Jaba Tkemaladze proposes centriole inheritance as a marker of aging in renewing tissues
On 28 August he posted a preprint on the Research Square platform outlining the “centriole counter” hypothesis.
In tissues that renew through repeated divisions, a cell lineage can retain changes in the centriole from one division to the next. In his earlier framework of four hallmarks of cellular age, Tkemaladze placed centrioles alongside telomeres and mitochondria. The current preprint narrows the focus to tissues that constantly replenish themselves with new cells.
A renewing tissue simultaneously replaces specialized cells and maintains a reserve of cells that can divide again. During division, the two daughter cells may receive
🔗 Read original →
On 28 August he posted a preprint on the Research Square platform outlining the “centriole counter” hypothesis.
In tissues that renew through repeated divisions, a cell lineage can retain changes in the centriole from one division to the next. In his earlier framework of four hallmarks of cellular age, Tkemaladze placed centrioles alongside telomeres and mitochondria. The current preprint narrows the focus to tissues that constantly replenish themselves with new cells.
A renewing tissue simultaneously replaces specialized cells and maintains a reserve of cells that can divide again. During division, the two daughter cells may receive
🔗 Read original →
PubMed Central (PMC)
Extensive programmed centriole elimination unveiled in C. elegans embryos
Centrioles are critical for fundamental cellular processes, including signaling, motility, and division. The extent to which centrioles are present after cell cycle exit in a developing organism is not known. The stereotypical lineage of ...
Retro expands Phase I trial of RTR242 to 108 volunteers in Australia
Retro will add two high‑dose cohorts and increase its Phase I study of RTR242 to 108 participants in Australia. The trial enrolls healthy adults aged 18–65 years. This expansion follows the initial dosing that began earlier this year.
The company is adding 32 participants and two new dose‑escalation groups to better define the dose for further clinical development. These groups will help Retro select the optimal dose for subsequent trials. The move aims to pinpoint a tolerable level that can be taken forward.
On August 26, CEO Joe Betts‑Lacqua told Business Insider that the study would grow from 76 to 108 volunteers. In May Retro announced the first human administration of RTR242. Since that start, the company has been expanding enrollment and raising doses.
Betts‑Lacqua explained that early tolerability testing has not yet revealed an upper dose limit, so the team must find a level participants can tolerate and carry into later studies. He said, “This is for us in some sense both a curse and a blessing: because of this it’s a bit harder to assess the moment when we can say: ‘hurray, the drug works’.”
Retro describes RTR242 as a small‑molecule compound that enhances autophagy — the process whereby a cell delivers damaged proteins and cellular components to lysosomes for breakdown. In lysosomes, the acidic interior degrades this cargo into reusable building blocks. The company expects RTR242 to restore lysosomal acidity and make this recycling more efficient.
Full Robbins, a biochemist at the University of Minnesota, called lysosomes an intriguing target for intervention. With the additional 32 participants, Retro widens the dose range from which it will choose the level for further development of RTR242.
🔗 Read original →
Retro will add two high‑dose cohorts and increase its Phase I study of RTR242 to 108 participants in Australia. The trial enrolls healthy adults aged 18–65 years. This expansion follows the initial dosing that began earlier this year.
The company is adding 32 participants and two new dose‑escalation groups to better define the dose for further clinical development. These groups will help Retro select the optimal dose for subsequent trials. The move aims to pinpoint a tolerable level that can be taken forward.
On August 26, CEO Joe Betts‑Lacqua told Business Insider that the study would grow from 76 to 108 volunteers. In May Retro announced the first human administration of RTR242. Since that start, the company has been expanding enrollment and raising doses.
Betts‑Lacqua explained that early tolerability testing has not yet revealed an upper dose limit, so the team must find a level participants can tolerate and carry into later studies. He said, “This is for us in some sense both a curse and a blessing: because of this it’s a bit harder to assess the moment when we can say: ‘hurray, the drug works’.”
Retro describes RTR242 as a small‑molecule compound that enhances autophagy — the process whereby a cell delivers damaged proteins and cellular components to lysosomes for breakdown. In lysosomes, the acidic interior degrades this cargo into reusable building blocks. The company expects RTR242 to restore lysosomal acidity and make this recycling more efficient.
Full Robbins, a biochemist at the University of Minnesota, called lysosomes an intriguing target for intervention. With the additional 32 participants, Retro widens the dose range from which it will choose the level for further development of RTR242.
🔗 Read original →
PubMed Central (PMC)
Autophagy–lysosomal-associated neuronal death in neurodegenerative disease
Autophagy, the major lysosomal pathway for degrading damaged or obsolete constituents, protects neurons by eliminating toxic organelles and peptides, restoring nutrient and energy homeostasis, and inhibiting apoptosis. These functions are especially ...
We need to translate Russian news post into natural English, format per rules.
First line: short headline under 90 chars, no markdown. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026).
We need to preserve all facts, numbers, names, citations exactly.
Let's extract facts:
- João Pedro de Magalhães (note spelling: "Жуан Педру де Магальяйнш" -> "João Pedro de Magalhães").
- He hypothesized that cancer protection may limit regeneration.
- On August 27 he hypothesized: developmental transitions could limit tissue restoration to reduce cancer risk.
- On August 22 Michael West asked whether a short reprogramming signal would suffice to restore large damaged tissue.
- Magalhães added safety condition: returning regenerative ability could weaken tumor protection.
- Partial reprogramming temporarily returns adult cell to a younger gene expression mode while preserving specialization.
- To repair damaged tissue, cells need to divide and build new structure.
- This growth simultaneously requires control: uncontrolled division forms tumor.
- In the post, which references an article in Nature Reviews Cancer, Magalhães formulated hypothesis: "One of the reasons why developmental transitions limit regenerative capacity is, as I suppose, to reduce cancer risk."
- By this evolutionary hypothesis, natural selection could have preserved mechanisms that reduced cancer risk in young organism.
- These same mechanisms stop dangerous cell division and after tissue damage may restrain its regeneration.
- Two animal experiments show such conflict in specific systems.
- In a 2010 study on mice, the MRL line, which has good ear-hole regeneration, ear connective tissue cells lacked p21 — a protein involved in division arrest.
- Mice lacking p21 closed ear holes similarly to MRL.
- In a 2015 study on zebrafish, scientists introduced human ARF gene into genome, which helps suppress tumor growth.
- After fin amputation, ARF activated in blastema — a mass of dividing cells from which the fin regrows.
- In a separate experiment, temporary activation of ARF inhibited regeneration via p53, the cell division control system.
- With partial reprogramming, one must simultaneously check whether the intervention restores tissue's regenerative ability and whether cell division control is preserved.
We need to wrap key numbers, percentages, drug/company/gene names, dates in .
Key facts: dates: August 27, August 22, 2010, 2015. Gene names: p21, ARF, p53. Possibly also MRL line (mouse strain). Also "Nature Reviews Cancer". Also maybe "Michael West". Also "João Pedro de Magalhães". But we need at most 4-5 double asterisks. So we need to be selective: maybe only the most important: dates (2010, 2015) and gene names (p21, ARF, p53) maybe also August dates? But we can only have 4-5. Let's decide: we can include 2010, 2015, p21, ARF, p53. That's 5. Or we could include August dates but they are less critical. We'll go with those.…
🔗 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, 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.
Let's extract facts:
- João Pedro de Magalhães (note spelling: "Жуан Педру де Магальяйнш" -> "João Pedro de Magalhães").
- He hypothesized that cancer protection may limit regeneration.
- On August 27 he hypothesized: developmental transitions could limit tissue restoration to reduce cancer risk.
- On August 22 Michael West asked whether a short reprogramming signal would suffice to restore large damaged tissue.
- Magalhães added safety condition: returning regenerative ability could weaken tumor protection.
- Partial reprogramming temporarily returns adult cell to a younger gene expression mode while preserving specialization.
- To repair damaged tissue, cells need to divide and build new structure.
- This growth simultaneously requires control: uncontrolled division forms tumor.
- In the post, which references an article in Nature Reviews Cancer, Magalhães formulated hypothesis: "One of the reasons why developmental transitions limit regenerative capacity is, as I suppose, to reduce cancer risk."
- By this evolutionary hypothesis, natural selection could have preserved mechanisms that reduced cancer risk in young organism.
- These same mechanisms stop dangerous cell division and after tissue damage may restrain its regeneration.
- Two animal experiments show such conflict in specific systems.
- In a 2010 study on mice, the MRL line, which has good ear-hole regeneration, ear connective tissue cells lacked p21 — a protein involved in division arrest.
- Mice lacking p21 closed ear holes similarly to MRL.
- In a 2015 study on zebrafish, scientists introduced human ARF gene into genome, which helps suppress tumor growth.
- After fin amputation, ARF activated in blastema — a mass of dividing cells from which the fin regrows.
- In a separate experiment, temporary activation of ARF inhibited regeneration via p53, the cell division control system.
- With partial reprogramming, one must simultaneously check whether the intervention restores tissue's regenerative ability and whether cell division control is preserved.
We need to wrap key numbers, percentages, drug/company/gene names, dates in .
Key facts: dates: August 27, August 22, 2010, 2015. Gene names: p21, ARF, p53. Possibly also MRL line (mouse strain). Also "Nature Reviews Cancer". Also maybe "Michael West". Also "João Pedro de Magalhães". But we need at most 4-5 double asterisks. So we need to be selective: maybe only the most important: dates (2010, 2015) and gene names (p21, ARF, p53) maybe also August dates? But we can only have 4-5. Let's decide: we can include 2010, 2015, p21, ARF, p53. That's 5. Or we could include August dates but they are less critical. We'll go with those.…
🔗 Read original →
Nature
The evolution of cancer and ageing: a history of constraint
Nature Reviews Cancer - In this Perspective, de Magalhães explores the evolutionary relationship between cancer and ageing, proposing that the need to minimize cancer risk early in life may...
LessWrong user links egg freezing to family planning decisions
On 26 August, LessWrong user boba_girl wrote a note following the June Reproductive Frontiers conference, linking egg and embryo freezing to family‑size planning. She views oocyte cryopreservation, embryo creation, and polygenic screening as interconnected decisions about timing, partner choice, and the number of possible births.
She starts from her personal situation: she wants children but is still looking for a partner. “I think that once I find a partner I will also try to freeze some embryos,” she writes. Until then, storing eggs buys her time to choose a partner.
Embryos are created after a partner appears, and their number helps the family envision how many birth attempts they could plan. Thus, her reasoning links the choice between eggs and embryos to two goals: preserving freedom to choose a partner and anticipating the possible embryo stock.
On the consumer market for polygenic screening, the procedure looks like a series of IVF cycles, genetic testing, and selection. boba_girl starts this chain earlier, asking what options can be preserved before embryos exist. Polygenic ranking compares the resulting embryos using statistical forecasts of disease risk and certain complex traits derived from genetic data.
The number of viable embryos determines both the number of comparison options for that ranking and the number of potential birth attempts. In her note, reproductive technology enters family planning step‑by‑step: first freeze eggs, then decide with a partner about embryos, and finally choose among those that are obtained.
🔗 Read original →
On 26 August, LessWrong user boba_girl wrote a note following the June Reproductive Frontiers conference, linking egg and embryo freezing to family‑size planning. She views oocyte cryopreservation, embryo creation, and polygenic screening as interconnected decisions about timing, partner choice, and the number of possible births.
She starts from her personal situation: she wants children but is still looking for a partner. “I think that once I find a partner I will also try to freeze some embryos,” she writes. Until then, storing eggs buys her time to choose a partner.
Embryos are created after a partner appears, and their number helps the family envision how many birth attempts they could plan. Thus, her reasoning links the choice between eggs and embryos to two goals: preserving freedom to choose a partner and anticipating the possible embryo stock.
On the consumer market for polygenic screening, the procedure looks like a series of IVF cycles, genetic testing, and selection. boba_girl starts this chain earlier, asking what options can be preserved before embryos exist. Polygenic ranking compares the resulting embryos using statistical forecasts of disease risk and certain complex traits derived from genetic data.
The number of viable embryos determines both the number of comparison options for that ranking and the number of potential birth attempts. In her note, reproductive technology enters family planning step‑by‑step: first freeze eggs, then decide with a partner about embryos, and finally choose among those that are obtained.
🔗 Read original →
PubMed Central (PMC)
Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations
The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural ...
We need to translate Russian text into English, format: first line 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 Chemical Biology, August 26, 2024? Actually date given: 26 августа в Nature Chemical Biology вышла работа о BrainCAB — метке на основе бринзоламида. So citation: Nature Chemical Biology, August 26, 2024? The year not given but likely 2024? The original Russian says "26 августа в Nature Chemical Biology вышла работа". No year given; we can just put Nature Chemical Biology, August 26 (no year). But we need to preserve facts exactly: date is 26 августа. We'll translate to August 26. No year given, so we omit year.
We need to wrap key numbers etc: e.g., BrainCAB tag based on brinzolamide, one chemical step, concentration ~8 times higher, 20 mg/kg dose, Car4 gene, etc. Must be selective: at most 4-5 double asterisk wrapped facts. Choose: BrainCAB tag based on brinzolamide, one chemical step, concentration ~8-fold higher, 20 mg/kg dose, Car4 gene knockout. That's 5.
Wrap study/journal citation in single underscores: Nature Chemical Biology, August 26.
Now produce headline under 90 chars: maybe "BrainCAB Tag Enables Antibody Delivery Across Blood‑Brain Barrier in Mice and Newborn Macaques". Count characters: Let's count: "BrainCAB Tag Enables Antibody Delivery Across Blood‑Brain Barrier in Mice and Newborn Macaques". Count: B(1) r2 a3 i4 n5 C6 A7 B8 (space9) T10 a11 g12 (space13) E14 n15 a16 b17 l18 e19 s20 (space21) A22 n23 t24 i25 b26 o27 d28 y29 (space30) D31 e32 l33 i34 v35 e36 r37 y38 (space39) A40 c41 r42 o43 s44 s45 (space46) B47 l48 o49 o50 d‑51 B52 r53 a54 i55 n56 (space57) B58 a59 r60 r61 i62 e63 r64 (space65) i66 n67 (space68) M69 i70 c71 e72 s73 (space74) a75 n76 d77 (space78) N79 e80 w81 b82 o83 r84 n85 (space86) M87 a88 c89 a90 q91 u92 e93 s94. That's 94 characters, exceeds 90. Need under 90. Let's shorten: maybe "BrainCAB Tag Delivers Antibodies Across Blood‑Brain Barrier in Mice and Macaques". Count: "BrainCAB Tag Delivers Antibodies Across Blood‑Brain Barrier in Mice and Macaques". Let's count: B1 r2 a3 i4 n5 C6 A7 B8 (space9) T10 a11 g12 (space13) D14 e15 l16 i17 v18 e19 r20 s21 (space22) A23 n24 t25 i26 b27 o28 d29 i30 e31 s32 (space33) A34 c35 r36 o37 s38 s39 (space40) B41 l42 o43 o44 d‑45 B46 r47 a48 i49 n50 (space51) B52 a53 r54 r55 i56 e57 r58 (space59) i60 n61 (space62) M63 i64 c65 e66 s67 (space68) a69 n70 d71 (space72) M73 a74 c75 a76 q77 u78 e79 s80. That's 80 characters. Good.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We need to include double asterisks for key facts: BrainCAB tag based on brinzolamide, one chemical step, concentration ~8-fold higher, 20 mg/kg dose, Car4 gene knockout.
We need to wrap study/journal citation in single underscores: Nature Chemical Biology, August 26.
We must preserve facts: mention that tag attaches via one chemical step to protein or short RNA/DNA; binds CA-IV on brain vasculature; antibody with tag accumulates in brain ~8-fold higher after 7 days in mice; in Car4 knockout mice no enrichment; in newborn macaques 20 mg/kg IV dose, antibody with tag found in brain regions and intercapillary tissue on days 5 and 7; each condition had one animal; nanobodies and siRNA also bind CA-IV and enter cells in cell assays; animal experiments focused on antibody.
Now format: after headline, blank line, then paragraphs.
Paragraph 1: introduce BrainCAB tag, its basis, one-step attachment, target CA-IV.
Paragraph 2: describe BBB, need for antibody to cross, concept grew from 2023 work, AAV uses CA-IV.…
🔗 Read original →
We need to wrap key numbers etc: e.g., BrainCAB tag based on brinzolamide, one chemical step, concentration ~8 times higher, 20 mg/kg dose, Car4 gene, etc. Must be selective: at most 4-5 double asterisk wrapped facts. Choose: BrainCAB tag based on brinzolamide, one chemical step, concentration ~8-fold higher, 20 mg/kg dose, Car4 gene knockout. That's 5.
Wrap study/journal citation in single underscores: Nature Chemical Biology, August 26.
Now produce headline under 90 chars: maybe "BrainCAB Tag Enables Antibody Delivery Across Blood‑Brain Barrier in Mice and Newborn Macaques". Count characters: Let's count: "BrainCAB Tag Enables Antibody Delivery Across Blood‑Brain Barrier in Mice and Newborn Macaques". Count: B(1) r2 a3 i4 n5 C6 A7 B8 (space9) T10 a11 g12 (space13) E14 n15 a16 b17 l18 e19 s20 (space21) A22 n23 t24 i25 b26 o27 d28 y29 (space30) D31 e32 l33 i34 v35 e36 r37 y38 (space39) A40 c41 r42 o43 s44 s45 (space46) B47 l48 o49 o50 d‑51 B52 r53 a54 i55 n56 (space57) B58 a59 r60 r61 i62 e63 r64 (space65) i66 n67 (space68) M69 i70 c71 e72 s73 (space74) a75 n76 d77 (space78) N79 e80 w81 b82 o83 r84 n85 (space86) M87 a88 c89 a90 q91 u92 e93 s94. That's 94 characters, exceeds 90. Need under 90. Let's shorten: maybe "BrainCAB Tag Delivers Antibodies Across Blood‑Brain Barrier in Mice and Macaques". Count: "BrainCAB Tag Delivers Antibodies Across Blood‑Brain Barrier in Mice and Macaques". Let's count: B1 r2 a3 i4 n5 C6 A7 B8 (space9) T10 a11 g12 (space13) D14 e15 l16 i17 v18 e19 r20 s21 (space22) A23 n24 t25 i26 b27 o28 d29 i30 e31 s32 (space33) A34 c35 r36 o37 s38 s39 (space40) B41 l42 o43 o44 d‑45 B46 r47 a48 i49 n50 (space51) B52 a53 r54 r55 i56 e57 r58 (space59) i60 n61 (space62) M63 i64 c65 e66 s67 (space68) a69 n70 d71 (space72) M73 a74 c75 a76 q77 u78 e79 s80. That's 80 characters. Good.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We need to include double asterisks for key facts: BrainCAB tag based on brinzolamide, one chemical step, concentration ~8-fold higher, 20 mg/kg dose, Car4 gene knockout.
We need to wrap study/journal citation in single underscores: Nature Chemical Biology, August 26.
We must preserve facts: mention that tag attaches via one chemical step to protein or short RNA/DNA; binds CA-IV on brain vasculature; antibody with tag accumulates in brain ~8-fold higher after 7 days in mice; in Car4 knockout mice no enrichment; in newborn macaques 20 mg/kg IV dose, antibody with tag found in brain regions and intercapillary tissue on days 5 and 7; each condition had one animal; nanobodies and siRNA also bind CA-IV and enter cells in cell assays; animal experiments focused on antibody.
Now format: after headline, blank line, then paragraphs.
Paragraph 1: introduce BrainCAB tag, its basis, one-step attachment, target CA-IV.
Paragraph 2: describe BBB, need for antibody to cross, concept grew from 2023 work, AAV uses CA-IV.…
🔗 Read original →
Nature
CA-IV-directed small-molecule shuttle enables targeted brain delivery of biologics
Nature Chemical Biology - Existing receptor targets to direct delivery of biologics across the blood−brain barrier are ubiquitously expressed in the central nervous system (CNS) and...
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 must preserve all facts, numbers, names, citations exactly. No commentary.
We need to identify key facts: numbers: 25,306 images, 40 tissue types, 970 donors, age 21-70 years, 15 tissues reliable trajectories, arteries peak at 30s, uterus/vagina early/mid 50s, digestive tissues, prostate, testes two peaks ~30 and ~50 years, three other algorithms, etc. Also mention date: 31 August in Nature Aging (the work came out). Actually text: "31 августа в Nature Aging вышла работа о PathStAR". So date: August 31, publication in Nature Aging. Could wrap date in double asterisks? It's a date. Also maybe wrap the number of images, donors, tissues, etc. But limit to 4-5 double asterisks total.
We need to be selective: maybe highlight: 25,306 images, 970 donors, 15 tissues, 30s (arteries), early/mid 50s (uterus/vagina), ~30 and ~50 years (digestive, prostate, testes). That's many. Need max 4-5. Choose most important: number of images, number of donors, number of tissues with reliable trajectories, peak ages for arteries, peak ages for uterus/vagina. That's 5.
Alternatively we could include date as double asterisk? But date is also important. However we need max 4-5. Let's decide: include 25,306 images, 970 donors, 15 tissues, arteries peak in the 30s, uterus/vagina peak in early/mid‑50s. That's 5.
We must not wrap whole sentences; just wrap the fact inside double asterisks, not the whole sentence. Eg: "The study analyzed 25,306 images..." That's okay.
Now citations: wrap study/journal citation in single underscores: Nature Aging, August 31 maybe include year? Not given year; but we can just put Nature Aging, August 31. Could also add year if known but not in text. We'll follow given: Nature Aging, August 31.
Now produce paragraphs 2-3 sentences each.
Let's craft translation.
Paragraph 1: Headline line then blank line then first paragraph.
Headline: something like "PathStAR maps human tissue aging peaks across the lifespan". Under 90 chars.
Now body.
Paragraph 1: Introduce method and dataset. Include numbers.
Sentence: "PathStAR matches digital scans of stained tissue sections to measure how quickly their structure changes." Next: "The researchers analyzed 25,306 images from 40 tissue types taken from 970 post‑mortem donors aged 21 to 70." Next: "A separate model estimated the donors’ chronological age from the same sections." That's 3 sentences.
Paragraph 2: Explain how PathStAR works and results.
Sentence: "PathStAR looks for age intervals in which a given tissue’s structure remodels fastest." Next: "Working with the GTEx collection, they split each scan into patches, turned visible structural features into numeric vectors, and compared those vectors between adjacent ten‑year age groups, shifting the group boundaries by one year each step." Next: "This produced a curve of structural‑remodeling speed for each tissue." That's 3 sentences.
Paragraph 3: Report reliable trajectories and peak ages.
Sentence: "Reliable trajectories were obtained for 15 tissues." Next: "In arteries the peak speed occurred in the 30s; in uterus and vagina it rose in the early and mid‑50s, around menopause." Next: "For digestive tissues, prostate and testes the curve showed two rises, near 30 and near 50 years." That's 3 sentences.…
🔗 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 must preserve all facts, numbers, names, citations exactly. No commentary.
We need to identify key facts: numbers: 25,306 images, 40 tissue types, 970 donors, age 21-70 years, 15 tissues reliable trajectories, arteries peak at 30s, uterus/vagina early/mid 50s, digestive tissues, prostate, testes two peaks ~30 and ~50 years, three other algorithms, etc. Also mention date: 31 August in Nature Aging (the work came out). Actually text: "31 августа в Nature Aging вышла работа о PathStAR". So date: August 31, publication in Nature Aging. Could wrap date in double asterisks? It's a date. Also maybe wrap the number of images, donors, tissues, etc. But limit to 4-5 double asterisks total.
We need to be selective: maybe highlight: 25,306 images, 970 donors, 15 tissues, 30s (arteries), early/mid 50s (uterus/vagina), ~30 and ~50 years (digestive, prostate, testes). That's many. Need max 4-5. Choose most important: number of images, number of donors, number of tissues with reliable trajectories, peak ages for arteries, peak ages for uterus/vagina. That's 5.
Alternatively we could include date as double asterisk? But date is also important. However we need max 4-5. Let's decide: include 25,306 images, 970 donors, 15 tissues, arteries peak in the 30s, uterus/vagina peak in early/mid‑50s. That's 5.
We must not wrap whole sentences; just wrap the fact inside double asterisks, not the whole sentence. Eg: "The study analyzed 25,306 images..." That's okay.
Now citations: wrap study/journal citation in single underscores: Nature Aging, August 31 maybe include year? Not given year; but we can just put Nature Aging, August 31. Could also add year if known but not in text. We'll follow given: Nature Aging, August 31.
Now produce paragraphs 2-3 sentences each.
Let's craft translation.
Paragraph 1: Headline line then blank line then first paragraph.
Headline: something like "PathStAR maps human tissue aging peaks across the lifespan". Under 90 chars.
Now body.
Paragraph 1: Introduce method and dataset. Include numbers.
Sentence: "PathStAR matches digital scans of stained tissue sections to measure how quickly their structure changes." Next: "The researchers analyzed 25,306 images from 40 tissue types taken from 970 post‑mortem donors aged 21 to 70." Next: "A separate model estimated the donors’ chronological age from the same sections." That's 3 sentences.
Paragraph 2: Explain how PathStAR works and results.
Sentence: "PathStAR looks for age intervals in which a given tissue’s structure remodels fastest." Next: "Working with the GTEx collection, they split each scan into patches, turned visible structural features into numeric vectors, and compared those vectors between adjacent ten‑year age groups, shifting the group boundaries by one year each step." Next: "This produced a curve of structural‑remodeling speed for each tissue." That's 3 sentences.
Paragraph 3: Report reliable trajectories and peak ages.
Sentence: "Reliable trajectories were obtained for 15 tissues." Next: "In arteries the peak speed occurred in the 30s; in uterus and vagina it rose in the early and mid‑50s, around menopause." Next: "For digestive tissues, prostate and testes the curve showed two rises, near 30 and near 50 years." That's 3 sentences.…
🔗 Read original →
Nature
Mapping structural aging across human tissues reveals tissue-specific trajectories and coordinated deterioration
Nature Aging - Yadav et al. develop a framework that quantifies tissue structural aging without training on chronological age, using a large dataset of human histology images. They reveal distinct...
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 citations: The text mentions "27 августа в Science вышла работа о конкурсе OpenKnot". So citation: Science, August 27? Actually date: 27 августа (August 27). Should we keep date? It says "27 августа в Science вышла работа". So we need to wrap the citation in single underscores: Science, August 27. Also maybe mention "OpenKnot" contest? Not a citation. Also mention "SHAPE" as a method, not citation. Also mention "cryo-electron microscopy". Not citation.
We need to wrap key numbers etc in double asterisks. Let's identify important facts:
- Algorithms and participants achieved score 90 out of 100 on 19 out of 20 targets in each of two new rounds.
- In first round: six AI methods and Eterna participants worked on 17 targets; most algorithms lagged behind humans.
- In second round: participants got RNet in interface; AI methods updated with its predictions.
- Then two new sets of 20 targets each, one set with RNA length up to 240 nucleotides.
- In both sets, AI and Eterna participants found solutions scoring 90/100 for 19 targets.
- For 19 out of 20 targets of third round, at least one design showed recovery of at least 80% of specified stems.
- Three molecules, whose sequences were proposed by AI methods, were investigated by cryo-electron microscopy; in all three, seven intended stems were visible.
- On two most detailed maps, additional pairs and contacts between bases appeared, which programs had not predefined.
We need to limit double asterisks to at most 4-5 per post. Choose maybe 5 key facts: scores 90/100 for 19/20 targets, RNA length up to 240 nucleotides, recovery of at least 80% stems, seven intended stems visible, additional pairs/contacts.
We need to wrap each fact with .
But careful: not wrap whole sentence, just the fact within sentence. Eg: "algorithms and participants achieved a score of 90 out of 100 on 19 out of 20 targets". That's two double asterisks groups. Might count as two facts. We need to keep total groups limited to 4-5. Could combine: "90/100 on 19/20 targets". That's one group. Then "up to 240 nucleotides". Then "at least 80% stem recovery". Then "seven intended stems visible". Then "additional base pairs and contacts". That's five groups.
We must not wrap whole sentence; just the fact.
Now citations: wrap study/journal citations in single underscores. We have Science, August 27. Possibly also mention "OpenKnot" contest? Not a citation. Also maybe "RNet" is a model, not citation.
We need to ensure we preserve all facts, numbers, names exactly.
Now produce headline: maybe "AI and Eterna Players Match in RNA Pseudoknot Design Challenge". Count characters: Let's count: "AI and Eterna Players Match in RNA Pseudoknot Design Challenge". That's length? Let's count: AI(2) + space=1 =>3, and=3 =>6, space=1 =>7, Eterna=6 =>13, space=1 =>14, Players=7 =>21, space=1 =>22, Match=5 =>27, space=1 =>28, in=2 =>30, space=1 =>31, RNA=3 =>34, space=1 =>35, Pseudoknot=10 =>45, space=1 =>46, Design=6 =>52, space=1 =>53, Challenge=9 =>62. So 62 chars, under 90.
Now blank line.
Then body paragraphs, each 2-3 sentences.
We need to split into short paragraphs.
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. No added commentary.
We need to identify citations: The text mentions "27 августа в Science вышла работа о конкурсе OpenKnot". So citation: Science, August 27? Actually date: 27 августа (August 27). Should we keep date? It says "27 августа в Science вышла работа". So we need to wrap the citation in single underscores: Science, August 27. Also maybe mention "OpenKnot" contest? Not a citation. Also mention "SHAPE" as a method, not citation. Also mention "cryo-electron microscopy". Not citation.
We need to wrap key numbers etc in double asterisks. Let's identify important facts:
- Algorithms and participants achieved score 90 out of 100 on 19 out of 20 targets in each of two new rounds.
- In first round: six AI methods and Eterna participants worked on 17 targets; most algorithms lagged behind humans.
- In second round: participants got RNet in interface; AI methods updated with its predictions.
- Then two new sets of 20 targets each, one set with RNA length up to 240 nucleotides.
- In both sets, AI and Eterna participants found solutions scoring 90/100 for 19 targets.
- For 19 out of 20 targets of third round, at least one design showed recovery of at least 80% of specified stems.
- Three molecules, whose sequences were proposed by AI methods, were investigated by cryo-electron microscopy; in all three, seven intended stems were visible.
- On two most detailed maps, additional pairs and contacts between bases appeared, which programs had not predefined.
We need to limit double asterisks to at most 4-5 per post. Choose maybe 5 key facts: scores 90/100 for 19/20 targets, RNA length up to 240 nucleotides, recovery of at least 80% stems, seven intended stems visible, additional pairs/contacts.
We need to wrap each fact with .
But careful: not wrap whole sentence, just the fact within sentence. Eg: "algorithms and participants achieved a score of 90 out of 100 on 19 out of 20 targets". That's two double asterisks groups. Might count as two facts. We need to keep total groups limited to 4-5. Could combine: "90/100 on 19/20 targets". That's one group. Then "up to 240 nucleotides". Then "at least 80% stem recovery". Then "seven intended stems visible". Then "additional base pairs and contacts". That's five groups.
We must not wrap whole sentence; just the fact.
Now citations: wrap study/journal citations in single underscores. We have Science, August 27. Possibly also mention "OpenKnot" contest? Not a citation. Also maybe "RNet" is a model, not citation.
We need to ensure we preserve all facts, numbers, names exactly.
Now produce headline: maybe "AI and Eterna Players Match in RNA Pseudoknot Design Challenge". Count characters: Let's count: "AI and Eterna Players Match in RNA Pseudoknot Design Challenge". That's length? Let's count: AI(2) + space=1 =>3, and=3 =>6, space=1 =>7, Eterna=6 =>13, space=1 =>14, Players=7 =>21, space=1 =>22, Match=5 =>27, space=1 =>28, in=2 =>30, space=1 =>31, RNA=3 =>34, space=1 =>35, Pseudoknot=10 =>45, space=1 =>46, Design=6 =>52, space=1 =>53, Challenge=9 =>62. So 62 chars, under 90.
Now blank line.
Then body paragraphs, each 2-3 sentences.
We need to split into short paragraphs.
Let's craft paragraphs:…
🔗 Read original →
PubMed Central (PMC)
De novo design of RNA pseudoknots with deep learning
RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an ...
Longevity Survey Database 'Who Wants to Live Forever' Released
On August 31, database creator Maxim Elison announced the release of version 1.1.3 of the 'Who Wants to Live Forever' registry, which links longevity percentages to specific questions and their sources. The registry contains 185 sources, 2,404 numerical observations, and 37 charts, storing for each result the exact question, conditions, and primary source.
The dashboard includes two U.S. records from 2025 about living to 100 years. In one survey respondents name their desired age, with 30% selecting 100. In another, a yes/no question receives agreement from 49% of participants. A different pair shows 57% wanting to reach 1
🔗 Read original →
On August 31, database creator Maxim Elison announced the release of version 1.1.3 of the 'Who Wants to Live Forever' registry, which links longevity percentages to specific questions and their sources. The registry contains 185 sources, 2,404 numerical observations, and 37 charts, storing for each result the exact question, conditions, and primary source.
The dashboard includes two U.S. records from 2025 about living to 100 years. In one survey respondents name their desired age, with 30% selecting 100. In another, a yes/no question receives agreement from 49% of participants. A different pair shows 57% wanting to reach 1
🔗 Read original →
PubMed Central (PMC)
Gender disparity in the individual attitude toward longevity among Japanese population: Findings from a national survey
The unprecedented population aging brings profound influences to the social values of longevity. The individual attitudes toward the expended life time deserves scrutiny, as it reflects the impacts of social networks and social welfare on people’s ...
AI‑Driven AutoDiscovery Finds Immune Signal in Lobular Breast Cancer
On 27 August, Ai2 and Providence Swedish reported work in which the AutoDiscovery language model searched breast‑cancer data for testable hypotheses; oncologist comments narrowed the search, and one signal was later validated in independent data and tumor tissue.
Lobular cancer was compared with the more common ductal type using the open TCGA database, which contains 1,097 cases with tumor type, mutations, gene activity, and treatment outcomes. AutoDiscovery generates hypotheses, plans analyses, writes executable Python code, runs calculations, and highlights results that most shift its hypothesis score; researchers receive the hypothesis, analysis, and code to reproduce the check.
In the SetScope study, two coding errors altered results enough for the author to retract them. The first run examined only table descriptions and tested 100 hypotheses, which reviewers deemed unsuitable for clinical interpretation, prompting the oncologist to comment on 26 positive findings. A second run evaluated 500 hypotheses.
Among the 13 top‑scoring results from the second run, the third‑ranked hypothesis proposed higher activity of PDCD1 and CD274 in lobular tumors. PDCD1 encodes the PD‑1 receptor and CD274 encodes the PD‑L1 ligand; their interaction suppresses T‑cell attack on the tumor.
Using the METABRIC database, the authors selected hormone‑sensitive, HER2‑negative primary tumors: 696 ductal and 74 lobular. Both genes showed slightly higher expression in lobular tumors. They then stained 12 lobular tumor samples and six normal tissue samples to distinguish cell types, finding more CD8⁺ and CD4⁺ T cells bearing PD‑1 in the stroma surrounding lobular cancers.
Oncologist Kelly Polson described the sequence in the partnership announcement: “AutoDiscovery helped us see a promising signal we might otherwise have missed; we then checked it on additional data sets and in the lab.” The earlier GELATO 2023 study of metastatic lobular cancer had examined carboplatin plus PD‑L1 blockade; the authors now recommend separate evaluation of immunotherapy for lobular cancer in future trials, noting that oncologist guidance directed the search while independent databases and tissue samples enabled two‑way verification.
🔗 Read original →
On 27 August, Ai2 and Providence Swedish reported work in which the AutoDiscovery language model searched breast‑cancer data for testable hypotheses; oncologist comments narrowed the search, and one signal was later validated in independent data and tumor tissue.
Lobular cancer was compared with the more common ductal type using the open TCGA database, which contains 1,097 cases with tumor type, mutations, gene activity, and treatment outcomes. AutoDiscovery generates hypotheses, plans analyses, writes executable Python code, runs calculations, and highlights results that most shift its hypothesis score; researchers receive the hypothesis, analysis, and code to reproduce the check.
In the SetScope study, two coding errors altered results enough for the author to retract them. The first run examined only table descriptions and tested 100 hypotheses, which reviewers deemed unsuitable for clinical interpretation, prompting the oncologist to comment on 26 positive findings. A second run evaluated 500 hypotheses.
Among the 13 top‑scoring results from the second run, the third‑ranked hypothesis proposed higher activity of PDCD1 and CD274 in lobular tumors. PDCD1 encodes the PD‑1 receptor and CD274 encodes the PD‑L1 ligand; their interaction suppresses T‑cell attack on the tumor.
Using the METABRIC database, the authors selected hormone‑sensitive, HER2‑negative primary tumors: 696 ductal and 74 lobular. Both genes showed slightly higher expression in lobular tumors. They then stained 12 lobular tumor samples and six normal tissue samples to distinguish cell types, finding more CD8⁺ and CD4⁺ T cells bearing PD‑1 in the stroma surrounding lobular cancers.
Oncologist Kelly Polson described the sequence in the partnership announcement: “AutoDiscovery helped us see a promising signal we might otherwise have missed; we then checked it on additional data sets and in the lab.” The earlier GELATO 2023 study of metastatic lobular cancer had examined carboplatin plus PD‑L1 blockade; the authors now recommend separate evaluation of immunotherapy for lobular cancer in future trials, noting that oncologist guidance directed the search while independent databases and tissue samples enabled two‑way verification.
🔗 Read original →
PubMed Central (PMC)
PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO trial
Invasive lobular breast cancer (ILC) is the second most common histological breast cancer subtype, but ILC-specific trials are lacking. Translational research revealed an immune-related ILC subset, and in mouse ILC models, synergy between immune ...
Light‑softening gel lets researchers control crypt division in mouse intestinal organoids
On 29 August, researchers published a study in Advanced Science describing how a light‑softening hydrogel enabled precise control of crypt division in mouse intestinal organoids. The organoids are three‑dimensional models of the intestinal epithelium that contain stem‑cell‑rich crypts. In normal growth or repair, a crypt can split into two daughter crypts, but in standard organoids this splitting occurs randomly, making it hard to follow a single structure from start to finish.
They placed the organoids in a soft gel that a laser can locally soften. First, the laser created a single channel for one crypt; after three days, two V‑shaped channels were made at its base. The tissue grew into the softened regions, allowing the researchers to set the time and place of division and to watch one crypt develop into two
🔗 Read original →
On 29 August, researchers published a study in Advanced Science describing how a light‑softening hydrogel enabled precise control of crypt division in mouse intestinal organoids. The organoids are three‑dimensional models of the intestinal epithelium that contain stem‑cell‑rich crypts. In normal growth or repair, a crypt can split into two daughter crypts, but in standard organoids this splitting occurs randomly, making it hard to follow a single structure from start to finish.
They placed the organoids in a soft gel that a laser can locally soften. First, the laser created a single channel for one crypt; after three days, two V‑shaped channels were made at its base. The tissue grew into the softened regions, allowing the researchers to set the time and place of division and to watch one crypt develop into two
🔗 Read original →
PubMed Central (PMC)
Sequential Patterning of Photoresponsive Hydrogels Directs Crypt Fission and Reveals the Role of Epithelial Curvature on Fission…
Fission increases the number of crypts in the intestine during neonatal growth and also restores crypt density after injury by bifurcation of a pre‐existing parent crypt into daughter crypts. While fission is typically symmetric in healthy crypts, ...
We need to translate Russian news post to English, format: first line headline under 90 chars, no markdown prefix. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. At most 4-5 per post, never a whole sentence. Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026). Must preserve facts, numbers, names, citations exactly. No commentary, no invented detail. No source link.
We need to extract facts: date: 29 August (presumably 2024? Not given year. The text: "29 августа в Advanced Science вышла работа о мышиных кишечных органоидах". So date: August 29 (year not given). Could be 2024? Not specified. We'll keep as "August 29". Might need year? The original Russian didn't include year. We'll just keep "August 29". Might be ambiguous but we preserve exactly as given: "29 августа". In English: "August 29". We'll not add year.
Important facts to bold: numbers: 184 organoids, ~70% formed daughter crypts, ~85% of successful divisions had short/long daughter length ratio >=0.7, channel widths 50 and 70 micrometers, gene Myh9, myosin IIA, maybe also "light‑softening gel". We need at most 4-5 bolded facts. Choose: 184 organoids, ~70%, ~85%, 50 and 70 micrometers, Myh9 gene. That's 5.
Citations: need to wrap study/journal citation and publication references in single underscores. The work published in Advanced Science on August 29. So we need "Advanced Science, August 29". Also maybe mention "2025 year work"? The text: "В работе 2025 года часть нынешней команды уже пользовалась светочувствительным гелем...". That is a citation to a 2025 work. We need to wrap that as well: maybe "2025 work" but better: "some 2025 study" but we need to keep exact reference: "В работе 2025 года". So we can put "2025 work" but we need to preserve the fact that it's a 2025 work. Could format as "2025 study". However we need to wrap study/journal citations and publication references in single underscores. So we can put "2025 work" as a reference. Also the current work is in Advanced Science, we already have that.
We must not bold whole sentences. So we will embed bold within sentences.
Now produce headline: maybe "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters: Let's count: "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters including spaces: Let's approximate: Light‑softening (16 inc hyphen?), plus space =17, gel (3) =>20, space=21, lets(4)=>25, space=26, researchers(10)=>36, space=37, control(7)=>44, space=45, crypt(5)=>50, space=51, division(8)=>59, space=60, in(2)=>62, space=63, mouse(5)=>68, space=69, intestinal(10)=>79, space=80, organoids(8)=>88. So 88 characters, under 90. Good.
Now body paragraphs. Need short paragraphs 2-3 sentences each, separated by blank lines.
We'll write maybe 4-5 paragraphs.
Paragraph 1: Introduce the study: On August 29, a paper in Advanced Science described how a light‑softening hydrogel enabled researchers to shape and split intestinal crypts in 3D mouse organoids. They used a laser to locally soften the gel, creating channels that guided crypt growth and division.
Paragraph 2: Details of the method: First, a laser made a single channel for one crypt; after three days, two V‑shaped channels were formed at its base. Tissue grew into the softened zones, letting scientists set the time and place of division and watch one crypt produce two daughters.…
🔗 Read original →
We need to extract facts: date: 29 August (presumably 2024? Not given year. The text: "29 августа в Advanced Science вышла работа о мышиных кишечных органоидах". So date: August 29 (year not given). Could be 2024? Not specified. We'll keep as "August 29". Might need year? The original Russian didn't include year. We'll just keep "August 29". Might be ambiguous but we preserve exactly as given: "29 августа". In English: "August 29". We'll not add year.
Important facts to bold: numbers: 184 organoids, ~70% formed daughter crypts, ~85% of successful divisions had short/long daughter length ratio >=0.7, channel widths 50 and 70 micrometers, gene Myh9, myosin IIA, maybe also "light‑softening gel". We need at most 4-5 bolded facts. Choose: 184 organoids, ~70%, ~85%, 50 and 70 micrometers, Myh9 gene. That's 5.
Citations: need to wrap study/journal citation and publication references in single underscores. The work published in Advanced Science on August 29. So we need "Advanced Science, August 29". Also maybe mention "2025 year work"? The text: "В работе 2025 года часть нынешней команды уже пользовалась светочувствительным гелем...". That is a citation to a 2025 work. We need to wrap that as well: maybe "2025 work" but better: "some 2025 study" but we need to keep exact reference: "В работе 2025 года". So we can put "2025 work" but we need to preserve the fact that it's a 2025 work. Could format as "2025 study". However we need to wrap study/journal citations and publication references in single underscores. So we can put "2025 work" as a reference. Also the current work is in Advanced Science, we already have that.
We must not bold whole sentences. So we will embed bold within sentences.
Now produce headline: maybe "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters: Let's count: "Light‑softening gel lets researchers control crypt division in mouse intestinal organoids". Count characters including spaces: Let's approximate: Light‑softening (16 inc hyphen?), plus space =17, gel (3) =>20, space=21, lets(4)=>25, space=26, researchers(10)=>36, space=37, control(7)=>44, space=45, crypt(5)=>50, space=51, division(8)=>59, space=60, in(2)=>62, space=63, mouse(5)=>68, space=69, intestinal(10)=>79, space=80, organoids(8)=>88. So 88 characters, under 90. Good.
Now body paragraphs. Need short paragraphs 2-3 sentences each, separated by blank lines.
We'll write maybe 4-5 paragraphs.
Paragraph 1: Introduce the study: On August 29, a paper in Advanced Science described how a light‑softening hydrogel enabled researchers to shape and split intestinal crypts in 3D mouse organoids. They used a laser to locally soften the gel, creating channels that guided crypt growth and division.
Paragraph 2: Details of the method: First, a laser made a single channel for one crypt; after three days, two V‑shaped channels were formed at its base. Tissue grew into the softened zones, letting scientists set the time and place of division and watch one crypt produce two daughters.…
🔗 Read original →
PubMed Central (PMC)
Sequential Patterning of Photoresponsive Hydrogels Directs Crypt Fission and Reveals the Role of Epithelial Curvature on Fission…
Fission increases the number of crypts in the intestine during neonatal growth and also restores crypt density after injury by bifurcation of a pre‐existing parent crypt into daughter crypts. While fission is typically symmetric in healthy crypts, ...
We need to translate Russian news into English, format with headline line under 90 chars, then blank line, then body paragraphs (2-3 sentences each) separated by blank lines. Wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post. Wrap study/journal citations and publication references in single underscores.
We need to identify key facts: FATE DNA editor, described August 31 in Nature Communications. Gene LMNA, progerin, farnesyl. Numbers: 322 of 323 progerin-positive muscle cells, 8.55% editing efficiency. Possibly also mention date August 31. Also maybe mention Hutchinson-Gilford progeria syndrome. Also mention FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles.
We need to select at most 4-5 double asterisk wrapped facts. Choose: August 31, Nature Communications, 322 of 323, 8.55%, maybe FATE? But gene names also need double asterisks? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So gene names like LMNA, progerin, farnesyl, FATE could be considered important. But we need to be selective, at most 4-5 total. So we need to decide which to highlight. Probably include: August 31, Nature Communications, 322 of 323, 8.55%, and maybe LMNA or FATE. But we need to keep within 4-5. Let's pick: August 31, Nature Communications, 322 of 323, 8.55%, LMNA. That's 5.
Now citations: study/journal citations and publication references in single underscores. So we need to wrap the journal name and date: Nature Communications, August 31. Or maybe Nature Communications and date separate? The rule: wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026). So we should put the whole citation: Nature Communications, August 31. That satisfies.
Now we need to produce headline under 90 chars, no markdown, no prefix '#'. Something like: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". Count characters: Let's count: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". That's 66? Let's count: D(1)N2A3 space4 e5d6i7t8o9r10 space11F12A13T14E15 space16d17e18t19a20c21h22e23s24 space25p26r27o28g29e30r31i32n33 space34f35r36o37m38 space39n40u41c42l43e44a45r46 space47e48n49v50e51l52o53p54e55 space56i57n58 space59h60u61m62a63n64 space65o66r67g68a69n70o71i72d73s74. So 74 chars, okay.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We need to preserve all facts, numbers, names, citations exactly. Do not add commentary.
Let's draft paragraphs.
Paragraph 1: Introduce FATE editor, described August 31 in Nature Communications, edits LMNA gene to prevent farnesylation of progerin.
Paragraph 2: Describe effect in neuromuscular organoids: progerin loses lipid tag, distributes in nucleus, DNA damage response markers γH2AX and 53BP1 form foci after irradiation; in muscle cells previously lacking foci, after editing they appear.
Paragraph 3: Provide specifics: In 322 of 323 progerin-positive muscle cells, no γH2AX foci before editing; 53BP1 retained at nuclear envelope. After FATE editing, foci reappear. Also mention that editing replaces one nucleotide in LMNA tail, preventing farnesyl attachment.
Paragraph 4: Mention that FATE also works in organoids with non-classic LMNA variant, freeing 53BP1 and restoring foci. Also that authors packaged FATE mRNA into lipid nanoparticles, microinjected into muscle part of organoids; after ten days editing efficiency up to 8.55%, progerin redistributed, DNA damage foci formed after irradiation.…
🔗 Read original →
We need to identify key facts: FATE DNA editor, described August 31 in Nature Communications. Gene LMNA, progerin, farnesyl. Numbers: 322 of 323 progerin-positive muscle cells, 8.55% editing efficiency. Possibly also mention date August 31. Also maybe mention Hutchinson-Gilford progeria syndrome. Also mention FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles.
We need to select at most 4-5 double asterisk wrapped facts. Choose: August 31, Nature Communications, 322 of 323, 8.55%, maybe FATE? But gene names also need double asterisks? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So gene names like LMNA, progerin, farnesyl, FATE could be considered important. But we need to be selective, at most 4-5 total. So we need to decide which to highlight. Probably include: August 31, Nature Communications, 322 of 323, 8.55%, and maybe LMNA or FATE. But we need to keep within 4-5. Let's pick: August 31, Nature Communications, 322 of 323, 8.55%, LMNA. That's 5.
Now citations: study/journal citations and publication references in single underscores. So we need to wrap the journal name and date: Nature Communications, August 31. Or maybe Nature Communications and date separate? The rule: wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026). So we should put the whole citation: Nature Communications, August 31. That satisfies.
Now we need to produce headline under 90 chars, no markdown, no prefix '#'. Something like: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". Count characters: Let's count: "DNA editor FATE detaches progerin from nuclear envelope in human organoids". That's 66? Let's count: D(1)N2A3 space4 e5d6i7t8o9r10 space11F12A13T14E15 space16d17e18t19a20c21h22e23s24 space25p26r27o28g29e30r31i32n33 space34f35r36o37m38 space39n40u41c42l43e44a45r46 space47e48n49v50e51l52o53p54e55 space56i57n58 space59h60u61m62a63n64 space65o66r67g68a69n70o71i72d73s74. So 74 chars, okay.
Now body: need short paragraphs 2-3 sentences each, separated by blank lines.
We need to preserve all facts, numbers, names, citations exactly. Do not add commentary.
Let's draft paragraphs.
Paragraph 1: Introduce FATE editor, described August 31 in Nature Communications, edits LMNA gene to prevent farnesylation of progerin.
Paragraph 2: Describe effect in neuromuscular organoids: progerin loses lipid tag, distributes in nucleus, DNA damage response markers γH2AX and 53BP1 form foci after irradiation; in muscle cells previously lacking foci, after editing they appear.
Paragraph 3: Provide specifics: In 322 of 323 progerin-positive muscle cells, no γH2AX foci before editing; 53BP1 retained at nuclear envelope. After FATE editing, foci reappear. Also mention that editing replaces one nucleotide in LMNA tail, preventing farnesyl attachment.
Paragraph 4: Mention that FATE also works in organoids with non-classic LMNA variant, freeing 53BP1 and restoring foci. Also that authors packaged FATE mRNA into lipid nanoparticles, microinjected into muscle part of organoids; after ten days editing efficiency up to 8.55%, progerin redistributed, DNA damage foci formed after irradiation.…
🔗 Read original →
PubMed Central (PMC)
Clinical Trial of Protein Farnesylation Inhibitors Lonafarnib, Pravastatin and Zoledronic Acid in Children with Hutchinson-Gilford…
Hutchinson-Gilford progeria syndrome is an extremely rare, fatal, segmental premature aging syndrome caused by a mutation in LMNA yielding the farnesylated aberrant protein, progerin. Without progerin-specific treatment, death occurs at an average ...
We need to translate Russian news post into natural English, format per rules.
First line: short headline under 90 chars. No markdown, no '#'.
Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. At most 4-5 per post.
Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026).
We need to preserve facts, numbers, names, citations exactly. No added commentary.
We need to identify key facts: date 31 августа (August 31) in Nature Communications described FATE. Gene LMNA, progerin, farnesyl, etc. Numbers: 322 of 323 progerin-positive muscle cells lacked γH2AX foci; 53BP1 retained at nuclear envelope. Also editing efficiency max 8.55% after ten days. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS). Also mention that FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles delivering mRNA.
We need to select at most 4-5 double-asterisk facts. Choose: date (August 31), journal Nature Communications, gene LMNA, progerin, farnesyl, numbers 322/323, editing efficiency 8.55%, maybe Hutchinson-Gilford progeria syndrome. But we need to be selective: at most 4-5. Let's pick: August 31, Nature Communications, LMNA, progerin, farnesyl, 322 of 323, 8.55%. That's many. Need max 4-5. Let's choose: August 31, Nature Communications, LMNA, progerin, 8.55% maybe also 322 of 323. That's 6. Need reduce.
We can combine some: maybe we can put date and journal together? But each double asterisk wraps a fact; we could put "August 31 in Nature Communications" but the citation should be underscores, not double asterisks. So date could be double asterisk, journal underscores. So date August 31 is one. Journal Nature Communications is underscores (not counted). Then we need gene name LMNA maybe double asterisk? Could be considered important. Progerin maybe double asterisk? Farnesyl maybe double asterisk? Number 322 of 323 maybe double asterisk. Editing efficiency 8.55% maybe double asterisk. That's many.
We need at most 4-5 double asterisk facts. Let's decide which are most important: date, journal (underscores), LMNA gene, progerin, farnesyl, editing efficiency, 322/323. Perhaps we can combine LMNA and progerin into one fact? Not really.
We could choose: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5. Date and journal can be in underscores? Date cannot be underscores because it's not a citation. But we could put date in double asterisk as well, but then exceed limit. Maybe we can omit date double asterisk and just mention date in plain text? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So dates should be double asterisk if we consider them important. But we can maybe not treat date as key fact? It's somewhat important but maybe we can skip double asterisk for date and just mention normally. The rule says wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. It implies we should wrap those categories, but we can be selective: at most 4-5 per post. So we need to choose which dates, numbers, etc to wrap. We can omit date wrapping if we stay within limit.
Thus we can wrap: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5.
Now we need to wrap study/journal citations in single underscores: Nature Communications. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS) but not wrap.…
🔗 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.
Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026).
We need to preserve facts, numbers, names, citations exactly. No added commentary.
We need to identify key facts: date 31 августа (August 31) in Nature Communications described FATE. Gene LMNA, progerin, farnesyl, etc. Numbers: 322 of 323 progerin-positive muscle cells lacked γH2AX foci; 53BP1 retained at nuclear envelope. Also editing efficiency max 8.55% after ten days. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS). Also mention that FATE replaces one letter in LMNA tail. Also mention lipid nanoparticles delivering mRNA.
We need to select at most 4-5 double-asterisk facts. Choose: date (August 31), journal Nature Communications, gene LMNA, progerin, farnesyl, numbers 322/323, editing efficiency 8.55%, maybe Hutchinson-Gilford progeria syndrome. But we need to be selective: at most 4-5. Let's pick: August 31, Nature Communications, LMNA, progerin, farnesyl, 322 of 323, 8.55%. That's many. Need max 4-5. Let's choose: August 31, Nature Communications, LMNA, progerin, 8.55% maybe also 322 of 323. That's 6. Need reduce.
We can combine some: maybe we can put date and journal together? But each double asterisk wraps a fact; we could put "August 31 in Nature Communications" but the citation should be underscores, not double asterisks. So date could be double asterisk, journal underscores. So date August 31 is one. Journal Nature Communications is underscores (not counted). Then we need gene name LMNA maybe double asterisk? Could be considered important. Progerin maybe double asterisk? Farnesyl maybe double asterisk? Number 322 of 323 maybe double asterisk. Editing efficiency 8.55% maybe double asterisk. That's many.
We need at most 4-5 double asterisk facts. Let's decide which are most important: date, journal (underscores), LMNA gene, progerin, farnesyl, editing efficiency, 322/323. Perhaps we can combine LMNA and progerin into one fact? Not really.
We could choose: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5. Date and journal can be in underscores? Date cannot be underscores because it's not a citation. But we could put date in double asterisk as well, but then exceed limit. Maybe we can omit date double asterisk and just mention date in plain text? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. So dates should be double asterisk if we consider them important. But we can maybe not treat date as key fact? It's somewhat important but maybe we can skip double asterisk for date and just mention normally. The rule says wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. It implies we should wrap those categories, but we can be selective: at most 4-5 per post. So we need to choose which dates, numbers, etc to wrap. We can omit date wrapping if we stay within limit.
Thus we can wrap: LMNA, progerin, farnesyl, 8.55%, 322 of 323. That's 5.
Now we need to wrap study/journal citations in single underscores: Nature Communications. Also maybe mention Hutchinson-Gilford progeria syndrome (HGPS) but not wrap.…
🔗 Read original →
PubMed Central (PMC)
Clinical Trial of Protein Farnesylation Inhibitors Lonafarnib, Pravastatin and Zoledronic Acid in Children with Hutchinson-Gilford…
Hutchinson-Gilford progeria syndrome is an extremely rare, fatal, segmental premature aging syndrome caused by a mutation in LMNA yielding the farnesylated aberrant protein, progerin. Without progerin-specific treatment, death occurs at an average ...
CAR-T cells may show second receptor before first recognizes target
On August 31 the authors posted a preprint describing a two‑step CAR‑T strategy in which the synthetic SynNotch receptor detects high HER2 density and then activates a CAR that kills the target. In some T cells the CAR appeared on the surface before the SynNotch signal arrived.
In three different constructs the basal CAR surface level was measured at 6%, 2%, and 0.2% of resting T cells. The construct with the highest background (6%) showed the poorest ability to discriminate between high‑ and low‑HER2 cells.
In a two‑tumor mouse model the high‑background construct reduced both high‑ and low‑HER2 tumors. After binding HER2, CAR triggers T‑cell proliferation, so even a small initial CAR‑positive fraction can shape the whole population. The authors’ mathematical model incorporated the fraction of such cells, HER2 density, and the T‑cell‑to‑target ratio; experiments indicated that optimal discrimination required different T‑cell doses for each construct.
To lower basal CAR expression they attached the mScarlet tag to the CAR C‑terminus, which cut the background by about 60% while preserving signal‑induced CAR. Degron tags reduced background further in culture, but T cells bearing degrons lost cytotoxic activity more quickly after CAR activation. In mouse models, mScarlet improved discrimination, suppressing high‑antigen tumors while low‑antigen tumors continued to grow.
For the two‑step CAR‑T design, both the timing of CAR expression and its residual level after proper signaling are crucial. Rohelo Hernandez‑Lopez et al., 2021
🔗 Read original →
On August 31 the authors posted a preprint describing a two‑step CAR‑T strategy in which the synthetic SynNotch receptor detects high HER2 density and then activates a CAR that kills the target. In some T cells the CAR appeared on the surface before the SynNotch signal arrived.
In three different constructs the basal CAR surface level was measured at 6%, 2%, and 0.2% of resting T cells. The construct with the highest background (6%) showed the poorest ability to discriminate between high‑ and low‑HER2 cells.
In a two‑tumor mouse model the high‑background construct reduced both high‑ and low‑HER2 tumors. After binding HER2, CAR triggers T‑cell proliferation, so even a small initial CAR‑positive fraction can shape the whole population. The authors’ mathematical model incorporated the fraction of such cells, HER2 density, and the T‑cell‑to‑target ratio; experiments indicated that optimal discrimination required different T‑cell doses for each construct.
To lower basal CAR expression they attached the mScarlet tag to the CAR C‑terminus, which cut the background by about 60% while preserving signal‑induced CAR. Degron tags reduced background further in culture, but T cells bearing degrons lost cytotoxic activity more quickly after CAR activation. In mouse models, mScarlet improved discrimination, suppressing high‑antigen tumors while low‑antigen tumors continued to grow.
For the two‑step CAR‑T design, both the timing of CAR expression and its residual level after proper signaling are crucial. Rohelo Hernandez‑Lopez et al., 2021
🔗 Read original →
CAR-T cells trigger macrophage iNOS, limiting lymphoma attack
In experiments on mice, CAR‑T cells were found to induce macrophages to express the enzyme iNOS, which weakens the attack on lymphoma. On 31 August the peer‑reviewed version of an article on large B‑cell lymphoma described how CAR‑T cells activate iNOS in macrophages, producing nitric oxide. The researchers studied axi‑cel, a CAR‑T therapy in which patient T cells receive a receptor that recognizes the CD19 protein on lymphoma cells.
In a 2021 study the same group linked short‑lived responses to axi‑cel with tumor interferon signals and suppressive myeloid cells. Pretreatment biopsies showed that patients whose response did not persist had a higher relative proportion of M2‑like macrophages — immune cells capable of dampening the immune response — as revealed by RNA analysis. This association pointed to where the mechanism might lie.
In a laboratory model the authors combined mouse CAR‑T cells, B‑cell tumor cells, and M2‑like macrophages. After recognizing tumor cells, CAR‑T released IFN‑γ, a signaling protein of the immune system. IFN‑γ turned on iNOS in the macrophages; the enzyme produced nitric oxide, which slowed CAR‑T cell proliferation and weakened their ability to kill tumor cells.
The authors verified this pathway in two ways. The iNOS blocker L‑NIL and genetic deletion of the NOS2 gene in macrophages restored CAR‑T cells’ capacity to proliferate and kill tumor cells. Conversely, a nitric‑oxide‑donating compound suppressed their activity again.
In a mouse model of B‑cell lymphoma, the combination of CAR‑T and L‑NIL extended survival compared with CAR‑T alone. During leukapheresis — the procedure that isolates cells from blood to manufacture the therapy — patients whose response did not last had a higher fraction of CD14‑positive monocytes expressing iNOS. The authors view iNOS as a possible target for future clinical studies of CAR‑T.
🔗 Read original →
In experiments on mice, CAR‑T cells were found to induce macrophages to express the enzyme iNOS, which weakens the attack on lymphoma. On 31 August the peer‑reviewed version of an article on large B‑cell lymphoma described how CAR‑T cells activate iNOS in macrophages, producing nitric oxide. The researchers studied axi‑cel, a CAR‑T therapy in which patient T cells receive a receptor that recognizes the CD19 protein on lymphoma cells.
In a 2021 study the same group linked short‑lived responses to axi‑cel with tumor interferon signals and suppressive myeloid cells. Pretreatment biopsies showed that patients whose response did not persist had a higher relative proportion of M2‑like macrophages — immune cells capable of dampening the immune response — as revealed by RNA analysis. This association pointed to where the mechanism might lie.
In a laboratory model the authors combined mouse CAR‑T cells, B‑cell tumor cells, and M2‑like macrophages. After recognizing tumor cells, CAR‑T released IFN‑γ, a signaling protein of the immune system. IFN‑γ turned on iNOS in the macrophages; the enzyme produced nitric oxide, which slowed CAR‑T cell proliferation and weakened their ability to kill tumor cells.
The authors verified this pathway in two ways. The iNOS blocker L‑NIL and genetic deletion of the NOS2 gene in macrophages restored CAR‑T cells’ capacity to proliferate and kill tumor cells. Conversely, a nitric‑oxide‑donating compound suppressed their activity again.
In a mouse model of B‑cell lymphoma, the combination of CAR‑T and L‑NIL extended survival compared with CAR‑T alone. During leukapheresis — the procedure that isolates cells from blood to manufacture the therapy — patients whose response did not last had a higher fraction of CD14‑positive monocytes expressing iNOS. The authors view iNOS as a possible target for future clinical studies of CAR‑T.
🔗 Read original →
Nature
IFN-γ-driven iNOS induction in macrophages mediates CAR T cell resistance in B cell lymphoma
Nature Communications - Tumor associated macrophages can support cancer progression by suppressing T cell effector functions. Here the authors report that induction of iNOS in tumor-associated...
NPR estimates US heat-related deaths about five times higher than official count
NPR, together with Boston University’s Center for Climate and Health, calculated that heat was linked to roughly about 9,000 deaths per year in the United States from 2018–2025. In contrast, death certificates recorded heat as a contributing factor in only about 1,700 cases annually over the same period.
The researchers used weekly mortality data from 530 counties that together represent ~73% of the US population, excluding 2020 due to the pandemic. They matched each week’s temperature—and the following week’s—to death counts, comparing local temperatures to each area’s normal climate to find the mortality‑minimum temperature for each state.
By aggregating county‑level estimates and scaling them to the continental United States, the model produced a national figure of approximately nine thousand heat‑associated deaths per year. In Maricopa County, where Phoenix is located, investigators already ask about air‑conditioning, electricity bills and medications that increase heat sensitivity when probing deaths.
Because different counting methods yield vastly different numbers, the way heat‑related mortality is measured directly influences which risks are prioritized for prevention efforts.
🔗 Read original →
NPR, together with Boston University’s Center for Climate and Health, calculated that heat was linked to roughly about 9,000 deaths per year in the United States from 2018–2025. In contrast, death certificates recorded heat as a contributing factor in only about 1,700 cases annually over the same period.
The researchers used weekly mortality data from 530 counties that together represent ~73% of the US population, excluding 2020 due to the pandemic. They matched each week’s temperature—and the following week’s—to death counts, comparing local temperatures to each area’s normal climate to find the mortality‑minimum temperature for each state.
By aggregating county‑level estimates and scaling them to the continental United States, the model produced a national figure of approximately nine thousand heat‑associated deaths per year. In Maricopa County, where Phoenix is located, investigators already ask about air‑conditioning, electricity bills and medications that increase heat sensitivity when probing deaths.
Because different counting methods yield vastly different numbers, the way heat‑related mortality is measured directly influences which risks are prioritized for prevention efforts.
🔗 Read original →
NPR
NPR spent 2 years tracking deaths from heat. We found a staggering hidden toll
An NPR investigation found that people in the U.S. are dying from heat much more often than official counts show. We explore the reasons why — and how lives can be saved.
Breathing 3D Model of Artificial Lung Mimics Soap‑Bubble Physics
The problem with existing lung‑on‑a‑chip devices was that cells were cultured on flat surfaces of synthetic PDMS, which does not mimic real tissue, and attempts to make membranes from soft hydrogels caused them to rupture instantly when stretched.
Korean researchers solved this by immersing a special mold in a composite hydrogel solution and drawing out an ultrathin, yet elastic and strong film—just as a soap bubble is blown through a straw.
Using 3D‑bioprinting, they rebuilt the three‑layer alveolar structure by sequentially depositing a vascular cell layer, a basement membrane, and an epithelial cell layer. To make the construct expand and contract like a real inhale and exhale, the team built a pneumatic drive system that cyclically changes pressure beneath the ultrathin hydrogel membrane, imitating the movement of the human diaphragm.
In testing, the artificial lung demonstrated high reliability, operating stably for about 240,000 breath cycles. Experiments with the influenza virus revealed that cellular inflammatory and antiviral responses change dramatically depending on whether the tissue is in motion or remains still. Because the system lets researchers freely set breath depth and frequency, they can now recreate both healthy lung conditions and various pathological processes in the lab, paving the way for more precise drug testing for viral and lung diseases without relying on animal tests or simple flat cell cultures.
🔗 Read original →
The problem with existing lung‑on‑a‑chip devices was that cells were cultured on flat surfaces of synthetic PDMS, which does not mimic real tissue, and attempts to make membranes from soft hydrogels caused them to rupture instantly when stretched.
Korean researchers solved this by immersing a special mold in a composite hydrogel solution and drawing out an ultrathin, yet elastic and strong film—just as a soap bubble is blown through a straw.
Using 3D‑bioprinting, they rebuilt the three‑layer alveolar structure by sequentially depositing a vascular cell layer, a basement membrane, and an epithelial cell layer. To make the construct expand and contract like a real inhale and exhale, the team built a pneumatic drive system that cyclically changes pressure beneath the ultrathin hydrogel membrane, imitating the movement of the human diaphragm.
In testing, the artificial lung demonstrated high reliability, operating stably for about 240,000 breath cycles. Experiments with the influenza virus revealed that cellular inflammatory and antiviral responses change dramatically depending on whether the tissue is in motion or remains still. Because the system lets researchers freely set breath depth and frequency, they can now recreate both healthy lung conditions and various pathological processes in the lab, paving the way for more precise drug testing for viral and lung diseases without relying on animal tests or simple flat cell cultures.
🔗 Read original →
The Advanced Portfolio
Ultrathin Hydrogel Membranes Inspired by Soap Films Enable Physiologically Relevant Breathing Lung Models
A soap-film inspired strategy enables the fabrication of ultrathin yet robust GelMA-PAAm membranes for breathing lung culture. The hydrogel membrane supports long-term cyclic deformation under air-li...
We need to translate Russian news post into English, format per rules.
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g. Nature Aging, July 2026).
We need to preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: Malva, RNA sequence search, data of 74 million single cells, August 26, journal Nature published article about Malva, index of raw RNA reads from single cells. It links short sequence fragments with barcode — label of specific cell, so query finds cells with needed mutation, RNA junction, pathogen trace and shows their tissue, age, disease, original study. Single-cell sequencing reads RNA of each cell separately. Usually from these reads they make a table: how many RNA of known genes per cell. Such table helps compare cells, but question about specific mutation, viral RNA or novel RNA junction requires returning to huge array of raw reads. Malva stores these reads in an index. It splits each read into fragments of 24 nucleotides — letters of genetic sequence — and records cell barcode next to it. Query by sequence returns cells where its fragments met, together with info about them. Indexes of individual samples can be combined, therefore database is supplemented with new data. In version described in article, index covered about 74 million cells from thousands of experiments. Search for a transcript — RNA copy of a gene — length 1000 nucleotides took 0.9 seconds on a single CPU core. Search result — pseudocounter: number of matched reads instead of RNA molecule quantity. Authors compared such pseudocounters with ordinary RNA counts, then looked for sequence variants, RNA junctions, transcript ends. In a small lung cancer sample Malva found mutations in EGFR gene; separate check covered 280 samples of 16 tumor types. These tests show that fast search finds biological signals in cellular data. Researcher can search for sequence linked to hypothesis in already accumulated public data and see in which tissues, ages and disease states it occurs.…
🔗 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 important facts: Malva, RNA sequence search, data of 74 million single cells, August 26, journal Nature published article about Malva, index of raw RNA reads from single cells. It links short sequence fragments with barcode — label of specific cell, so query finds cells with needed mutation, RNA junction, pathogen trace and shows their tissue, age, disease, original study. Single-cell sequencing reads RNA of each cell separately. Usually from these reads they make a table: how many RNA of known genes per cell. Such table helps compare cells, but question about specific mutation, viral RNA or novel RNA junction requires returning to huge array of raw reads. Malva stores these reads in an index. It splits each read into fragments of 24 nucleotides — letters of genetic sequence — and records cell barcode next to it. Query by sequence returns cells where its fragments met, together with info about them. Indexes of individual samples can be combined, therefore database is supplemented with new data. In version described in article, index covered about 74 million cells from thousands of experiments. Search for a transcript — RNA copy of a gene — length 1000 nucleotides took 0.9 seconds on a single CPU core. Search result — pseudocounter: number of matched reads instead of RNA molecule quantity. Authors compared such pseudocounters with ordinary RNA counts, then looked for sequence variants, RNA junctions, transcript ends. In a small lung cancer sample Malva found mutations in EGFR gene; separate check covered 280 samples of 16 tumor types. These tests show that fast search finds biological signals in cellular data. Researcher can search for sequence linked to hypothesis in already accumulated public data and see in which tissues, ages and disease states it occurs.…
🔗 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...
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
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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…