We need to translate Russian news post to English, format: first line headline under 90 chars, no markdown prefix. 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). Must preserve facts, numbers, names, citations exactly. No commentary, no invented detail. No source link.

We need to extract facts: date: 29 August (presumably 2024? Not given year. The text: "29 августа в Advanced Science вышла работа о мышиных кишечных органоидах". So date: August 29 (year not given). Could be 2024? Not specified. We'll keep as "August 29". Might need year? The original Russian didn't include year. We'll just keep "August 29". Might be ambiguous but we preserve exactly as given: "29 августа". In English: "August 29". We'll not add year.

Important facts to bold: numbers: 184 organoids, ~70% formed daughter crypts, ~85% of successful divisions had short/long daughter length ratio >=0.7, channel widths 50 and 70 micrometers, gene Myh9, myosin IIA, maybe also "light‑softening gel". We need at most 4-5 bolded facts. Choose: 184 organoids, ~70%, ~85%, 50 and 70 micrometers, Myh9 gene. That's 5.

Citations: need to wrap study/journal citation and publication references in single underscores. The work published in Advanced Science on August 29. So we need "Advanced Science, August 29". Also maybe mention "2025 year work"? The text: "В работе 2025 года часть нынешней команды уже пользовалась светочувствительным гелем...". That is a citation to a 2025 work. We need to wrap that as well: maybe "2025 work" but better: "some 2025 study" but we need to keep exact reference: "В работе 2025 года". So we can put "2025 work" but we need to preserve the fact that it's a 2025 work. Could format as "2025 study". However we need to wrap study/journal citations and publication references in single underscores. So we can put "2025 work" as a reference. Also the current work is in Advanced Science, we already have that.

We must not bold whole sentences. So we will embed bold within sentences.

Now produce headline: maybe "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters: Let's count: "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters including spaces: Let's approximate: Light‑softening (16 inc hyphen?), plus space =17, gel (3) =>20, space=21, lets(4)=>25, space=26, researchers(10)=>36, space=37, control(7)=>44, space=45, crypt(5)=>50, space=51, division(8)=>59, space=60, in(2)=>62, space=63, mouse(5)=>68, space=69, intestinal(10)=>79, space=80, organoids(8)=>88. So 88 characters, under 90. Good.

Now body paragraphs. Need short paragraphs 2-3 sentences each, separated by blank lines.

We'll write maybe 4-5 paragraphs.

Paragraph 1: Introduce the study: On August 29, a paper in Advanced Science described how a light‑softening hydrogel enabled researchers to shape and split intestinal crypts in 3D mouse organoids. They used a laser to locally soften the gel, creating channels that guided crypt growth and division.

Paragraph 2: Details of the method: First, a laser made a single channel for one crypt; after three days, two V‑shaped channels were formed at its base. Tissue grew into the softened zones, letting scientists set the time and place of division and watch one crypt produce two daughters.…

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We need to translate Russian news into English, format with headline line under 90 chars, then blank line, then body paragraphs (2-3 sentences each) separated by blank lines. Wrap 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 identify key facts: FATE DNA editor, described August 31 in Nature Communications. Gene LMNA, progerin, farnesyl. Numbers: 322 of 323 progerin-positive muscle cells, 8.55% editing efficiency. Possibly also mention date August 31. Also maybe mention Hutchinson-Gilford progeria syndrome. Also mention FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles.

We need to select at most 4-5 double asterisk wrapped facts. Choose: August 31, Nature Communications, 322 of 323, 8.55%, maybe FATE? But gene names also need double asterisks? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So gene names like LMNA, progerin, farnesyl, FATE could be considered important. But we need to be selective, at most 4-5 total. So we need to decide which to highlight. Probably include: August 31, Nature Communications, 322 of 323, 8.55%, and maybe LMNA or FATE. But we need to keep within 4-5. Let's pick: August 31, Nature Communications, 322 of 323, 8.55%, LMNA. That's 5.

Now citations: study/journal citations and publication references in single underscores. So we need to wrap the journal name and date: Nature Communications, August 31. Or maybe Nature Communications and date separate? The rule: wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026). So we should put the whole citation: Nature Communications, August 31. That satisfies.

Now we need to produce headline under 90 chars, no markdown, no prefix '#'. Something like: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". Count characters: Let's count: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". That's 66? Let's count: D(1)N2A3 space4 e5d6i7t8o9r10 space11F12A13T14E15 space16d17e18t19a20c21h22e23s24 space25p26r27o28g29e30r31i32n33 space34f35r36o37m38 space39n40u41c42l43e44a45r46 space47e48n49v50e51l52o53p54e55 space56i57n58 space59h60u61m62a63n64 space65o66r67g68a69n70o71i72d73s74. So 74 chars, okay.

Now body: need short paragraphs 2-3 sentences each, separated by blank lines.

We need to preserve all facts, numbers, names, citations exactly. Do not add commentary.

Let's draft paragraphs.

Paragraph 1: Introduce FATE editor, described August 31 in Nature Communications, edits LMNA gene to prevent farnesylation of progerin.

Paragraph 2: Describe effect in neuromuscular organoids: progerin loses lipid tag, distributes in nucleus, DNA damage response markers γH2AX and 53BP1 form foci after irradiation; in muscle cells previously lacking foci, after editing they appear.

Paragraph 3: Provide specifics: In 322 of 323 progerin-positive muscle cells, no γH2AX foci before editing; 53BP1 retained at nuclear envelope. After FATE editing, foci reappear. Also mention that editing replaces one nucleotide in LMNA tail, preventing farnesyl attachment.

Paragraph 4: Mention that FATE also works in organoids with non-classic LMNA variant, freeing 53BP1 and restoring foci. Also that authors packaged FATE mRNA into lipid nanoparticles, microinjected into muscle part of organoids; after ten days editing efficiency up to 8.55%, progerin redistributed, DNA damage foci formed after irradiation.…

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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.

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 key facts: date 31 августа (August 31) in Nature Communications described FATE. Gene LMNA, progerin, farnesyl, etc. Numbers: 322 of 323 progerin-positive muscle cells lacked γH2AX foci; 53BP1 retained at nuclear envelope. Also editing efficiency max 8.55% after ten days. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS). Also mention that FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles delivering mRNA.

We need to select at most 4-5 double-asterisk facts. Choose: date (August 31), journal Nature Communications, gene LMNA, progerin, farnesyl, numbers 322/323, editing efficiency 8.55%, maybe Hutchinson-Gilford progeria syndrome. But we need to be selective: at most 4-5. Let's pick: August 31, Nature Communications, LMNA, progerin, farnesyl, 322 of 323, 8.55%. That's many. Need max 4-5. Let's choose: August 31, Nature Communications, LMNA, progerin, 8.55% maybe also 322 of 323. That's 6. Need reduce.

We can combine some: maybe we can put date and journal together? But each double asterisk wraps a fact; we could put "August 31 in Nature Communications" but the citation should be underscores, not double asterisks. So date could be double asterisk, journal underscores. So date August 31 is one. Journal Nature Communications is underscores (not counted). Then we need gene name LMNA maybe double asterisk? Could be considered important. Progerin maybe double asterisk? Farnesyl maybe double asterisk? Number 322 of 323 maybe double asterisk. Editing efficiency 8.55% maybe double asterisk. That's many.

We need at most 4-5 double asterisk facts. Let's decide which are most important: date, journal (underscores), LMNA gene, progerin, farnesyl, editing efficiency, 322/323. Perhaps we can combine LMNA and progerin into one fact? Not really.

We could choose: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5. Date and journal can be in underscores? Date cannot be underscores because it's not a citation. But we could put date in double asterisk as well, but then exceed limit. Maybe we can omit date double asterisk and just mention date in plain text? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So dates should be double asterisk if we consider them important. But we can maybe not treat date as key fact? It's somewhat important but maybe we can skip double asterisk for date and just mention normally. The rule says wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. It implies we should wrap those categories, but we can be selective: at most 4-5 per post. So we need to choose which dates, numbers, etc to wrap. We can omit date wrapping if we stay within limit.

Thus we can wrap: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5.

Now we need to wrap study/journal citations in single underscores: Nature Communications. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS) but not wrap.…

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CAR-T cells may show second receptor before first recognizes target

On August 31 the authors posted a preprint describing a two‑step CAR‑T strategy in which the synthetic SynNotch receptor detects high HER2 density and then activates a CAR that kills the target. In some T cells the CAR appeared on the surface before the SynNotch signal arrived.

In three different constructs the basal CAR surface level was measured at 6%, 2%, and 0.2% of resting T cells. The construct with the highest background (6%) showed the poorest ability to discriminate between high‑ and low‑HER2 cells.

In a two‑tumor mouse model the high‑background construct reduced both high‑ and low‑HER2 tumors. After binding HER2, CAR triggers T‑cell proliferation, so even a small initial CAR‑positive fraction can shape the whole population. The authors’ mathematical model incorporated the fraction of such cells, HER2 density, and the T‑cell‑to‑target ratio; experiments indicated that optimal discrimination required different T‑cell doses for each construct.

To lower basal CAR expression they attached the mScarlet tag to the CAR C‑terminus, which cut the background by about 60% while preserving signal‑induced CAR. Degron tags reduced background further in culture, but T cells bearing degrons lost cytotoxic activity more quickly after CAR activation. In mouse models, mScarlet improved discrimination, suppressing high‑antigen tumors while low‑antigen tumors continued to grow.

For the two‑step CAR‑T design, both the timing of CAR expression and its residual level after proper signaling are crucial. Rohelo Hernandez‑Lopez et al., 2021

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CAR-T cells trigger macrophage iNOS, limiting lymphoma attack

In experiments on mice, CAR‑T cells were found to induce macrophages to express the enzyme iNOS, which weakens the attack on lymphoma. On 31 August the peer‑reviewed version of an article on large B‑cell lymphoma described how CAR‑T cells activate iNOS in macrophages, producing nitric oxide. The researchers studied axi‑cel, a CAR‑T therapy in which patient T cells receive a receptor that recognizes the CD19 protein on lymphoma cells.

In a 2021 study the same group linked short‑lived responses to axi‑cel with tumor interferon signals and suppressive myeloid cells. Pretreatment biopsies showed that patients whose response did not persist had a higher relative proportion of M2‑like macrophages — immune cells capable of dampening the immune response — as revealed by RNA analysis. This association pointed to where the mechanism might lie.

In a laboratory model the authors combined mouse CAR‑T cells, B‑cell tumor cells, and M2‑like macrophages. After recognizing tumor cells, CAR‑T released IFN‑γ, a signaling protein of the immune system. IFN‑γ turned on iNOS in the macrophages; the enzyme produced nitric oxide, which slowed CAR‑T cell proliferation and weakened their ability to kill tumor cells.

The authors verified this pathway in two ways. The iNOS blocker L‑NIL and genetic deletion of the NOS2 gene in macrophages restored CAR‑T cells’ capacity to proliferate and kill tumor cells. Conversely, a nitric‑oxide‑donating compound suppressed their activity again.

In a mouse model of B‑cell lymphoma, the combination of CAR‑T and L‑NIL extended survival compared with CAR‑T alone. During leukapheresis — the procedure that isolates cells from blood to manufacture the therapy — patients whose response did not last had a higher fraction of CD14‑positive monocytes expressing iNOS. The authors view iNOS as a possible target for future clinical studies of CAR‑T.

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NPR estimates US heat-related deaths about five times higher than official count

NPR, together with Boston University’s Center for Climate and Health, calculated that heat was linked to roughly about 9,000 deaths per year in the United States from 2018–2025. In contrast, death certificates recorded heat as a contributing factor in only about 1,700 cases annually over the same period.

The researchers used weekly mortality data from 530 counties that together represent ~73% of the US population, excluding 2020 due to the pandemic. They matched each week’s temperature—and the following week’s—to death counts, comparing local temperatures to each area’s normal climate to find the mortality‑minimum temperature for each state.

By aggregating county‑level estimates and scaling them to the continental United States, the model produced a national figure of approximately nine thousand heat‑associated deaths per year. In Maricopa County, where Phoenix is located, investigators already ask about air‑conditioning, electricity bills and medications that increase heat sensitivity when probing deaths.

Because different counting methods yield vastly different numbers, the way heat‑related mortality is measured directly influences which risks are prioritized for prevention efforts.

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Breathing 3D Model of Artificial Lung Mimics Soap‑Bubble Physics

The problem with existing lung‑on‑a‑chip devices was that cells were cultured on flat surfaces of synthetic PDMS, which does not mimic real tissue, and attempts to make membranes from soft hydrogels caused them to rupture instantly when stretched.

Korean researchers solved this by immersing a special mold in a composite hydrogel solution and drawing out an ultrathin, yet elastic and strong film—just as a soap bubble is blown through a straw.

Using 3D‑bioprinting, they rebuilt the three‑layer alveolar structure by sequentially depositing a vascular cell layer, a basement membrane, and an epithelial cell layer. To make the construct expand and contract like a real inhale and exhale, the team built a pneumatic drive system that cyclically changes pressure beneath the ultrathin hydrogel membrane, imitating the movement of the human diaphragm.

In testing, the artificial lung demonstrated high reliability, operating stably for about 240,000 breath cycles. Experiments with the influenza virus revealed that cellular inflammatory and antiviral responses change dramatically depending on whether the tissue is in motion or remains still. Because the system lets researchers freely set breath depth and frequency, they can now recreate both healthy lung conditions and various pathological processes in the lab, paving the way for more precise drug testing for viral and lung diseases without relying on animal tests or simple flat cell cultures.

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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 need to preserve all facts, numbers, names, citations exactly. No added commentary.

We need to identify important facts: Malva, RNA sequence search, data of 74 million single cells, August 26, journal Nature published article about Malva, index of raw RNA reads from single cells. It links short sequence fragments with barcode — label of specific cell, so query finds cells with needed mutation, RNA junction, pathogen trace and shows their tissue, age, disease, original study. Single-cell sequencing reads RNA of each cell separately. Usually from these reads they make a table: how many RNA of known genes per cell. Such table helps compare cells, but question about specific mutation, viral RNA or novel RNA junction requires returning to huge array of raw reads. Malva stores these reads in an index. It splits each read into fragments of 24 nucleotides — letters of genetic sequence — and records cell barcode next to it. Query by sequence returns cells where its fragments met, together with info about them. Indexes of individual samples can be combined, therefore database is supplemented with new data. In version described in article, index covered about 74 million cells from thousands of experiments. Search for a transcript — RNA copy of a gene — length 1000 nucleotides took 0.9 seconds on a single CPU core. Search result — pseudocounter: number of matched reads instead of RNA molecule quantity. Authors compared such pseudocounters with ordinary RNA counts, then looked for sequence variants, RNA junctions, transcript ends. In a small lung cancer sample Malva found mutations in EGFR gene; separate check covered 280 samples of 16 tumor types. These tests show that fast search finds biological signals in cellular data. Researcher can search for sequence linked to hypothesis in already accumulated public data and see in which tissues, ages and disease states it occurs.…

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

We need to identify key facts: Malva opened search by RNA sequences in data of 74 million single cells. On August 26, journal Nature published article about Malva, an index of raw RNA reads from single cells. It links short sequence fragments to barcode — label of specific cell, so query finds cells with needed mutation, RNA junction, or pathogen trace and shows their tissue, age, disease, original study. Single-cell sequencing reads RNA of each cell separately. Usually from these reads they make a table: how much RNA of known genes per cell. Such table helps compare cells, but question about specific mutation, viral RNA, or novel RNA junction requires returning to huge array of raw reads. Malva stores these reads in an index. It divides each read into fragments of 24 nucleotides — letters of genetic sequence — and records cell barcode next to it. Query by sequence returns cells where its fragments met, together with info about them. Indexes of individual samples can be combined, so database is supplemented with new data. In version described in article, index covered about 74 million cells from thousands of experiments. Search for a transcript — RNA copy of a gene — length 1000 nucleotides took 0.9 seconds on a single CPU core. Search result — pseudocount: number of matched reads instead of RNA molecule count. Authors matched such pseudocounts with regular RNA counts, then looked for sequence variants, RNA junctions, transcript ends. In a small lung cancer sample Malva found mutations in EGFR gene; separate check covered 280 samples of 16 tumor types. These tests show that fast search finds biological signals in cellular data. Researcher can search for sequence linked to hypothesis in already accumulated public data and see in which tissues, ages, disease states it occurs.

We need to wrap key numbers: 74 million cells, August 26, Nature, 24 nucleotides, 1000 nucleotides, 0.9 seconds, EGFR, 280 samples, 16 tumor types. Also maybe thousands of experiments. But we need at most 4-5 double asterisks. So choose most important: 74 million cells, August 26, Nature, 0.9 seconds, EGFR, 280 samples, 16 tumor types. That's 6. Need max 4-5. So we need to be selective: maybe 74 million cells, August 26, Nature, 0.9 seconds, EGFR. That's 5. Or we could include 280 samples and 16 tumor types as part of same fact? Could combine: "280 samples of 16 tumor types" as one fact. That would be: 74 million cells, August 26, Nature, 0.9 seconds, EGFR, 280 samples of 16 tumor types. That's 6 again. Could combine EGFR and sample count? Not ideal. Perhaps we can limit to: 74 million cells, August 26, Nature, 0.9 seconds, EGFR. That's 5. The sample count maybe less crucial. Or we could include 24 nucleotides fragment length as important? Might be less. Let's decide: key numbers: 74 million cells, August 26, Nature, 0.9 seconds, EGFR. That's 5.

Wrap each in double asterisks: 74 million cells, August 26, Nature, 0.9 seconds, EGFR.

Now citations: The article is in Nature journal. We need to wrap study/journal citations and publication references in single underscores. So we need something like Nature maybe with date. The Russian says: "26 августа журнал Nature опубликовал статью о Malva". So we can put Nature, August 26 (or Nature with date). Use single underscores.…

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New GPT‑5.6 manuscript improves prime gap construction

A 48-page manuscript submitted on August 25 by GPT 5.6 Sol outlines a new way to create long gaps between consecutive primes. On August 26 the user DottedCalculator filed a related partial proof, naming the model GPT 5.6 Pro. Both sources describe a novel twist on the classic problem of large prime gaps.

The construction uses divisibility: for each small prime a residue is chosen so that numbers with that residue are divisible by the prime. The Chinese remainder theorem merges all conditions into a single shift, yielding an interval where every number is composite. Covering more numbers this way produces a longer guaranteed gap.

The manuscript changes the start of this construction. For a subset of primes it prefers the zero residue—meaning the number is divisible by that prime—so those composites are covered already at the first step. For larger primes it selects residues that capture many of the remaining composites, each residue covering a whole group. The remaining primes are handled by another part of the scheme from Ford, Green, Konyagin, Maynard, Tao 2018, where May

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Why Libet Got the Credit for Grey Walter’s Earlier Free‑Will Experiment

In Benjamin Libet’s classic study, participants wore an EEG cap, faced an oscilloscope with a rotating dot, and were asked to flex a finger or wrist whenever they felt the urge. They had to remember the dot’s position at the moment they consciously decided to act (moment W), while sensors recorded muscle activity (moment M) and cortical brain activity. The results showed that the unconscious readiness potential—a neural surge—appeared 350-500 milliseconds before the subject’s conscious intention, fueling the idea that the brain decides before we do.

Twenty years earlier, in 1963, cybernetics pioneer William Grey Walter performed a similar test with epilepsy patients who already had invasive electrode matrices implanted in their motor cortex for clinical treatment. He wired those electrodes to a slide projector and gave subjects a dummy button that did nothing; the projector changed slides in response to the amplified brain signal a few milliseconds before the finger could physically press the button. Thus the machine seemed to read their intentions and switch the image as they were about to act, preceding the actual movement.

🔗 Source: @solid_state_humanity

We need to translate Russian text into natural English, format: 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. Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026). There are no explicit citations in the text, but there is mention of a podcast episode on August 20, and maybe mention of a drug tirzepatide? Actually it's "тираголиумаб" which is "tirgolumab"? Wait: "тираголиумаб — антитело, блокирующее белок TIGIT на иммунных клетках." That's "tirgolumab" (maybe "tirgolumab"? Actually the drug is "tiragolumab"? Let's check: There's a drug "tiragolumab" (anti-TIGIT). Yes, tiragolumab is an anti-TIGIT antibody. So we should keep that name. Also "атезолизумаб" is atezolizumab. So we need to wrap drug names in double asterisks? The rule: wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. So we should bold drug names: tiragolumab, atezolizumab. Also maybe TIGIT as a gene/protein name. Also maybe non-small cell lung cancer? That's a disease name, maybe not required but could be considered important fact. Also dates: August 20 (20 августа). So we should wrap August 20 in double asterisks. Also maybe mention of "пяти- или десятилетней давности" (five- or ten-year-old knowledge). Could wrap five- or ten-year? But that's not a number? It's a time frame. Could be considered important. But we need at most 4-5 double asterisks. Let's pick: August 20, tiragolumab, atezolizumab, TIGIT, maybe non-small cell lung cancer (NSCLC). That's 5. Or we could include next-generation sequencing as a key method. But we need to be selective.

Also we need to wrap study/journal citations in single underscores. There's no explicit citation like a journal, but maybe we can treat the podcast as a citation? Not needed. The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." If none, we just don't use any.

We need to preserve all facts, numbers, names, citations exactly. Do not add commentary.

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

Let's craft translation.

First line headline: something like "Evaluating AI in Biology by New Signals It Enables". Must be under 90 chars. Let's count: "Evaluating AI in Biology by the New Biological Signals It Helps Obtain" That's maybe 78. Good.

Now body.

We need to translate the Russian paragraph.

Let's translate:…

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Sparks Brain Preservation Announces Brain Preservation Summit in Salem, Oregon

Sparks Brain Preservation announced the Brain Preservation Summit to be held 19–20 September in Salem, Oregon. The event will bring together researchers working on connectome preservation, tissue fixation, cryobiology, electron microscopy, and long‑term storage.

The program features fifteen‑minute talks followed by five minutes for questions, tours of the Sparks brain‑sample repository, and a partial list of 18 confirmed speakers. Organizers aim to strengthen both the Sparks community and the broader group seriously working on this challenge, and to make the current state of science more transparent to interested parties.

Sparks links long‑term memory and personality to the fine physical structure of the brain; its approach uses aldehyde fixation to stabilize tissue, cryobiology for low‑temperature handling, and electron microscopy to visualize synapses—the points of neuronal contact—on small fragments. These topics are tied to connectome preservation and long‑term tissue storage.

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We need to translate Russian news post into natural fluent 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.

We need to 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 added commentary.

We need to identify citations: The article published August 25 (year? Not given, but likely 2024? Actually they mention "В статье, опубликованной 25 августа". No year given. Could be 2024? But we must preserve exactly as given: "25 августа". No year. So we keep that date.

Also mention "В работе 2025 года липиды оказались..." So we have year 2025.

Also mention "В статье, опубликованной 25 августа". Could be 2024? But we just keep "25 августа". No year.

We need to wrap key numbers, percentages, drug names, etc. Let's identify important facts:

- Blood only partially reflected metabolism of five organs in mice receiving life-extending interventions.
- Plasma compared with liver, kidneys, gastrocnemius muscle, and two adipose tissues in genetically heterogeneous UM-HET3 mice.
- From four to twelve months animals received caloric restriction or one of four drugs: rapamycin, canagliflozin, 17-alpha-estradiol, acarbose; these regimens previously extended mouse lifespan.
- Similarity between plasma and tissues varied with organ, regimen, sex.
- Blood can be taken repeatedly from live animal, so its composition used to infer organ metabolism.
- Plasma mixes substances from food, organs, gut microbes.
- Determining what happened in one organ from such analysis requires direct verification.
- In human studies, NAD+ level in whole blood changed after nicotinamide riboside, but hardly responded to age, exercise, nutrition.
- Authors of new article checked which tissue changes in life-extending regimens are simultaneously visible in blood.
- In 2025 work, lipids were the most noticeably changing class of substances in six sample types.
- New article clarifies which of these shifts can be linked to specific tissue via plasma.
- Each tissue had 33–37% of annotated metabolites also found in plasma.
- Metabolites are small molecules participating in metabolism.
- Set of common molecules for each tissue was its own, so plasma composed of partial overlaps with several organs.
- After statistical check filtering random differences, authors highlighted 260 compounds that changed both in plasma and at least one tissue under one regimen.
- Ergothioneine, a food compound, changed in same direction in blood, muscle, liver, kidney, subcutaneous fat under several regimens.
- Lipids with docosahexaenoic acid (DHA) — omega-3 fatty acid — changed concordantly in blood and two adipose tissues.
- Another candidate, 1,5-anhydroglucitol, changed in same direction in blood, liver, kidney under regimens with strong lifespan extension.
- In males authors found more groups of related molecules with concordant response than in females.
- For this mouse model a useful blood marker requires linking to specific tissue, regimen, and sex.
- Repeated blood analyses become more informative when such linkage is first established by organ measurements.…

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QuantHealth preprint predicted VESALIUS-CV hazard ratio close to actual trial result

On 31 August 2025, QuantHealth released a preprint describing a computer simulation of the VESALIUS‑CV trial. The simulation, posted to the OSF archive since 11 August 2025, estimated a hazard ratio of 0.78 for the primary composite endpoint.

On 25 June 2026, ClinicalTrials.gov published the trial’s observed hazard ratio of 0.75. VESALIUS‑CV evaluated evolocumab, an LDL‑lowering antibody, with a median follow‑up of 55.2 months; the first major event occurred in 336 of 6 129 evolocumab‑treated participants versus 443 of 6 128 placebo recipients.

The OSF analysis defined the endpoint as cardiovascular death, myocardial infarction, or ischemic stroke, whereas the trial registry listed the first of coronary death, myocardial infarction, or ischemic stroke. These two overlapping composite measures correspond to the predicted 0.78 and observed 0.75 hazard ratios.

QuantHealth’s model was trained on de‑identified insurance claims and electronic health records, mapping the drug to associated proteins, diseases, and biological pathways. Using protocol criteria, it generated a virtual control group from existing records and estimated time‑to‑event under evolocumab and placebo scenarios. Prior to VESALIUS‑CV, the approach was validated against 22 completed cardiovascular trials, achieving an F1 score of 0.83 in 24 of those comparisons.

All authors were QuantHealth employees during the study, and the work was funded by QuantHealth.

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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 need to preserve all facts, numbers, names, citations exactly.

We need to identify key facts: three deaths, Novartis halted eight rap-cel studies, date 24 August, IEC-HS severe immune reaction, two oncology rap-cel studies continue, 2022 study with five lupus patients, remission after three months, naive phenotype B cells, after that CAR-T tested in more autoimmune diseases: by May in registry counted 280 such trials, rap-cel one of these programs. Reuters data: Novartis halted eight studies at lupus, rheumatoid arthritis, vasculitis, multiple sclerosis, myasthenia gravis and other diseases. Company reviewing events with independent safety boards, looking for ways to detect dangerous side effects earlier. Same days Bristol Myers Squibb temporarily halted enrollment in autoimmune studies of another CD19 CAR-T therapy, zola-cel. At routine safety check company saw temporary reversible inflammatory reactions and decided to review clinical data, per BioPharma Dive.

We need to wrap key numbers, percentages, drug/company/gene names, dates in . At most 4-5 per post.

Potential important facts: three deaths, eight studies halted, date 24 August, IEC-HS, two oncology studies continue, five lupus patients, three months remission, 280 trials by May, eight studies halted (again), maybe we need to limit to 4-5.

We need to be selective: maybe choose: three deaths, eight studies halted, 24 August, IEC-HS, two oncology studies continue, five lupus patients, three months, 280 trials by May. That's many. Need max 4-5. Let's pick: three deaths, eight studies halted, 24 August, IEC-HS, two oncology studies continue. That's 5.

We also need to wrap drug/company/gene names? Those are also important facts but we can maybe not wrap them if we already used quota. But rule says wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post, never a whole sentence. So we need to choose which to wrap. Could include drug names like rap-cel, zola-cel, CD19. But we have limited slots. Perhaps we can wrap numbers and dates, and maybe one drug name. Let's decide: wrap three deaths, eight studies halted, 24 August, IEC-HS, two oncology studies continue. That covers numbers and a condition. Good.

Now citations: study/journal citations and publication references in single underscores _. We have Reuters, BioPharma Dive. Those are publication references. So wrap Reuters and BioPharma Dive. Also maybe mention "study 2022" but that's not a journal citation. Could also wrap Nature Aging etc but not present. So we will wrap Reuters and BioPharma Dive.

We must not add any extra commentary.

Now format: headline line, blank line, then paragraphs.

We need short headline under 90 chars. Something like: "Novartis halts eight rap-cel CAR-T trials after three patient deaths". Count characters: Let's count: "Novartis halts eight rap-cel CAR-T trials after three patient deaths". That's 66 approx. Good.

Now body paragraphs: each 2-3 sentences.

We need to split into short paragraphs separated by blank lines.

Let's craft paragraphs:…

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We need to translate Russian news about macrophage CD38 NAD+ depletion in placenta of aged mice, humans, pigs. Need to produce headline under 90 chars, then blank line, then body paragraphs 2-3 sentences each, separated by blank lines. Must wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post. Must wrap study/journal citations and publication references in single underscores (e.g., Nature Aging, July 2026). Here citation: article published August 27 in Nature Communications. So we need Nature Communications, August 27? Actually format: Nature Communications, August 27 (year? Not given). We'll include year maybe 2024? Not given. We'll just put Nature Communications, August 27.

We need to select up to 4-5 double-asterisk facts. Important facts: NAD+ levels lower in older women (35-44 vs 20-25). Percentages: stromal cells decreased from 44.8% to 23.9%; macrophages increased from 7.7% to 17.1%. CD38 upregulation. Fetal weight increased from 0.887 to 0.961 gram. IUGR decreased from 24.4% to 8.9%. Also maybe mention eight-month-old mice. Also mention nicotinamide riboside and inhibitor 78c. But we must limit to 4-5. Choose: NAD+ levels lower in older women (maybe not a number but statement). Better to include numbers: stromal cells 44.8%→23.9%; macrophages 7.7%→17.1%; fetal weight 0.887→0.961 g; IUGR 24.4%→8.9%; CD38 upregulation (maybe not a number). That's 4 numeric facts. Could also include eight-month-old mice but that's less critical. We'll stick to those four.

We need to wrap each in double asterisks. Eg 44.8%, 23.9%, etc. Also percentages and numbers.

We must not wrap whole sentences, just the facts.

We need to ensure we don't exceed 4-5 double asterisks. We'll have maybe 5: stromal % decrease, macrophage % increase, fetal weight increase, IUGR decrease, NAD+ levels lower (maybe we can avoid). Let's pick 4.

We'll wrap: 44.8%, 23.9%, 7.7%, 17.1%, 0.887 g, 0.961 g, 24.4%, 8.9% – that's many. Need to be selective: maybe combine each pair as a range? But rule says 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. So we need to limit total number of double-asterisk wrapped items to 4-5. Could wrap each fact as a phrase, e.g., stromal cells dropped from 44.8% to 23.9% counts as one wrapped fact (though contains two numbers). That's okay. Similarly macrophage fraction rose from 7.7% to 17.1%. Fetal weight increased from 0.887 g to 0.961 g. IUGR fell from 24.4% to 8.9%. That's four wrapped facts. Good.

We also need to wrap study/journal citation in single underscores: Nature Communications, August 27.

Now produce headline under 90 chars: maybe "Macrophage CD38 Depletes NAD⁺ in Aged Placenta, Impairing Fetal Growth". Count characters: Let's count: "Macrophage CD38 Depletes NAD⁺ in Aged Placenta, Impairing Fetal Growth". Count: M(1) ... Let's approximate: It's likely under 90. We'll ensure.

Now body: split into short paragraphs 2-3 sentences each, separated by blank lines.

We need to preserve all facts, numbers, names, citations exactly. No extra commentary.

Let's craft paragraphs:

Paragraph 1: Introduce study: article published Aug 27 in Nature Communications; macrophages deplete NAD+ via CD38 in placenta of old mice, humans, pigs; NAD+ lower in older women.

Paragraph 2: Describe cellular changes: stromal cells decreased from 44.8% to 23.9%; macrophages increased from 7.7% to 17.1%; closer proximity in old placenta.

Paragraph 3: Mechanism: CD38 on macrophages breaks down NMN, blocking NAD+ production in stromal cells; antibody blockade restores NMN effect.…

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PRAXIS AI‑driven protein design completes 25 rounds in a month

In a preprint dated 14 August 2026, researchers introduced PRAXIS, an autonomous system that designs and tests proteins. Software agents propose protein variants, while a robotic lab builds the corresponding DNA, expresses the proteins, and measures their activity. Each experiment’s outcome updates a shared computational model that guides the next round of agent selections.

The team focused on the GH1 family of glycoside‑hydrolase enzymes, taking six natural GH1 sequences and dividing each into eight fragments. By recombining these fragments while preserving the overall protein scaffold, they generated roughly 1.7 million chimeras—proteins assembled from pieces of the original enzymes.

Three autonomous agents searched for variants with activity and selectivity toward glucose, xylose, or mannose. Each agent chose its own batch of candidates, but all experimental results fed into a single measurement set that refreshed the shared model. Consequently, a test performed for one sugar could influence the next selections of the other agents.

Over the course of about a month, the system completed 25 rounds. In each round the robotic lab synthesized DNA for the chosen variants, produced the proteins, and assayed their activity on fluorescent substrates that emit a measurable light signal. The assay result directly determined the next experiment.

After the automated rounds, the top candidates were retested manually alongside the original enzymes. Several variants showed altered selectivity for the target sugars, and the three most active variants shared a common sequence fragment. Upon further validation, these proteins also exhibited higher expression levels, yielding more protein in the lab.

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We need to translate Russian news post into 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.

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.

Let's extract facts:

- scPILOT transfers response of cells to a known stimulus to another patient, cell type, or species.
- Article published August 29 in Advanced Science about scPILOT, a computational model that uses measured cellular response to a stimulus to predict response in another patient, cell type, or species.
- In tests, authors hid cells after stimulus exactly where they made predictions: models only had baseline cells, hidden measurements served as final test.
- Single-cell sequencing shows which genes are active in individual cells.
- Experiment measures one group of cells before stimulus, another after.
- Therefore ordinary calculation often reduces response of whole group to average difference and loses cell-to-cell differences.
- Authors apply optimal transport — a mathematical way to match probabilistic pairs between two groups with minimal total discrepancy. It links cells before and after already measured stimulus and estimates for each baseline cell the shift in gene activity.
- Then model receives baseline cells of a new patient, another cell type, or another species. It finds in prior experiments groups with similar gene activity, transfers their cellular shifts to the new group, and weights more heavily the closer groups.
- Adding this shift to baseline state, scPILOT predicts gene activity after same stimulus.
- On seven immune cell types treated with interferon-beta, a signaling protein of the immune system, scPILOT obtained average R² 0.945.
- This metric shows how predicted average gene activity levels matched measured; best of remaining models had 0.935.
- In test on eight patients R² was 0.598, roughly like CellOT, and by distributions of individual cells scPILOT showed smallest discrepancy.
- Accuracy depended on proximity of baseline cell states.
- Three most similar contexts gave almost same accuracy as all available contexts together, while three most distant worsened prediction.
- On six cancer cell lines differences between lines were stronger than effect of gene knockout. There scPILOT by average gene activity levels was comparable to simple baseline prediction.
- scPILOT uses past experiments as source of prediction, matching their cells to new population: the closer the baseline states, the more useful the transferred response.

We need to include citations: The article published Aug 29 in Advanced Science. So citation: Advanced Science, August 29 maybe? Actually they said "29 августа в Advanced Science вышла статья о scPILOT". So we can wrap as Advanced Science, August 29.

Also maybe mention interferon-beta as a drug/protein: interferon-beta.

Numbers: R² 0.945, 0.935, 0.598, seven immune cell types, eight patients, six cancer cell lines, three most similar contexts, three most distant.

We need at most 4-5 double asterisk facts. Choose key numbers: R² 0.945, R² 0.935, R² 0.598, seven immune cell types, eight patients, six cancer cell lines, interferon-beta maybe. That's many. Need to limit to 4-5. Let's pick: 0.945, 0.935, 0.598, seven immune cell types, eight patients. Could also include interferon-beta as a protein name but maybe not needed. We'll keep 5.

Wrap each in double asterisks.

Now paragraphs: short paragraphs 2-3 sentences each.

Let's craft:…

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Beijing, Tianjin and Hebei Release Joint Neurointerface Standards Plan

On August 26, regulators from Beijing, Tianjin, and Hebei published a joint plan for common standards and testing of neurointerfaces. It outlines rules for neural signals and devices, measuring accuracy and safety, and testing sites.

A neurointerface connects the brain to a device; electrodes record brain electrical activity and send the signal to software, which recognizes a command and makes the device act or return feedback. To compare systems, one must check signal recording, algorithm performance, device properties, and the whole chain.

In the full plan, the three regions assign future work along this chain: electrodes, chips for recording and decoding neural signals, recording systems, and real‑time algorithms. The document tasks them with developing data‑quality and component requirements, device characteristics, safety, and ethics.

One task is to create rules for labeling EEG data—records of brain electrical activity. The plan also calls for assessing signal‑collection accuracy and long‑term safety of neurointerfaces. Common rules should define how to describe these recordings, and how to measure and test components and whole systems.

The plan includes work on quality‑standard laboratories, a measurement and certification center, testing tools, and safety, efficacy, and reliability metrics. Test sites should gather EEG databases, conduct clinical studies, and verify technologies in applied scenarios.

These tasks form a sequence: record the signal, measure system properties, and check application. A 2025 joint document of seven Chinese ministries set a 2027 goal to develop neurointerface technology, industry, and standards; the regional plan translates that goal into work on signals and data, device measurement, testing, and research sites.

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Google unveils PPE for automated geographic forecasting

Google unveiled its Planetary Prediction Engine (PPE) on August 27. The system receives a textual task and labeled source data, then produces a geographic forecast. It is designed to automate the data‑gathering and preparation steps that epidemiologists normally perform manually.

According to the technical preprint, preparing data for an operational epidemic forecast involves more than >700 actions. PPE first defines the target area and time window and decides how to link tables. It then searches open geographic databases, government portals and scientific repositories, harmonizes the information to common district boundaries and assembles a training set.

Using that training set, PPE builds a forecast and tests it on held‑out districts and weeks. During the Bundibugyo ebolavirus outbreak in the Democratic Republic of Congo, the system ranked districts each week before any cases were reported. Over five weekly forecasts it placed 15 of 18 districts that later saw their first cases inside its top‑ten risk list, achieving 83.3% accuracy.

The authors compare this result with the approximately ~73% performance of an earlier published model that also ranked ten districts by risk. PPE separates training from verification; its Feature Gate filter removes variables that would pre‑reveal the answer, such as part of the target indicator, same‑questionnaire data, event consequences or future information. Missing values are imputed only on the training

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