth.

After injury, other cardiomyocytes rebuild lost tissue; zebrafish hearts can regenerate muscle after ventricular tip removal; surviving cardiomyocytes change state, divide, and replace the lost tissue; the heart consists of several such layers.

Researchers mapped 1,668 cardiomyocytes of adult heart and found a group with active phlda2 gene; these cells lie in a thin layer between outer compact muscle and inner trabeculae.

They created a fish line where phlda2-active cells produce bacterial NTR enzyme and become sensitive to metronidazole; treatment removed about 96.9% of these cells from the ventricle.

In young fish, trabecular muscle area became roughly 54.7% smaller, outer compact layer lost proper organization, and coronary vessels fragmented; after 90 days the vascular pattern remained disrupted.

This layer participates in how the growing heart assembles muscle and vascular network.

In adult fish, ventricular tip was removed six days after treatment start; one week later the wound contained as many gata4-active cells (a marker of dividing cardiomyocytes during repair) as in controls.

By day 30 new muscle covered the injury; vessel density and scar area matched controls; the phlda2‑active layer did not recover even after 60 days, and its cells did not overlap with those activating gata4 during regeneration, indicating that heart growth and adult tissue repair rely on different cardiomyocyte populations.

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BioPharma APAC Maps Four Longevity Funding Channels in Asia‑Pacific

On September 9, BioPharma APAC released a map of four channels of longevity financing in the Asia‑Pacific region. The map separates funds that pay for therapy development, diagnostics, clinic services, and scientific projects linked to the region but financed or incorporated outside it. Venture‑tracker records show when an investor receives equity after a deal.

Payments from pharmaceutical companies for joint development, research grants, and clinic revenue from examinations go to different recipients and support distinct work, which the author keeps separate. Therapeutic programs in the map draw venture money, public capital, grants, and pharma partnership payments. Biological‑age diagnostics — tests that assess age‑related changes in the body — form a separate line.

Two additional lines cover clinics and services funded

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Radical Numerics launches Omnii for personalized mRNA cancer vaccine target selection

On September 8, Radical Numerics unveiled Omnii, a post‑trained model designed to pick targets for personalized mRNA cancer vaccines. The system takes a tumor’s mutational profile and the patient’s MHC variants, then selects vaccine targets and builds an mRNA cassette sequence.

Omnii first predicts whether a mutant peptide will be presented by the patient’s MHC class I molecules on the tumor cell surface. It then estimates the peptide’s immunogenicity, i.e., whether a T‑cell will recognize the presented fragment and mount a response. Only peptides that pass both presentation and immunogenicity checks can be included in the vaccine cassette.

In head‑to‑head comparisons, Omnii achieved an AUROC 0.939 for MHC class I presentation, outperforming BigMHC‑EL’s 0.933. For immunogenicity prediction, the model scored AUROC 0.750, compared with 0.558 for BigMHC‑IM.

In a simulation where 100 candidate peptides were evaluated, only 6% were truly immunogenic and the cassette had five slots. Omnii selected an average of 1.3 immunogenic targets per cassette, while BigMHC‑IM managed just 0.7.

After target selection, Omnii outputs the mRNA sequence encoding the chosen neoantigens. The model is currently available in research preview, and Radical Numerics is seeking partners in cancer immunology and vaccine development.

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IRIS RNA model predicts developmental signals to improve stem cell differentiation

The IRIS model uses RNA profiles to infer which developmental signals acted on a cell. It was described in Nature Methods, September 8 after being trained on human stem cells exposed to known combinations of six developmental signals and then tested on single‑cell data from mouse embryos.

An RNA profile records which genes are currently active, giving a snapshot of the cell’s state. A benchmark of 400 models on cell atlases showed that merely expanding collections of RNA profiles quickly ceases to improve predictive power.

IRIS first learns from cells with known signal combinations, then searches the RNA profile for the trace of a signaling pathway—the chain through which a cell responds to an external cue. Researchers cultured human embryonic stem cells, applied defined signal mixes at different stages, and measured single‑cell RNA after each condition to teach IRIS to recognize these traces.

When applied to mouse embryo data, IRIS recovered the expected order of signal combinations in lineages that give rise to the foregut and heart muscle. It then predicted heightened activity of WNT and BMP in the prospective respiratory mesenchyme surrounding the lung bud.

In mouse foregut culture, activating WNT expanded the domain where the respiratory mesenchymal markers Tbx4 and Foxf1 are expressed, while a WNT inhibitor erased Tbx4 expression. Guided by this finding, the authors added WNT stimulation on the fourth day of differentiation of human stem cells and maintained it, which raised TBX4 levels and kept intermediate WNT activity to preserve FOXF1.

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OmniSyn builds 2.7‑billion‑entry virtual library for human protein drug design

A preprint describing OmniSyn was posted on bioRxiv, September 6. The computational model takes a protein’s amino‑acid sequence and proposes a candidate molecule together with a step‑by‑step synthesis plan that uses purchasable chemical fragments and reaction types. Unlike many structure‑based methods, OmniSyn relies solely on the sequence, selecting from over 220 000 fragments and 115 common reaction types during training.

The model was tested on 35 proteins not seen during training. For these targets OmniSyn identified exact matches to known active molecules in 0.255% of cases, compared with 0.028% for the next‑best method. When the candidates were analogues of known molecules, the retrosynthesis tool AiZynthFinder found a viable route for 71.92% of them.

For 21 306 human protein sequences OmniSyn generated 2.7 billion target‑molecule entries, each containing a synthetic route and scoring metrics for candidate selection. In a test on 200 proteins, the interaction score distinguished the true target from random proteins (7.64 vs 4.47) and the docking score showed better binding (−6.88 vs −6.20 kcal mol⁻¹).

The library stores, for every protein sequence, a virtual candidate, its synthesis plan

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Lipid nanoparticle with cerium oxide, CasRx plasmid, and D4F peptide reduces kidney injury

The authors described a lipid nanoparticle on 6 September that contains three parts: cerium oxide to scavenge reactive oxygen species, a plasmid expressing the CasRx protein guided by a short RNA to target STING transcripts, and the peptide D4F that directs the particle to kidney macrophages. In kidney organoids and a mouse model of transient blood‑flow occlusion, this construct lowered macrophage inflammation, tubular damage, and markers of kidney dysfunction.

Cerium oxide bound excess ROS, the plasmid drove CasRx production that cleaved STING RNA, and D4F facilitated particle accumulation in kidneys and binding to macrophages. In activated mouse macrophages the full construct decreased the proportion of cells showing a fluorescent ROS signal from 71.9% to 24.5%; a second guide RNA gave a similar reduction, while mutating the STING target site partially restored signal levels, confirming the effect depended on CasRx‑STING targeting.

Medium taken from macrophages treated with the nanoparticle reduced death, ROS signaling, and inflammatory gene activity in nearby tubular cells, even though the particles themselves did not reach the tubules, indicating that altered macrophage state can be transmitted via secreted factors. In kidney organoids, D4F increased the fraction of particles‑labeled cells from 9.65% to 27.5%.

In the mouse ischemia‑reperfusion model, two intravenous doses were given two hours before vessel occlusion and 30 minutes after blood flow returned; kidneys were evaluated after one day. Animals receiving the full construct showed the lowest tubular injury, creatinine, and blood urea nitrogen among all groups, with gene‑protein analysis revealing suppressed inflammatory pathways and enhanced oxidative‑stress defense and tissue‑repair programs.

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