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.…
🔗 Read original →
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.…
🔗 Read original →
Nature
Ultrafast and reference-free sequence discovery in single-cell data
Nature - Malva enables ultrafast, reference-free searching of raw single-cell sequences across millions of cells, transforming static transcriptomic atlases into dynamic resources for understanding...
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
🔗 Read original →
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
🔗 Read original →
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
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
Telegram
Solid State Humanity
Взлом свободной воли: почему Либет получил всю славу за пересказ чужого фокуса
Вы наверняка слышали о знаменитом эксперименте Бенджамина Либета о т.н. потенциале готовности, который покусился на свободу воли. Если нет, то напоминаю: Либет надевал на испытуемых…
Вы наверняка слышали о знаменитом эксперименте Бенджамина Либета о т.н. потенциале готовности, который покусился на свободу воли. Если нет, то напоминаю: Либет надевал на испытуемых…
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:…
🔗 Read original →
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:…
🔗 Read original →
PubMed Central (PMC)
Benchmarking foundation cell models for post-perturbation RNA-seq prediction
Accurately predicting cellular responses to perturbations is essential for understanding cell behaviour in both healthy and diseased states. While perturbation data is ideal for building such predictive models, its availability is considerably lower ...
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.
🔗 Read original →
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.
🔗 Read original →
PubMed Central (PMC)
Structural brain preservation: a potential bridge to future medical technologies
When faced with the prospect of death, some people would prefer a form of long-term preservation that may allow them to be restored to healthy life in the future, if technology ever develops to the point that this is feasible and humane. Some ...
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.…
🔗 Read original →
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.…
🔗 Read original →
PubMed Central (PMC)
Drug‐Based Lifespan Extension in Mice Strongly Affects Lipids Across Six Organs
Caloric restriction is associated with slow aging in model organisms. Additionally, some drugs have also been shown to slow aging in rodents. To better understand metabolic mechanisms that are involved in increased lifespan, we analyzed metabolomic ...
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.
🔗 Read original →
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.
🔗 Read original →
medRxiv
Prospective In-silico Simulation of the VESALIUS-CV Trial Using Biomedical Knowledge Graph and Real-World Data-Driven AI Modeling
Background. Cardiovascular-outcomes trials are lengthy, costly, and associated with substantial uncertainty prior to readout. In-silico trial simulation using real-world data (RWD) has emerged as a potential tool to support earlier decision-making; however…
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:…
🔗 Read original →
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:…
🔗 Read original →
PubMed Central (PMC)
Immune Effector Cell-Associated HLH-like Syndrome: A Review of the Literature of an Increasingly Recognized Entity
Chimeric antigen receptor (CAR)-T cells are a novel type of therapy that is becoming more prominent in the treatment of many hematological malignancies. They are associated with serious side effects including hemophagocytic lymphohistiocytosis ...
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.…
🔗 Read original →
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.…
🔗 Read original →
Nature
CD38-activated macrophages drive age-related placental senescence by depleting NAD [+] in decidual stromal cells
Nature Communications - The authors show that CD38-activated macrophages deplete NAD+ in decidual stromal cells, driving placental senescence and fetal growth restriction during aging. NAD+...
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.
🔗 Read original →
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.
🔗 Read original →
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:…
🔗 Read original →
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:…
🔗 Read original →
PubMed Central (PMC)
Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport
Predicting how single cells respond to perturbations is a central problem in computational biology, with potential relevance to emerging artificial intelligence virtual cell (AIVC) research and drug‐discovery efforts. However, substantial variation ...
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.
🔗 Read original →
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.
🔗 Read original →
Jiemian
《京津冀脑机接口跨区域质量强链工作方案》印发 | 界面新闻
其中提到，加强质量共性技术攻关。支持链主企业、高校、科研院所、医疗机构等开展质量攻关活动，鼓励联合开展质量技术攻关。找准共性质量痛点，推进脑机接口质量强链标志性项目建设。围绕脑机接口涉及的神经信号采集、解码、控制和反馈等关键环节，聚焦脑电信号感知与采集、人机交互技术、脑机接口典型应用场景等方面，推动新型干电极、柔性电极、脑机接口信号采集芯片、计算解码芯片、集成整机采集系统、高性能实时解码算法等质量共性技术突破和转化落地。
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
🔗 Read original →
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
🔗 Read original →
Google Research
Planetary prediction engine: Automating global models via Earth AI
As part of Google Earth AI, we introduce the planetary prediction engine (PPE), an experimental research capability that autonomously executes the full geospatial modeling workflow — from data discovery to model training — achieving improvements across diverse…
We need to translate Russian news post into natural English, format according to 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,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 and citations exactly. No added commentary.
We need to identify important facts: dates: 29 August (the workshop launched pilot). Also maybe "AIDaRS" name, "OpenReview", "GitHub", "NeurIPS". Also maybe "AIDaR workshop at NeurIPS". Also maybe "technical report AIDaRS". Also maybe "voluntary pilot". Also maybe "closed GitHub repositories". Also maybe "pull request". Also maybe "reviewer can leave general feedback or comment on a line". Also maybe "author receives via service". Also maybe "pilot will test whether GitHub review tools give more useful and reproducible feedback". Also maybe "organizers want to know whether uniformly structured project is easier for humans and software agents". Also maybe "participation voluntary, OpenReview remains mandatory submission route".
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. So we can wrap 29 August maybe. Also maybe NeurIPS? That's a conference name, not a number but maybe considered important fact. The rule says "key numbers, percentages, drug/company/gene names, dates". So conference name not required but could be considered important fact? It says "key numbers, percentages, drug/company/gene names, dates". So we should limit to those. So we can wrap date 29 August. Also maybe OpenReview? It's a service name, not a number/percentage/drug/company/gene name/date. So maybe not. However we could wrap GitHub? It's a company name, qualifies as company name. So we can wrap GitHub in double asterisks. Also maybe AIDaRS? It's a project name, not a company/drug/gene. Not required. Could wrap NeurIPS? It's a conference, not a company/drug/gene. Not required. But we could still wrap if we want but limit to 4-5. Let's pick: 29 August, GitHub, OpenReview (maybe not needed), NeurIPS (maybe not needed). Let's stick to date and GitHub as two. Could also include "AIDaRS" as a project name but not required. Might be okay to wrap it but risk exceeding limit. Let's keep to 2-3.
We also need to wrap study/journal citations and publication references in single underscores _. There's no explicit citation like a journal article; but there is "technical report AIDaRS". That's not a journal citation. There's no explicit study/journal citation. So we may not need any underscores. But we could wrap "technical report AIDaRS" maybe not needed. The rule says wrap study/journal citations and publication references in single underscores. If none, we don't add.
Thus we will have double asterisks around 29 August and GitHub maybe also OpenReview? It's a service name, not a company? It's a company/platform. Could be considered company name. But to be safe, we can wrap GitHub and OpenReview as company names. That's two. Plus date. That's three.
Now format: Headline line, blank line, then paragraphs.
We need to split into short paragraphs 2-3 sentences each.…
🔗 Read original →
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,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 and citations exactly. No added commentary.
We need to identify important facts: dates: 29 August (the workshop launched pilot). Also maybe "AIDaRS" name, "OpenReview", "GitHub", "NeurIPS". Also maybe "AIDaR workshop at NeurIPS". Also maybe "technical report AIDaRS". Also maybe "voluntary pilot". Also maybe "closed GitHub repositories". Also maybe "pull request". Also maybe "reviewer can leave general feedback or comment on a line". Also maybe "author receives via service". Also maybe "pilot will test whether GitHub review tools give more useful and reproducible feedback". Also maybe "organizers want to know whether uniformly structured project is easier for humans and software agents". Also maybe "participation voluntary, OpenReview remains mandatory submission route".
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. So we can wrap 29 August maybe. Also maybe NeurIPS? That's a conference name, not a number but maybe considered important fact. The rule says "key numbers, percentages, drug/company/gene names, dates". So conference name not required but could be considered important fact? It says "key numbers, percentages, drug/company/gene names, dates". So we should limit to those. So we can wrap date 29 August. Also maybe OpenReview? It's a service name, not a number/percentage/drug/company/gene name/date. So maybe not. However we could wrap GitHub? It's a company name, qualifies as company name. So we can wrap GitHub in double asterisks. Also maybe AIDaRS? It's a project name, not a company/drug/gene. Not required. Could wrap NeurIPS? It's a conference, not a company/drug/gene. Not required. But we could still wrap if we want but limit to 4-5. Let's pick: 29 August, GitHub, OpenReview (maybe not needed), NeurIPS (maybe not needed). Let's stick to date and GitHub as two. Could also include "AIDaRS" as a project name but not required. Might be okay to wrap it but risk exceeding limit. Let's keep to 2-3.
We also need to wrap study/journal citations and publication references in single underscores _. There's no explicit citation like a journal article; but there is "technical report AIDaRS". That's not a journal citation. There's no explicit study/journal citation. So we may not need any underscores. But we could wrap "technical report AIDaRS" maybe not needed. The rule says wrap study/journal citations and publication references in single underscores. If none, we don't add.
Thus we will have double asterisks around 29 August and GitHub maybe also OpenReview? It's a service name, not a company? It's a company/platform. Could be considered company name. But to be safe, we can wrap GitHub and OpenReview as company names. That's two. Plus date. That's three.
Now format: Headline line, blank line, then paragraphs.
We need to split into short paragraphs 2-3 sentences each.…
🔗 Read original →
sina.bio
What does it mean for data to be AI ready? | Sina Booeshaghi
A short introduction to an experimental workshop submission system for the AIDaR NeurIPS 2026.
AdaptiveFlow screens 69 billion compounds to find new FSP1 and PARP1 inhibitors
On September 1, the journal Nature Biotechnology published an article describing the open‑source virtual‑screening system
🔗 Read original →
On September 1, the journal Nature Biotechnology published an article describing the open‑source virtual‑screening system
🔗 Read original →
Nature
AI-enhanced adaptive virtual screening of large libraries for ligand discovery
Nature Biotechnology - Massive chemical libraries are screened computationally at scale.
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 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 important facts: numbers like 29 August, seven methods, two datasets, 27 combinations, 18 of 27, 12, etc. Also Monroe + TabPFN, CheMeleon, Mol-JEPA + TabICL, MEGA-CL, ExpansionRx, Biogen ADME, 18 combinations, etc. Also Claude Code, 22 human queries.
We need to be selective: at most 4-5 double asterisks. So we need to pick the most important facts: maybe date (29 August), Monroe + TabPFN being best in 18 of 27 combos on ExpansionRx, CheMeleon best in 12, Monroe+TabPFN leader in all 18 combos on Biogen ADME, Claude Code performed calculations, 22 human queries. That's 5 facts.
We need to wrap each in double asterisks. Ensure not to wrap whole sentence, just the fact.
We also need to wrap study/journal citations in single underscores. There's no explicit journal citation; maybe the preprint? The text mentions "препринте о Mol-JEPA". That's a preprint, not a journal. Could treat as preprint on Mol-JEPA. But we need to wrap study/journal citations and publication references in single underscores. There's no explicit journal name; maybe we can treat "препринте о Mol-JEPA" as a citation: preprint on Mol-JEPA. Also "В журнале проекта сохранены 22 содержательных человеческих запроса". That's not a journal citation. Could treat as project journal. But we need to be careful: we should only wrap actual citations. The text: "В препринте о Mol-JEPA её авторы сообщали о меньшей ошибке модели..." So we can wrap "препринте о Mol-JEPA" as preprint on Mol-JEPA. Also maybe "В журнале проекта сохранены 22 содержательных человеческих запроса" could be project journal. But that might be too many. We need to keep underscores only for study/journal citations and publication references. The preprint qualifies. The project journal maybe not a formal publication; but it's a journal of the project. Could be considered a publication reference. We'll include one underscore for the preprint.
We need to ensure we don't overdo underscores. Probably just one.
Now construct headline: maybe "Monroe + TabPFN tops independent AI comparison for drug‑property prediction". Count characters: Let's count: "Monroe + TabPFN tops independent AI comparison for drug‑property prediction". Count characters including spaces: Monroe(6) + space=1 =>7, + (1) =>8, space=1 =>9, TabPFN(7)=>16, space=1=>17, tops(4)=>21, space=1=>22, independent(10)=>32, space=1=>33, AI(2)=>35, space=1=>36, comparison(10)=>46, space=1=>47, for(3)=>50, space=1=>51, drug‑property (maybe hyphen counts as one) drug‑property: let's count "drug‑property": drug (4) + hyphen (1) + property (8) =13 =>64, space=1=>65, prediction(10)=>75. So 75 chars, under 90. Good.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We'll produce maybe 4 paragraphs.
Paragraph 1: Introduce the comparison, date, datasets.
Paragraph 2: Results on ExpansionRx dataset.
Paragraph 3: Results on Biogen ADME dataset and implications.
Paragraph 4: Methodology details, Claude Code, human queries, repository.
We need to embed double asterisks for key facts.
Select facts:…
🔗 Read original →
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.
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: numbers like 29 August, seven methods, two datasets, 27 combinations, 18 of 27, 12, etc. Also Monroe + TabPFN, CheMeleon, Mol-JEPA + TabICL, MEGA-CL, ExpansionRx, Biogen ADME, 18 combinations, etc. Also Claude Code, 22 human queries.
We need to be selective: at most 4-5 double asterisks. So we need to pick the most important facts: maybe date (29 August), Monroe + TabPFN being best in 18 of 27 combos on ExpansionRx, CheMeleon best in 12, Monroe+TabPFN leader in all 18 combos on Biogen ADME, Claude Code performed calculations, 22 human queries. That's 5 facts.
We need to wrap each in double asterisks. Ensure not to wrap whole sentence, just the fact.
We also need to wrap study/journal citations in single underscores. There's no explicit journal citation; maybe the preprint? The text mentions "препринте о Mol-JEPA". That's a preprint, not a journal. Could treat as preprint on Mol-JEPA. But we need to wrap study/journal citations and publication references in single underscores. There's no explicit journal name; maybe we can treat "препринте о Mol-JEPA" as a citation: preprint on Mol-JEPA. Also "В журнале проекта сохранены 22 содержательных человеческих запроса". That's not a journal citation. Could treat as project journal. But we need to be careful: we should only wrap actual citations. The text: "В препринте о Mol-JEPA её авторы сообщали о меньшей ошибке модели..." So we can wrap "препринте о Mol-JEPA" as preprint on Mol-JEPA. Also maybe "В журнале проекта сохранены 22 содержательных человеческих запроса" could be project journal. But that might be too many. We need to keep underscores only for study/journal citations and publication references. The preprint qualifies. The project journal maybe not a formal publication; but it's a journal of the project. Could be considered a publication reference. We'll include one underscore for the preprint.
We need to ensure we don't overdo underscores. Probably just one.
Now construct headline: maybe "Monroe + TabPFN tops independent AI comparison for drug‑property prediction". Count characters: Let's count: "Monroe + TabPFN tops independent AI comparison for drug‑property prediction". Count characters including spaces: Monroe(6) + space=1 =>7, + (1) =>8, space=1 =>9, TabPFN(7)=>16, space=1=>17, tops(4)=>21, space=1=>22, independent(10)=>32, space=1=>33, AI(2)=>35, space=1=>36, comparison(10)=>46, space=1=>47, for(3)=>50, space=1=>51, drug‑property (maybe hyphen counts as one) drug‑property: let's count "drug‑property": drug (4) + hyphen (1) + property (8) =13 =>64, space=1=>65, prediction(10)=>75. So 75 chars, under 90. Good.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We'll produce maybe 4 paragraphs.
Paragraph 1: Introduce the comparison, date, datasets.
Paragraph 2: Results on ExpansionRx dataset.
Paragraph 3: Results on Biogen ADME dataset and implications.
Paragraph 4: Methodology details, Claude Code, human queries, repository.
We need to embed double asterisks for key facts.
Select facts:…
🔗 Read original →
Nature
Accurate predictions on small data with a tabular foundation model
Nature - Tabular Prior-data Fitted Network, a tabular foundation model, provides accurate predictions on small data and outperforms all previous methods on datasets with up to 10,000 samples by a...
We need to translate to English, format: first line 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 facts, numbers, names, citations exactly. No commentary.
We need to identify key facts: date "21 августа в Nature Cell Biology вышла статья" => August 21 in Nature Cell Biology. So date: August 21, maybe year? Not given. We'll keep "August 21". Wrap in double asterisks? It's a date, yes important. Also "примерно у каждой десятой клетки" => about one in ten cells => ~10%. So number: ~10% or "about one in ten". Wrap as about one in ten? Could be ~10%. Also "около 70% первичных клеток с cGAMP — одиночных или первых в локальной группе — прежде образовали мостик с cGAS". So about 70% of primary cells with cGAMP formed a bridge. So about 70%. Also "cGAMP — сигнальная молекула, которую клеточный сенсор ДНК cGAS синтезирует при включении". Not a number. Also "cGAMP от одной активированной клетки проходил через межклеточные соединения, и рядом возникала небольшая группа клеток с реакцией репортёра." Not a number.
We need at most 4-5 double asterisk facts. Choose: date, ~10%, ~70%, maybe also "cGAS" and "STING" are gene/protein names; they should be wrapped? The rule says wrap key numbers, percentages, drug/company/gene names, dates. So gene names also qualify. But we must be selective: at most 4-5 per post. So we need to decide which to wrap. Could wrap date, percentage (~10%), percentage (~70%), and maybe gene names cGAS and STING? That would be >5. Better to wrap date, ~10%, ~70%, and maybe "cGAMP" as key molecule? But it's not a number. The rule says key numbers, percentages, drug/company/gene names, dates. So gene names are allowed but we need to limit total. Perhaps we wrap date, ~10%, ~70%, and gene names cGAS and STING as two separate? That would be 5 total (date, 10%, 70%, cGAS, STING). That's okay (max 5). But we need to ensure not whole sentence wrapped. So we wrap each individually.
We also need to wrap study/journal citations in single underscores. The citation: "21 августа в Nature Cell Biology вышла статья". So we need to wrap the journal name and date in single underscores: Nature Cell Biology, August 21. Probably include year? Not given. We'll just do Nature Cell Biology, August 21.
Now produce translation.
First line headline under 90 chars. Something like: "DNA damage triggers cGAS sensor only in a subset of cells". Count characters: Let's count: "DNA damage triggers cGAS sensor only in a subset of cells". That's 53 characters. Good.
Now blank line, then body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft translation:
Paragraph 1: Introduce reporter construct and measurement of cGAMP. Include date citation.
Paragraph 2: Results after irradiation: signal in about one in ten cells; other DNA damage methods also gave response only in fraction.
Paragraph 3: Experiments with low doses showing spread to neighboring cells forming small groups; hypothesis about micronuclei.
Paragraph 4: Live imaging after induced chromosome missegregation showed cGAS could stay on micronuclei for hours without cGAMP synthesis; localization and synthesis are separate observations.
Paragraph 5: When chromosome segregation disrupted, about 70% of primary cGAMP-positive cells (singles or first in local group) had previously formed a DNA bridge with cGAS; after irradiation cGAMP appeared in primary cells irrespective of prior bridge, indicating dependence on damage origin.…
🔗 Read original →
We need to preserve facts, numbers, names, citations exactly. No commentary.
We need to identify key facts: date "21 августа в Nature Cell Biology вышла статья" => August 21 in Nature Cell Biology. So date: August 21, maybe year? Not given. We'll keep "August 21". Wrap in double asterisks? It's a date, yes important. Also "примерно у каждой десятой клетки" => about one in ten cells => ~10%. So number: ~10% or "about one in ten". Wrap as about one in ten? Could be ~10%. Also "около 70% первичных клеток с cGAMP — одиночных или первых в локальной группе — прежде образовали мостик с cGAS". So about 70% of primary cells with cGAMP formed a bridge. So about 70%. Also "cGAMP — сигнальная молекула, которую клеточный сенсор ДНК cGAS синтезирует при включении". Not a number. Also "cGAMP от одной активированной клетки проходил через межклеточные соединения, и рядом возникала небольшая группа клеток с реакцией репортёра." Not a number.
We need at most 4-5 double asterisk facts. Choose: date, ~10%, ~70%, maybe also "cGAS" and "STING" are gene/protein names; they should be wrapped? The rule says wrap key numbers, percentages, drug/company/gene names, dates. So gene names also qualify. But we must be selective: at most 4-5 per post. So we need to decide which to wrap. Could wrap date, percentage (~10%), percentage (~70%), and maybe gene names cGAS and STING? That would be >5. Better to wrap date, ~10%, ~70%, and maybe "cGAMP" as key molecule? But it's not a number. The rule says key numbers, percentages, drug/company/gene names, dates. So gene names are allowed but we need to limit total. Perhaps we wrap date, ~10%, ~70%, and gene names cGAS and STING as two separate? That would be 5 total (date, 10%, 70%, cGAS, STING). That's okay (max 5). But we need to ensure not whole sentence wrapped. So we wrap each individually.
We also need to wrap study/journal citations in single underscores. The citation: "21 августа в Nature Cell Biology вышла статья". So we need to wrap the journal name and date in single underscores: Nature Cell Biology, August 21. Probably include year? Not given. We'll just do Nature Cell Biology, August 21.
Now produce translation.
First line headline under 90 chars. Something like: "DNA damage triggers cGAS sensor only in a subset of cells". Count characters: Let's count: "DNA damage triggers cGAS sensor only in a subset of cells". That's 53 characters. Good.
Now blank line, then body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft translation:
Paragraph 1: Introduce reporter construct and measurement of cGAMP. Include date citation.
Paragraph 2: Results after irradiation: signal in about one in ten cells; other DNA damage methods also gave response only in fraction.
Paragraph 3: Experiments with low doses showing spread to neighboring cells forming small groups; hypothesis about micronuclei.
Paragraph 4: Live imaging after induced chromosome missegregation showed cGAS could stay on micronuclei for hours without cGAMP synthesis; localization and synthesis are separate observations.
Paragraph 5: When chromosome segregation disrupted, about 70% of primary cGAMP-positive cells (singles or first in local group) had previously formed a DNA bridge with cGAS; after irradiation cGAMP appeared in primary cells irrespective of prior bridge, indicating dependence on damage origin.…
🔗 Read original →
Nature
A versatile cGAMP reporter reveals principles of cGAS activation by DNA damage and chromosome instability
Nature Cell Biology - Lebrec et al. present a FRET-based cGAS reporter and show that cGAS activation by genotoxic stress is rare and uncoupled from micronuclei. The study implicates R-loop...
Scaling autologous CAR‑T therapy faces manufacturing bottlenecks
On September 1, PharmaVoice described the manufacturing challenge of autologous CAR‑T: each patient requires a unique journey from cell collection to treatment. Using Carvykti as an example, the process depends on production, quality control, logistics, and clinic scheduling. Autologous CAR‑T is a cancer cell therapy made from the patient’s own T‑cells, which are engineered, expanded, tested, and returned to the same individual, with each dose undergoing its own cycle.
As patient numbers rise, clinics and manufacturers must run many of these cycles in parallel. Cells, factory slots, materials, QC, dose release, delivery, and treatment timing must align for each person; a delay anywhere postpones that patient’s therapy. “When you scale autologous cell therapy, you’re not enlarging a single batch — you’re replicating processes,” said Mike O’Mara, COO of Cellipont Bioservices, a contract cell‑therapy manufacturer. Early on, one experienced team can handle several cycles, but commercial production uses multiple teams simultaneously reproducing the same personal process; manual steps and operator variability then have a stronger impact on quality and timing. Automation and closed‑system processing reduce manual operations and cycle‑to‑cycle differences.
The Carvykti network already shows this scale: it is available at 348 sites in 19 countries according to Legend Biotech, and all four of its manufacturing sites are operating. The expanded facility in Raritan, New Jersey, is designed for up to 10,000 patients per year, yet every personalized dose still must pass QC, release, delivery, and clinic scheduling. In June 2025 FDA REMS removal, the agency lifted the special safety program for six approved autologous CAR‑T products, including Carvykti, eliminating the need for separate site certification and updating post‑administration monitoring instructions, thereby simplifying part of the post‑release pathway.
In the in vivo CAR‑T approach, a genetic vector delivers instructions that reprogram immune cells inside the patient’s body. June 2025 phase I LB2501 data confirmed that this strategy is being tested in humans, with an ongoing phase I study evaluating whether a single infusion can generate CAR‑T cells in vivo. Such a method could shift much of the external cell‑work to a standardized vector product usable for many patients, although that vector product also requires reliable commercial‑scale manufacturing. _clinical_trials_
🔗 Read original →
On September 1, PharmaVoice described the manufacturing challenge of autologous CAR‑T: each patient requires a unique journey from cell collection to treatment. Using Carvykti as an example, the process depends on production, quality control, logistics, and clinic scheduling. Autologous CAR‑T is a cancer cell therapy made from the patient’s own T‑cells, which are engineered, expanded, tested, and returned to the same individual, with each dose undergoing its own cycle.
As patient numbers rise, clinics and manufacturers must run many of these cycles in parallel. Cells, factory slots, materials, QC, dose release, delivery, and treatment timing must align for each person; a delay anywhere postpones that patient’s therapy. “When you scale autologous cell therapy, you’re not enlarging a single batch — you’re replicating processes,” said Mike O’Mara, COO of Cellipont Bioservices, a contract cell‑therapy manufacturer. Early on, one experienced team can handle several cycles, but commercial production uses multiple teams simultaneously reproducing the same personal process; manual steps and operator variability then have a stronger impact on quality and timing. Automation and closed‑system processing reduce manual operations and cycle‑to‑cycle differences.
The Carvykti network already shows this scale: it is available at 348 sites in 19 countries according to Legend Biotech, and all four of its manufacturing sites are operating. The expanded facility in Raritan, New Jersey, is designed for up to 10,000 patients per year, yet every personalized dose still must pass QC, release, delivery, and clinic scheduling. In June 2025 FDA REMS removal, the agency lifted the special safety program for six approved autologous CAR‑T products, including Carvykti, eliminating the need for separate site certification and updating post‑administration monitoring instructions, thereby simplifying part of the post‑release pathway.
In the in vivo CAR‑T approach, a genetic vector delivers instructions that reprogram immune cells inside the patient’s body. June 2025 phase I LB2501 data confirmed that this strategy is being tested in humans, with an ongoing phase I study evaluating whether a single infusion can generate CAR‑T cells in vivo. Such a method could shift much of the external cell‑work to a standardized vector product usable for many patients, although that vector product also requires reliable commercial‑scale manufacturing. _clinical_trials_
🔗 Read original →
PharmaVoice
The hidden scale-out problem behind CAR-T’s growth
CAR-T’s commercial challenge isn’t making bigger batches. It’s making thousands of patient-specific manufacturing runs behave like one reliable system. In vivo aims to eliminate the batch altogether.
We need to translate Russian news post into natural English, format with headline under 90 chars, blank line, then short paragraphs (2-3 sentences each). 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 identify facts: date: 26 августа (August 26) in JACS Au. Also mention AF3-ReD method. Possibly mention beta-subunit of F1-ATPase, proteins OxlT and NarK. Also mention that reducing number of sequences in MSA also finds closed variants but some predictions diverge. Also mention that authors suggest using intermediate variants as starting points for MD.
We need to select at most 4-5 double-asterisk facts. Likely: date August 26, journal JACS Au, protein β-subunit of F1-ATPase, proteins OxlT and NarK maybe count as two but could be considered one fact? Better to keep under 5. Let's pick: August 26, JACS Au, β-subunit of F1-ATPase, OxlT, NarK (that's 5). Or we could combine OxlT and NarK as one fact: "transporter proteins OxlT and NarK". That would be one fact. Then we have 4 facts: date, journal, β-subunit, transporter proteins. Good.
Now citations: we need to wrap study/journal citations and publication references in single underscores. The article is in JACS Au on August 26. So we can put JACS Au, August 26 maybe. Or JACS Au and date separate? The rule: wrap study/journal citations and publication references in single underscores. So we can put JACS Au, August 26 as a single underscored phrase.
Now formatting: headline under 90 chars, no markdown. Then blank line, then body paragraphs separated by blank lines. Each paragraph 2-3 sentences.
Let's craft headline: "AF3-ReD expands AlphaFold3 to capture multiple protein conformations". Count characters: let's count: "AF3-ReD expands AlphaFold3 to capture multiple protein conformations". That's 61 characters approx. Good.
Now body.
Paragraph 1: Introduce method and date/citation. Something like: "On August 26, researchers published a study in JACS Au describing AF3-ReD, a modification of AlphaFold3 that steers the model toward alternative protein structures. The approach leaves the neural‑network weights unchanged but adds a repulsive term that pushes subsequent runs away from conformations already found." That's 2-3 sentences.
Paragraph 2: Explain why multiple conformations matter, give examples: β-subunit of F1-ATPase switching between open and closed states upon ATP binding, and transporter proteins OxlT and NarK that alternate between inward‑ and outward‑open states. Wrap key facts in double asterisks: August 26, JACS Au, β-subunit of F1-ATPase, OxlT and NarK. Ensure each fact is wrapped.
Paragraph 3: Show results: standard AlphaFold3 gave only the open state for the β‑subunit even with ATP, whereas AF3-ReD produced open, closed and intermediate models that match experimental structures. Also note that reducing the MSA depth yielded some closed models but many predictions diverged from known structures.
Paragraph 4: Conclude with authors' suggestion: using the intermediate conformations as starting points for molecular dynamics simulations can help map ligand‑induced transitions, making structure prediction the first step in dissecting protein functional cycles.
We need to ensure double asterisks only around selected facts, not whole sentences. Let's embed them.
Important: At most 4-5 double asterisk facts. We'll have four: August 26, JACS Au, β-subunit of F1-ATPase, OxlT and NarK. Good.
Now ensure we don't accidentally double asterisk other numbers like "multiple" etc. Avoid.…
🔗 Read original →
We need to identify facts: date: 26 августа (August 26) in JACS Au. Also mention AF3-ReD method. Possibly mention beta-subunit of F1-ATPase, proteins OxlT and NarK. Also mention that reducing number of sequences in MSA also finds closed variants but some predictions diverge. Also mention that authors suggest using intermediate variants as starting points for MD.
We need to select at most 4-5 double-asterisk facts. Likely: date August 26, journal JACS Au, protein β-subunit of F1-ATPase, proteins OxlT and NarK maybe count as two but could be considered one fact? Better to keep under 5. Let's pick: August 26, JACS Au, β-subunit of F1-ATPase, OxlT, NarK (that's 5). Or we could combine OxlT and NarK as one fact: "transporter proteins OxlT and NarK". That would be one fact. Then we have 4 facts: date, journal, β-subunit, transporter proteins. Good.
Now citations: we need to wrap study/journal citations and publication references in single underscores. The article is in JACS Au on August 26. So we can put JACS Au, August 26 maybe. Or JACS Au and date separate? The rule: wrap study/journal citations and publication references in single underscores. So we can put JACS Au, August 26 as a single underscored phrase.
Now formatting: headline under 90 chars, no markdown. Then blank line, then body paragraphs separated by blank lines. Each paragraph 2-3 sentences.
Let's craft headline: "AF3-ReD expands AlphaFold3 to capture multiple protein conformations". Count characters: let's count: "AF3-ReD expands AlphaFold3 to capture multiple protein conformations". That's 61 characters approx. Good.
Now body.
Paragraph 1: Introduce method and date/citation. Something like: "On August 26, researchers published a study in JACS Au describing AF3-ReD, a modification of AlphaFold3 that steers the model toward alternative protein structures. The approach leaves the neural‑network weights unchanged but adds a repulsive term that pushes subsequent runs away from conformations already found." That's 2-3 sentences.
Paragraph 2: Explain why multiple conformations matter, give examples: β-subunit of F1-ATPase switching between open and closed states upon ATP binding, and transporter proteins OxlT and NarK that alternate between inward‑ and outward‑open states. Wrap key facts in double asterisks: August 26, JACS Au, β-subunit of F1-ATPase, OxlT and NarK. Ensure each fact is wrapped.
Paragraph 3: Show results: standard AlphaFold3 gave only the open state for the β‑subunit even with ATP, whereas AF3-ReD produced open, closed and intermediate models that match experimental structures. Also note that reducing the MSA depth yielded some closed models but many predictions diverged from known structures.
Paragraph 4: Conclude with authors' suggestion: using the intermediate conformations as starting points for molecular dynamics simulations can help map ligand‑induced transitions, making structure prediction the first step in dissecting protein functional cycles.
We need to ensure double asterisks only around selected facts, not whole sentences. Let's embed them.
Important: At most 4-5 double asterisk facts. We'll have four: August 26, JACS Au, β-subunit of F1-ATPase, OxlT and NarK. Good.
Now ensure we don't accidentally double asterisk other numbers like "multiple" etc. Avoid.…
🔗 Read original →
bioRxiv
Enhanced sampling of protein conformations in AlphaFold3 with repulsive bias in the diffusion generative model
Conformational changes in proteins are vital to their function yet remain challenging for state-of-the-art artificial intelligence, such as AlphaFold3 (AF3), to predict. It has been observed that AF3 sometimes fails to capture ligand-induced conformational…
We need to translate Russian news post 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, 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: 31 August Axios published analysis; first three months of 2026 local resistance blocked or delayed 75 data-center projects worth $130 billion; August 15 head of Anthropic Dario Amodei linked trust in AI to real cancer treatment; Anthropic quickly scaling work in biology and medicine, hopes to see first signs of result in coming months; NVIDIA with Eli Lilly building lab for drug development; Isomorphic Labs applying AI to drug discovery; Lloyd Price, partner at Nelson Advisors digital health deals, criterion: "Most often I hear one question: where is the contribution or breakthrough that directly led to drug development? It's a team game, and AI is just one of its participants."
Also citations: Brookings citing Data Center Watch; Axios article; maybe we need to wrap study/journal citations in single underscores. There's no explicit journal citation, but Brookings referencing Data Center Watch maybe considered a citation. Also "Axios published analysis" maybe not a citation. The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." So we need to wrap any such references. We have Brookings citing Data Center Watch – that's a citation. Also maybe "Axios" is a publication reference; we could wrap "Axios" in underscores? The instruction says study/journal citations and publication references. So we can wrap "Axios" as a publication reference. Also "Brookings" maybe a think tank, not a journal. But we can treat Brookings as a publication reference. However we need to be selective: at most 4-5 double asterisks for facts. Single underscores for citations can be more? Not limited but we should follow.
We need to keep numbers and names in double asterisks: e.g., 31 August, first three months of 2026, 75, $130 billion, August 15, Anthropic, Dario Amodei, NVIDIA, Eli Lilly, Isomorphic Labs, Lloyd Price, Nelson Advisors. But we must limit to 4-5 double asterisks total. So we need to choose only the most important facts: maybe the numbers: 75, $130 billion, 31 August, first three months of 2026, August 15. That's 5. Or we could include company names as facts but they might be considered important. However the rule says "key numbers, percentages, drug/company/gene names, dates". So company names also qualify. But we must limit to 4-5 total. So we need to decide which to highlight. Perhaps we highlight the numbers and dates, and leave company names without asterisks. Or we could highlight a couple company names and fewer numbers. Let's think: The most salient facts: the blockage of 75 projects worth $130bn in Q1 2026; the date of Axios article (31 Aug); Dario Amodei's statement on Aug 15 linking AI trust to cancer treatment. That's three numbers/dates. Could also include the company names Anthropic, NVIDIA/Eli Lilly, Isomorphic Labs as important but we might exceed limit. We can maybe include Anthropic as a company name, but then we have 4. Let's pick: 75, $130 billion, first three months of 2026, August 15. That's four. Or we could also include 31 August as date of article. But we already have August 15; maybe we need both…
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We need to identify important facts: numbers: 31 August Axios published analysis; first three months of 2026 local resistance blocked or delayed 75 data-center projects worth $130 billion; August 15 head of Anthropic Dario Amodei linked trust in AI to real cancer treatment; Anthropic quickly scaling work in biology and medicine, hopes to see first signs of result in coming months; NVIDIA with Eli Lilly building lab for drug development; Isomorphic Labs applying AI to drug discovery; Lloyd Price, partner at Nelson Advisors digital health deals, criterion: "Most often I hear one question: where is the contribution or breakthrough that directly led to drug development? It's a team game, and AI is just one of its participants."
Also citations: Brookings citing Data Center Watch; Axios article; maybe we need to wrap study/journal citations in single underscores. There's no explicit journal citation, but Brookings referencing Data Center Watch maybe considered a citation. Also "Axios published analysis" maybe not a citation. The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." So we need to wrap any such references. We have Brookings citing Data Center Watch – that's a citation. Also maybe "Axios" is a publication reference; we could wrap "Axios" in underscores? The instruction says study/journal citations and publication references. So we can wrap "Axios" as a publication reference. Also "Brookings" maybe a think tank, not a journal. But we can treat Brookings as a publication reference. However we need to be selective: at most 4-5 double asterisks for facts. Single underscores for citations can be more? Not limited but we should follow.
We need to keep numbers and names in double asterisks: e.g., 31 August, first three months of 2026, 75, $130 billion, August 15, Anthropic, Dario Amodei, NVIDIA, Eli Lilly, Isomorphic Labs, Lloyd Price, Nelson Advisors. But we must limit to 4-5 double asterisks total. So we need to choose only the most important facts: maybe the numbers: 75, $130 billion, 31 August, first three months of 2026, August 15. That's 5. Or we could include company names as facts but they might be considered important. However the rule says "key numbers, percentages, drug/company/gene names, dates". So company names also qualify. But we must limit to 4-5 total. So we need to decide which to highlight. Perhaps we highlight the numbers and dates, and leave company names without asterisks. Or we could highlight a couple company names and fewer numbers. Let's think: The most salient facts: the blockage of 75 projects worth $130bn in Q1 2026; the date of Axios article (31 Aug); Dario Amodei's statement on Aug 15 linking AI trust to cancer treatment. That's three numbers/dates. Could also include the company names Anthropic, NVIDIA/Eli Lilly, Isomorphic Labs as important but we might exceed limit. We can maybe include Anthropic as a company name, but then we have 4. Let's pick: 75, $130 billion, first three months of 2026, August 15. That's four. Or we could also include 31 August as date of article. But we already have August 15; maybe we need both…
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Axios
AI giants lean into health care to stall public backlash
Saving the world with new medical cures is better than being blamed for big electric bills.
Ai2 outlines five AI‑agent tasks for scientific research
On September 1, the research organization Ai2 published a discussion of systems that assist scientists. Participants identified five tasks for an AI agent: providing expert judgment, adjusting the agent’s course during work, varying how assignments are checked, monitoring the quality of source data, and linking analysis to laboratory experiments.
The discussion used AutoDiscovery—a program that proposes hypotheses from scientific data and tests them via analysis—as a starting point. In an August case study of lobular cancer, an oncologist’s comments narrowed the program’s search, and the team then validated the found signal on independent data and tumor samples.
This example illustrates scientific taste: a specialist selects results that could grow into the next question and sets the direction for further search. Research evolves as a project proceeds—unexpected outcomes, new papers, fresh datasets lead the researcher to change the agent’s instructions, context, and tools.
Oncologist Kelly Paulson summed it up: “This is research, not search: we must discover something new and verify it.” Retrieving records, structuring information, and literature review can be predefined and checked against results, whereas a hypothesis about a novel mechanism or surprising experiment needs separate analysis and reproduction.
Abraham Flaksman described a case where the AI spotted an error in the algorithms of a previously published paper; the researcher verified the comment, agreed, and asked the journal to retract the work. The speed of analysis depends on what the system receives—experiment design, data collection, and causal logic.
Steven Salerno noted that AI amplifies both strong research and weak premises with methodological flaws. In projects involving hundreds of cell types and thousands of changing genes, the agent can gather literature and prioritize hypotheses for testing, with each lab result shaping the next hypothesis and the next experiment.
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On September 1, the research organization Ai2 published a discussion of systems that assist scientists. Participants identified five tasks for an AI agent: providing expert judgment, adjusting the agent’s course during work, varying how assignments are checked, monitoring the quality of source data, and linking analysis to laboratory experiments.
The discussion used AutoDiscovery—a program that proposes hypotheses from scientific data and tests them via analysis—as a starting point. In an August case study of lobular cancer, an oncologist’s comments narrowed the program’s search, and the team then validated the found signal on independent data and tumor samples.
This example illustrates scientific taste: a specialist selects results that could grow into the next question and sets the direction for further search. Research evolves as a project proceeds—unexpected outcomes, new papers, fresh datasets lead the researcher to change the agent’s instructions, context, and tools.
Oncologist Kelly Paulson summed it up: “This is research, not search: we must discover something new and verify it.” Retrieving records, structuring information, and literature review can be predefined and checked against results, whereas a hypothesis about a novel mechanism or surprising experiment needs separate analysis and reproduction.
Abraham Flaksman described a case where the AI spotted an error in the algorithms of a previously published paper; the researcher verified the comment, agreed, and asked the journal to retract the work. The speed of analysis depends on what the system receives—experiment design, data collection, and causal logic.
Steven Salerno noted that AI amplifies both strong research and weak premises with methodological flaws. In projects involving hundreds of cell types and thousands of changing genes, the agent can gather literature and prioritize hypotheses for testing, with each lab result shaping the next hypothesis and the next experiment.
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medRxiv
Surprisal-based large language models reveal immunologic insights in lobular breast cancer
In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery…