We need to translate Russian news post into English, format: first line short headline under 90 chars, no markdown. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines. Wrap few genuinely important facts (key numbers, percentages, drug/company/gene names, dates) in double asterisks. At most 4-5 per post. Wrap study/journal citations and publication references in single underscores (e.g., Nature Aging, July 2026). Must preserve all facts, numbers, names, citations exactly. No commentary. No source link.
We need to identify key facts: date: 1 September in Cell (the paper). Gene MMLV Gag. Numbers: 12,931 genes found in lysed cells; 11,598 also in medium; correlation coefficient 0.797. Four-day differentiation: 772 genes with changed activity. Microfluidic: seven days, six devices, would need 42 independent if destructive. Also mention control: shortened Gag gave less RNA.
We need to limit double asterisks to at most 4-5 facts. Choose maybe: 1 September, Cell, MMLV Gag, 12,931 genes, 11,598 genes, 0.797 correlation, 772 genes, six devices, 42 independent. That's many; need only 4-5. So select most important: date, journal, gene name, correlation coefficient, number of genes changed in differentiation, maybe number of devices vs destructive. But we need to keep facts but can't overdo double asterisks. We can embed some numbers without double asterisks? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post, never a whole sentence. So we must choose which facts to bold. Others remain plain text but still must be preserved. So we need to include all facts but only some get double asterisks. Choose maybe: 1 September, Cell, MMLV Gag, 0.797, 772 genes. That's 5. Could also include six devices but then exceed. We'll stick with those five.
Now need to wrap study/journal citations in single underscores. The citation is the paper: "в Cell вышла работа". So we need Cell maybe with date? The format: Nature Aging, July 2026. So we should put Cell, September 1? Actually the paper published 1 September in Cell. So we can put Cell, September 1. That satisfies citation.
Now produce headline under 90 chars. Something like: "Live-cell RNA release lets researchers repeatedly read gene activity from same culture". Count characters: Let's count: "Live-cell RNA release lets researchers repeatedly read gene activity from same culture". Count: L(1) i2 v3 e4 -5 c6 e7 l8 l9 space10 R11 N12 A13 space14 r15 e16 l17 e18 a19 s20 e21 space22 l23 e24 t25 s26 space27 r28 e29 s30 e31 a32 r33 c34 h35 e36 r37 s38 space39 r40 e41 p42 e43 a44 t45 e46 d47 l48 y49 space50 r51 e52 a53 d54 space55 g56 e57 n58 e59 space60 a61 c62 t63 i64 v65 i66 t67 y68 space69 f70 r71 o72 m73 space74 s75 a76 m77 e78 space79 c80 u81 l82 t83 u84 r85 e86. That's 86 characters. Good under 90.
Now blank line, then body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft paragraphs:
Paragraph 1: Introduce method: On 1 September, Cell published a study describing a way to repeatedly measure gene activity in a single living cell culture. Researchers introduced a genetic construct encoding the MMLV Gag protein, which causes cells to package cytoplasmic RNA into virus‑like particles and release them into the medium.
Paragraph 2: Explain sampling: The released RNA accumulates in the medium between samplings, providing a readout of the transcriptome while the culture remains intact for further measurements. A control using a truncated Gag that cannot form particles showed markedly less RNA in the medium.…
🔗 Read original →
We need to identify key facts: date: 1 September in Cell (the paper). Gene MMLV Gag. Numbers: 12,931 genes found in lysed cells; 11,598 also in medium; correlation coefficient 0.797. Four-day differentiation: 772 genes with changed activity. Microfluidic: seven days, six devices, would need 42 independent if destructive. Also mention control: shortened Gag gave less RNA.
We need to limit double asterisks to at most 4-5 facts. Choose maybe: 1 September, Cell, MMLV Gag, 12,931 genes, 11,598 genes, 0.797 correlation, 772 genes, six devices, 42 independent. That's many; need only 4-5. So select most important: date, journal, gene name, correlation coefficient, number of genes changed in differentiation, maybe number of devices vs destructive. But we need to keep facts but can't overdo double asterisks. We can embed some numbers without double asterisks? The rule: wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post, never a whole sentence. So we must choose which facts to bold. Others remain plain text but still must be preserved. So we need to include all facts but only some get double asterisks. Choose maybe: 1 September, Cell, MMLV Gag, 0.797, 772 genes. That's 5. Could also include six devices but then exceed. We'll stick with those five.
Now need to wrap study/journal citations in single underscores. The citation is the paper: "в Cell вышла работа". So we need Cell maybe with date? The format: Nature Aging, July 2026. So we should put Cell, September 1? Actually the paper published 1 September in Cell. So we can put Cell, September 1. That satisfies citation.
Now produce headline under 90 chars. Something like: "Live-cell RNA release lets researchers repeatedly read gene activity from same culture". Count characters: Let's count: "Live-cell RNA release lets researchers repeatedly read gene activity from same culture". Count: L(1) i2 v3 e4 -5 c6 e7 l8 l9 space10 R11 N12 A13 space14 r15 e16 l17 e18 a19 s20 e21 space22 l23 e24 t25 s26 space27 r28 e29 s30 e31 a32 r33 c34 h35 e36 r37 s38 space39 r40 e41 p42 e43 a44 t45 e46 d47 l48 y49 space50 r51 e52 a53 d54 space55 g56 e57 n58 e59 space60 a61 c62 t63 i64 v65 i66 t67 y68 space69 f70 r71 o72 m73 space74 s75 a76 m77 e78 space79 c80 u81 l82 t83 u84 r85 e86. That's 86 characters. Good under 90.
Now blank line, then body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft paragraphs:
Paragraph 1: Introduce method: On 1 September, Cell published a study describing a way to repeatedly measure gene activity in a single living cell culture. Researchers introduced a genetic construct encoding the MMLV Gag protein, which causes cells to package cytoplasmic RNA into virus‑like particles and release them into the medium.
Paragraph 2: Explain sampling: The released RNA accumulates in the medium between samplings, providing a readout of the transcriptome while the culture remains intact for further measurements. A control using a truncated Gag that cannot form particles showed markedly less RNA in the medium.…
🔗 Read original →
Cell
Live-cell transcriptomics with engineered virus-like particles
Najia, Le, Borrajo et al. establish “cellular self-reporting,” a technology to export
molecular analytes from cells in virus-like particles. The authors demonstrate how
mRNA export enables longitudinal transcriptome-wide profiling of living cells by sampling…
molecular analytes from cells in virus-like particles. The authors demonstrate how
mRNA export enables longitudinal transcriptome-wide profiling of living cells by sampling…
Netherlands Reverses Plan to Fully Fund Animal-Free Methods at Primate Center
On July 3, Minister Rianne Letsche rt wrote to parliament that the Biomedical Primate Research Centre (BPRC) can again allocate its institutional grant based on scientific need, after the House of Representatives reversed the earlier plan in March and the Senate confirmed the reversal on June 30. The previous plan would have directed the entire institutional subsidy to animal‑free methods by 2030, making long‑term project planning difficult for the centre.
Now BPRC must distribute the grant weighing scientific need and the availability of suitable animal‑free methods; the government has limited primate experiments to 120–150 per year and requires the share of the institutional grant for developing and validating animal‑free methods to increase from 17% to 30% by 2030.
BPRC houses about 1,000 macaques and is one of only two EU organisations with its own primate breeding colony; the ministry ties the centre’s work to infection, brain disease, and drug research, and obliges BPRC to collaborate with developers of animal‑free methods and share data with them.
On September 2, The Transmitter reported that parliament will revisit animal‑research funding this autumn and may propose budget changes; centre director Merel Langeleer said BPRC is now fully dependent on policy and elections, and the government plans an independent evaluation of further reductions in primate experiments by 2030.
🔗 Read original →
On July 3, Minister Rianne Letsche rt wrote to parliament that the Biomedical Primate Research Centre (BPRC) can again allocate its institutional grant based on scientific need, after the House of Representatives reversed the earlier plan in March and the Senate confirmed the reversal on June 30. The previous plan would have directed the entire institutional subsidy to animal‑free methods by 2030, making long‑term project planning difficult for the centre.
Now BPRC must distribute the grant weighing scientific need and the availability of suitable animal‑free methods; the government has limited primate experiments to 120–150 per year and requires the share of the institutional grant for developing and validating animal‑free methods to increase from 17% to 30% by 2030.
BPRC houses about 1,000 macaques and is one of only two EU organisations with its own primate breeding colony; the ministry ties the centre’s work to infection, brain disease, and drug research, and obliges BPRC to collaborate with developers of animal‑free methods and share data with them.
On September 2, The Transmitter reported that parliament will revisit animal‑research funding this autumn and may propose budget changes; centre director Merel Langeleer said BPRC is now fully dependent on policy and elections, and the government plans an independent evaluation of further reductions in primate experiments by 2030.
🔗 Read original →
China proposes national dual-use research risk management system
On 31 August, the journal Frontiers in Bioengineering and Biotechnology published a review by Ruihan Zhang. The author proposes a national system for managing risks of dual‑use life‑science research. Dual‑use research is legitimate work whose results could be misused for harm.
Zhang divides risk into five pathways and allocates eleven measures across three stages of implementation. The pathways begin with different triggering events, such as a laboratory accident, insider misuse, cyberattack, work outside conventional institutions, and publications that facilitate dangerous application.
The first stage includes a general regulatory framework, project evaluation by scientific organisations, and epidemiological surveillance of unusual health events. These measures split responsibility between research institutions and the health system.
At the second stage, laboratories introduce daily risk management, response plans, protection for medical and lab staff, access‑misuse controls, and outreach to the public.
The third stage covers protection of digital systems, rules for responsible publication, and international exchange of experience. This structure shows who evaluates projects, who detects anomalous cases, and who answers when risk extends beyond the lab walls.
Separately, Zhang incorporates physicians, laboratory personnel, and response services into the system, stressing that safety depends on the people who first encounter a threat and on the coordination of their actions.
🔗 Read original →
On 31 August, the journal Frontiers in Bioengineering and Biotechnology published a review by Ruihan Zhang. The author proposes a national system for managing risks of dual‑use life‑science research. Dual‑use research is legitimate work whose results could be misused for harm.
Zhang divides risk into five pathways and allocates eleven measures across three stages of implementation. The pathways begin with different triggering events, such as a laboratory accident, insider misuse, cyberattack, work outside conventional institutions, and publications that facilitate dangerous application.
The first stage includes a general regulatory framework, project evaluation by scientific organisations, and epidemiological surveillance of unusual health events. These measures split responsibility between research institutions and the health system.
At the second stage, laboratories introduce daily risk management, response plans, protection for medical and lab staff, access‑misuse controls, and outreach to the public.
The third stage covers protection of digital systems, rules for responsible publication, and international exchange of experience. This structure shows who evaluates projects, who detects anomalous cases, and who answers when risk extends beyond the lab walls.
Separately, Zhang incorporates physicians, laboratory personnel, and response services into the system, stressing that safety depends on the people who first encounter a threat and on the coordination of their actions.
🔗 Read original →
Frontiers
Frontiers | A national framework for managing dual-use research of concern: integrating biosecurity, public health, and research…
Dual-use research of concern (DURC)—legitimate life-science research that could be misapplied to cause significant harm—has become an increasingly urgent pol...
Tabula Sapiens 2.0 Atlas Maps 1.1 Million Cells Across 28 Tissues
On September 2, the journal Cell, September 2 published an article describing the second version of the Tabula Sapiens atlas, which now includes data from 1.1 million cells across 28 human tissues. The update added nine donors and four tissues, and examined RNA profiles of cells that have stopped dividing.
RNA molecules are working copies of genes, showing which genes a cell is currently reading. Because comparing organs across individuals can mix tissue differences with donor‑specific variation and sample handling, the researchers took multiple organs from four of the nine new donors to isolate tissue effects.
To study stable cell‑division arrest (senescence), they defined a working condition: a cell must contain RNA of CDKN2A (a cell‑cycle‑arrest gene) and lack RNA of MKI67 (a marker of active division). Using this criterion they isolated 48,114 cells from 25 tissues of 21 donors.
Within this
🔗 Read original →
On September 2, the journal Cell, September 2 published an article describing the second version of the Tabula Sapiens atlas, which now includes data from 1.1 million cells across 28 human tissues. The update added nine donors and four tissues, and examined RNA profiles of cells that have stopped dividing.
RNA molecules are working copies of genes, showing which genes a cell is currently reading. Because comparing organs across individuals can mix tissue differences with donor‑specific variation and sample handling, the researchers took multiple organs from four of the nine new donors to isolate tissue effects.
To study stable cell‑division arrest (senescence), they defined a working condition: a cell must contain RNA of CDKN2A (a cell‑cycle‑arrest gene) and lack RNA of MKI67 (a marker of active division). Using this criterion they isolated 48,114 cells from 25 tissues of 21 donors.
Within this
🔗 Read original →
We need to translate Russian news post into natural English, format per rules.
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body paragraphs short (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores.
We need to preserve facts: Tabula Sapiens 2.0 collected atlas of 1.1 million cells from 28 human tissues. On September 2, article about second version of Tabula Sapiens published in journal Cell. They added nine donors and four tissues, and studied RNA profiles of cells with division arrest. RNA are working copies of genes: they show which genes a cell is currently reading. Inter-organ comparisons easily mix tissue properties with differences between individuals and sample processing. Therefore, for four of the nine new donors they took multiple organs. Comparing tissues from same person allows researchers to more clearly separate tissue contribution from donor-related differences. For analysis of senescence — stable cell division arrest — authors set working condition: cell has RNA copy of gene CDKN2A, associated with cell cycle arrest, and lacks RNA copy of MKI67, indicator of active division. Thus they isolated 48,114 cells from 25 tissues of 21 donors. For each combination of cell type, tissue, and donor, their RNA compared with other cells of same group. In this sample they found 3,792 genes with elevated RNA levels. After CDKN2A, the most common was CDKN2B, also related to cell cycle arrest: it increased in 18 of 30 major cell groups. CXCL8 and MIF increased in five such groups, SERPINE1 — in two. Then authors combined these genes into 17 programs — groups of genes whose RNA levels change together. In epithelial cells forming organ coverings and linings, and fibroblasts creating tissue support, adhesion programs are more noticeable. In immune, epithelial, and stem cells, energy exchange programs and systems that assemble and disassemble proteins changed differently. The working criterion gathered the overall sample, and its RNA programs primarily differed together with cell type.
We need to select key facts for double asterisks: numbers: 1.1 million cells, 28 tissues, September 2, Cell journal, nine donors, four tissues, 48,114 cells, 25 tissues, 21 donors, 3,792 genes, CDKN2A, CDKN2B, CXCL8, MIF, SERPINE1, 18 of 30 major cell groups, five groups, two groups, 17 programs. That's many. We need at most 4-5 double asterisks. So we need to be selective: maybe highlight total cells, date, journal, number of donors/tissues added, number of senescent cells isolated, number of elevated genes, maybe top gene. Choose 4-5.
We need to wrap study/journal citations in single underscores: e.g., Cell, September 2 or Cell? The citation: article published in Cell on September 2. So we can put Cell, September 2.
Now produce headline: maybe "Tabula Sapiens 2.0 Atlas Maps 1.1 Million Cells Across 28 Human Tissues". Count characters: Let's count: "Tabula Sapiens 2.0 Atlas Maps 1.1 Million Cells Across 28 Human Tissues". Count: T(1) a2 b3 u4 l5 a6 space7 S8 a9 p10 i11 e12 n13 s14 space15 1 16 . 17 2 18 . 19 0 20 space21 A22 t23 l24 a25 s26 space27 M28 a29 p30 s31 space32 1 33 . 34 1 35 space36 M37 i38 l39 l40 i41 o42 n43 space44 C45 e46 l47 l48 s49 space50 A51 c52 r53 o54 s55 s56 space57 2 58 8 59 space60 H61 u62 m63 a64 n65 space66 T67 i68 s69 s70 u71 e72 s73. That's 73 characters. Under 90, good.
Now blank line.
Now body paragraphs: maybe 3-4 paragraphs each 2-3 sentences.
Paragraph 1: Introduce publication and dataset.…
🔗 Read original →
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body paragraphs short (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores.
We need to preserve facts: Tabula Sapiens 2.0 collected atlas of 1.1 million cells from 28 human tissues. On September 2, article about second version of Tabula Sapiens published in journal Cell. They added nine donors and four tissues, and studied RNA profiles of cells with division arrest. RNA are working copies of genes: they show which genes a cell is currently reading. Inter-organ comparisons easily mix tissue properties with differences between individuals and sample processing. Therefore, for four of the nine new donors they took multiple organs. Comparing tissues from same person allows researchers to more clearly separate tissue contribution from donor-related differences. For analysis of senescence — stable cell division arrest — authors set working condition: cell has RNA copy of gene CDKN2A, associated with cell cycle arrest, and lacks RNA copy of MKI67, indicator of active division. Thus they isolated 48,114 cells from 25 tissues of 21 donors. For each combination of cell type, tissue, and donor, their RNA compared with other cells of same group. In this sample they found 3,792 genes with elevated RNA levels. After CDKN2A, the most common was CDKN2B, also related to cell cycle arrest: it increased in 18 of 30 major cell groups. CXCL8 and MIF increased in five such groups, SERPINE1 — in two. Then authors combined these genes into 17 programs — groups of genes whose RNA levels change together. In epithelial cells forming organ coverings and linings, and fibroblasts creating tissue support, adhesion programs are more noticeable. In immune, epithelial, and stem cells, energy exchange programs and systems that assemble and disassemble proteins changed differently. The working criterion gathered the overall sample, and its RNA programs primarily differed together with cell type.
We need to select key facts for double asterisks: numbers: 1.1 million cells, 28 tissues, September 2, Cell journal, nine donors, four tissues, 48,114 cells, 25 tissues, 21 donors, 3,792 genes, CDKN2A, CDKN2B, CXCL8, MIF, SERPINE1, 18 of 30 major cell groups, five groups, two groups, 17 programs. That's many. We need at most 4-5 double asterisks. So we need to be selective: maybe highlight total cells, date, journal, number of donors/tissues added, number of senescent cells isolated, number of elevated genes, maybe top gene. Choose 4-5.
We need to wrap study/journal citations in single underscores: e.g., Cell, September 2 or Cell? The citation: article published in Cell on September 2. So we can put Cell, September 2.
Now produce headline: maybe "Tabula Sapiens 2.0 Atlas Maps 1.1 Million Cells Across 28 Human Tissues". Count characters: Let's count: "Tabula Sapiens 2.0 Atlas Maps 1.1 Million Cells Across 28 Human Tissues". Count: T(1) a2 b3 u4 l5 a6 space7 S8 a9 p10 i11 e12 n13 s14 space15 1 16 . 17 2 18 . 19 0 20 space21 A22 t23 l24 a25 s26 space27 M28 a29 p30 s31 space32 1 33 . 34 1 35 space36 M37 i38 l39 l40 i41 o42 n43 space44 C45 e46 l47 l48 s49 space50 A51 c52 r53 o54 s55 s56 space57 2 58 8 59 space60 H61 u62 m63 a64 n65 space66 T67 i68 s69 s70 u71 e72 s73. That's 73 characters. Under 90, good.
Now blank line.
Now body paragraphs: maybe 3-4 paragraphs each 2-3 sentences.
Paragraph 1: Introduce publication and dataset.…
🔗 Read original →
Bacterial Patterns Shift with Moving Frog Embryon and Xenobot, Machine Model Distinguishes Conditions
On September 3, a preprint reported that motile *Bacillus subtilis* bacteria altered their pattern in fluid near a frog embryo and a xenobot — a body assembled from embryonic cells. When the embryo was moved, the bacterial accumulation zone moved with it. The bacterial pattern allowed a computer model to distinguish three conditions: embryo, xenobot, and bacteria‑only culture.
Researchers tested whether the bacterial spatial pattern contains information about a neighboring living system, comparing the frog embryo and xenobot (both share the same species and genome, but the xenobot is assembled separately and develops differently). In bacteria‑only culture, motile cells self‑organized into a branching pattern; a non‑motile strain and fluorescent microparticles hardly produced this pattern. Near the xenobot, motile bacteria formed a glowing halo whose average area reached 9.4 mm² after one hour, while in the two control groups it remained at hundredths of a square millimeter. This linked the halo to active bacterial behavior.
Near the living embryo the halo grew; near a heat‑inactivated embryo the early accumulation then receded. When the embryo was translocated, the bacterial cluster disappeared from the original site and appeared at the new location, showing that the bacterial pattern changes with the target’s position and state.
Researchers then examined the ionic environment. Raising potassium chloride concentration from 1.5 to 200 mmol/L increased the halo area to 23.0 mm² by the 11th hour, up from 0.67 mm² at baseline. Bacteria lacking the YugO potassium‑channel protein showed slower early accumulation, indicating that the ionic milieu modulates the early bacterial response to the living target.
To force the model to rely on the bacterial pattern, the target’s silhouette was masked in the frames. From the remaining bacterial distribution on deposited rollers the model distinguished the three conditions with 61.13% accuracy, compared with a baseline of 33%. The authors conclude that “the physiological state of one collective is partially recorded in the shape of another,” showing that bacterial positioning serves as a measurable readout of the state and position of a neighboring multicellular target.
🔗 Read original →
On September 3, a preprint reported that motile *Bacillus subtilis* bacteria altered their pattern in fluid near a frog embryo and a xenobot — a body assembled from embryonic cells. When the embryo was moved, the bacterial accumulation zone moved with it. The bacterial pattern allowed a computer model to distinguish three conditions: embryo, xenobot, and bacteria‑only culture.
Researchers tested whether the bacterial spatial pattern contains information about a neighboring living system, comparing the frog embryo and xenobot (both share the same species and genome, but the xenobot is assembled separately and develops differently). In bacteria‑only culture, motile cells self‑organized into a branching pattern; a non‑motile strain and fluorescent microparticles hardly produced this pattern. Near the xenobot, motile bacteria formed a glowing halo whose average area reached 9.4 mm² after one hour, while in the two control groups it remained at hundredths of a square millimeter. This linked the halo to active bacterial behavior.
Near the living embryo the halo grew; near a heat‑inactivated embryo the early accumulation then receded. When the embryo was translocated, the bacterial cluster disappeared from the original site and appeared at the new location, showing that the bacterial pattern changes with the target’s position and state.
Researchers then examined the ionic environment. Raising potassium chloride concentration from 1.5 to 200 mmol/L increased the halo area to 23.0 mm² by the 11th hour, up from 0.67 mm² at baseline. Bacteria lacking the YugO potassium‑channel protein showed slower early accumulation, indicating that the ionic milieu modulates the early bacterial response to the living target.
To force the model to rely on the bacterial pattern, the target’s silhouette was masked in the frames. From the remaining bacterial distribution on deposited rollers the model distinguished the three conditions with 61.13% accuracy, compared with a baseline of 33%. The authors conclude that “the physiological state of one collective is partially recorded in the shape of another,” showing that bacterial positioning serves as a measurable readout of the state and position of a neighboring multicellular target.
🔗 Read original →
bioRxiv
Living multicellular systems induce decodable spatial patterns in bacterial collectives
Living systems continuously modify their environments through chemical, mechanical, metabolic and bioelectrical activity. Whether a presence of a multicellular system can be encoded into the emergent spatial organization of another living collective in a…
We need to translate Russian news post into natural English, format per rules.
First line: short headline under 90 chars, no markdown. Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026).
We need to preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: numbers like 186, 70 million, 4680, 600, 246, 40 fixed additions, 77 rational coefficients, >1/50000 margin, August 2025? Actually date: "30 августа OpenAI опубликовала работу" => August 30 (year unspecified, likely 2025?). The original text: "OpenAI опубликовала результат GPT-6 Astra: бесконечно многие соседние простые различаются не более чем на 186 30 августа OpenAI опубликовала работу о коротких промежутках между простыми." So date: August 30 (maybe 2024?). We'll keep as "August 30". Also mention "2013 year Ethan Zhang", "Polymath projects", "James Maynard", "next Polymath project", "OpenAI work reduces bound to 186". Also mention "Axiom Math formalized previous bound 246 in August". Also mention "Lean 4 code", "numerical certificate", "Selberg sieve", etc.
We need to wrap key numbers in double asterisks: maybe 186, 70 million, 4680, 600, 246, 40, 77, >1/50000. That's many; we need at most 4-5. So we must be selective: choose the most important: the bound 186, the previous bound 246, the improvement from 70 million to 186 maybe mention 70 million as historical. But we can only have 4-5 total. Let's pick: 186, 246, 70 million, 40 (fixed additions), maybe 77 rational coefficients. That's 5.
We must not wrap whole sentences; just wrap the numbers.
Citations: we have references to work by OpenAI, Polymath, James Maynard, Axiom Math formalization, Lean checking. We need to wrap study/journal citations and publication references in single underscores. There's no explicit journal name; maybe we treat the work as a paper? Could use OpenAI work, August 30? But that's not a journal. The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." So we need to identify any such citations. The text mentions "OpenAI опубликовала работу", "В августе Axiom Math формализовала прежнюю границу 246 ; там Lean проверял цепочку уже известной теоремы." Also "В новом репозитории эта цепочка начинается с трёх явно названных входов". Not exactly journal citations. Could treat "OpenAI work, August 30" as a publication reference. Also "Axiom Math, August". We'll wrap those in single underscores.
We must not add source link.
Now produce headline: maybe "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". Count characters: Let's count: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". That's about 58 characters. Good.
Now body: need short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: Summarize the result: On August 30, OpenAI released a result from GPT-6 Astra showing that infinitely many consecutive primes differ by at most 186, i.e., lim inf (p_{n+1} - p_n) ≤ 186. Include citation OpenAI work, August 30.…
🔗 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. No added commentary.
We need to identify important facts: numbers like 186, 70 million, 4680, 600, 246, 40 fixed additions, 77 rational coefficients, >1/50000 margin, August 2025? Actually date: "30 августа OpenAI опубликовала работу" => August 30 (year unspecified, likely 2025?). The original text: "OpenAI опубликовала результат GPT-6 Astra: бесконечно многие соседние простые различаются не более чем на 186 30 августа OpenAI опубликовала работу о коротких промежутках между простыми." So date: August 30 (maybe 2024?). We'll keep as "August 30". Also mention "2013 year Ethan Zhang", "Polymath projects", "James Maynard", "next Polymath project", "OpenAI work reduces bound to 186". Also mention "Axiom Math formalized previous bound 246 in August". Also mention "Lean 4 code", "numerical certificate", "Selberg sieve", etc.
We need to wrap key numbers in double asterisks: maybe 186, 70 million, 4680, 600, 246, 40, 77, >1/50000. That's many; we need at most 4-5. So we must be selective: choose the most important: the bound 186, the previous bound 246, the improvement from 70 million to 186 maybe mention 70 million as historical. But we can only have 4-5 total. Let's pick: 186, 246, 70 million, 40 (fixed additions), maybe 77 rational coefficients. That's 5.
We must not wrap whole sentences; just wrap the numbers.
Citations: we have references to work by OpenAI, Polymath, James Maynard, Axiom Math formalization, Lean checking. We need to wrap study/journal citations and publication references in single underscores. There's no explicit journal name; maybe we treat the work as a paper? Could use OpenAI work, August 30? But that's not a journal. The instruction: "Wrap study/journal citations and publication references in single underscores (e.g. Nature Aging, July 2026)." So we need to identify any such citations. The text mentions "OpenAI опубликовала работу", "В августе Axiom Math формализовала прежнюю границу 246 ; там Lean проверял цепочку уже известной теоремы." Also "В новом репозитории эта цепочка начинается с трёх явно названных входов". Not exactly journal citations. Could treat "OpenAI work, August 30" as a publication reference. Also "Axiom Math, August". We'll wrap those in single underscores.
We must not add source link.
Now produce headline: maybe "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". Count characters: Let's count: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". That's about 58 characters. Good.
Now body: need short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: Summarize the result: On August 30, OpenAI released a result from GPT-6 Astra showing that infinitely many consecutive primes differ by at most 186, i.e., lim inf (p_{n+1} - p_n) ≤ 186. Include citation OpenAI work, August 30.…
🔗 Read original →
We need to translate Russian news post into English, format per rules.
First line: short headline under 90 chars, no markdown, not prefixed with '#'.
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: numbers: 186, 30 августа (August 30), 2013, 70 миллионов (70 million), 4680, 600, 246, 40 фиксированных добавок (40 fixed offsets), 0 and 186, 77 рациональных коэффициентов (77 rational coefficients), запас больше 1/50000 (margin greater than 1/50000), August Axiom Math formalized previous bound 246, etc.
We need to select at most 4-5 double asterisk facts. Choose the most important: 186, August 30, 2013, 70 million, 246 maybe. But we need at most 4-5. Let's pick: 186, August 30, 2013, 70 million, 246. That's 5.
We need to ensure we don't wrap whole sentences; just wrap the numbers/dates.
We need to wrap study/journal citations in single underscores. There's mention of "OpenAI опубликовала работу о коротких промежутках между простыми". Not a journal. There's "Axiom Math". Possibly a journal? It says "В августе Axiom Math формализовала прежнюю границу 246". So we can wrap Axiom Math as a citation? It's a publication reference. Also maybe "OpenAI" is not a journal. We'll wrap Axiom Math in single underscores.
Also maybe "Lean 4" is a tool, not citation. Not needed.
We need to preserve all facts exactly. So we need to translate the content.
Let's draft headline: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". Count characters: Let's count: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". That's about 58 characters. Good.
Now body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: On August 30, OpenAI released a result from GPT-6 Astra showing that infinitely many adjacent primes differ by no more than 186. The proof was produced by GPT-6 Astra, accompanied by a numerical certificate and Lean 4 code that checks the logical steps.
Paragraph 2: The number 186 means that there are infinitely many pairs of consecutive primes with a gap at most 186; formally, lim inf (p_{n+1} − p_n) ≤ 186. In 2013, Yitang Zhang established an upper bound of 70 million; subsequent Polymath projects reduced it to 4680, James Maynard to 600, and another Polymath effort to 246. OpenAI’s work improves this bound to 186.
Paragraph 3: The authors select 40 fixed shifts added to a base integer n, all lying between 0 and 186, and prove that for infinitely many n at least two of the shifted numbers are prime, giving a gap ≤ 186. The proof uses a multidimensional Selberg sieve, assigning numerical weights to such sets and estimating prime frequency. A new technique factors common divisors into three parts of suitable size, allowing a broader weight set and the needed inequality for the 40 numbers.…
🔗 Read original →
First line: short headline under 90 chars, no markdown, not prefixed with '#'.
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: numbers: 186, 30 августа (August 30), 2013, 70 миллионов (70 million), 4680, 600, 246, 40 фиксированных добавок (40 fixed offsets), 0 and 186, 77 рациональных коэффициентов (77 rational coefficients), запас больше 1/50000 (margin greater than 1/50000), August Axiom Math formalized previous bound 246, etc.
We need to select at most 4-5 double asterisk facts. Choose the most important: 186, August 30, 2013, 70 million, 246 maybe. But we need at most 4-5. Let's pick: 186, August 30, 2013, 70 million, 246. That's 5.
We need to ensure we don't wrap whole sentences; just wrap the numbers/dates.
We need to wrap study/journal citations in single underscores. There's mention of "OpenAI опубликовала работу о коротких промежутках между простыми". Not a journal. There's "Axiom Math". Possibly a journal? It says "В августе Axiom Math формализовала прежнюю границу 246". So we can wrap Axiom Math as a citation? It's a publication reference. Also maybe "OpenAI" is not a journal. We'll wrap Axiom Math in single underscores.
Also maybe "Lean 4" is a tool, not citation. Not needed.
We need to preserve all facts exactly. So we need to translate the content.
Let's draft headline: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". Count characters: Let's count: "OpenAI’s GPT-6 Astra proves infinitely many prime gaps ≤ 186". That's about 58 characters. Good.
Now body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: On August 30, OpenAI released a result from GPT-6 Astra showing that infinitely many adjacent primes differ by no more than 186. The proof was produced by GPT-6 Astra, accompanied by a numerical certificate and Lean 4 code that checks the logical steps.
Paragraph 2: The number 186 means that there are infinitely many pairs of consecutive primes with a gap at most 186; formally, lim inf (p_{n+1} − p_n) ≤ 186. In 2013, Yitang Zhang established an upper bound of 70 million; subsequent Polymath projects reduced it to 4680, James Maynard to 600, and another Polymath effort to 246. OpenAI’s work improves this bound to 186.
Paragraph 3: The authors select 40 fixed shifts added to a base integer n, all lying between 0 and 186, and prove that for infinitely many n at least two of the shifted numbers are prime, giving a gap ≤ 186. The proof uses a multidimensional Selberg sieve, assigning numerical weights to such sets and estimating prime frequency. A new technique factors common divisors into three parts of suitable size, allowing a broader weight set and the needed inequality for the 40 numbers.…
🔗 Read original →
We need to translate Russian news post into English, format as 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.
Let's extract facts:
- Date: 29 августа (August 29) in Molecular Neurodegeneration (journal). So citation: Molecular Neurodegeneration, August 29? Actually they said "29 августа в Molecular Neurodegeneration вышла работа Эмили Том и соавторов". So we can put Molecular Neurodegeneration, August 29 (year? Not given; assume 2026? Not given. We'll just keep date as given: August 29. Could also include year if known but not in text. We'll keep as is.
- Authors: Emily Tom and coauthors.
- Study: pigment epithelium of retina.
- They tracked pathway from age-related lipid change to membrane repair and tissue remodeling beneath this layer.
- Pigment epithelium supplies photoreceptors and digests their spent parts.
- Between it and blood vessels lies Bruch's membrane; with age extracellular material accumulates at this boundary.
- In donor epithelium samples and retina of old mice, authors first saw age signature: genes related to membranes changed, and reserve of long-chain polyunsaturated fatty acids.
- Then they checked ELOVL2 — enzyme that elongates such fatty acids.
- When its activity reduced in cells or disrupted in mice, these lipids decreased, and ceramides increased.
- Membrane became more ordered: its lipids packed tighter, calcium level rose in cells.
- Simultaneously lysosomes accumulated at cell edge and fused with outer membrane, releasing part of contents outward.
- Lysosomes normally digest cellular material, but here authors consider their exit to surface as a way to patch damaged boundary.
- In cell layer where upper and lower sides can be measured separately, more acid sphingomyelinase was secreted downward, toward Bruch's membrane.
- This enzyme converts one membrane lipid into ceramides; in mutant mice at same boundary APOE protein accumulated, part of deposits.
- So authors link prolonged lysosomal repair with remodeling of extracellular space beneath pigment epithelium.
- Authors checked this chain in reverse, adding 24:5n-3 — direct product of ELOVL2.
- In 2025 work same team showed in old mice that its administration improved visual indicators and decreased deposits under pigment epithelium.
- In new work addition of 24:5n-3 to cells weakened signs of membrane stress and returned lysosomes from cell edge; after intravitreal injection in mice, ceramide accumulation decreased and pigment epithelium structure partially restored.
- By authors' model, deficiency of ELOVL2 products changes membrane properties, while prolonged lysosomal work maintains its integrity and simultaneously changes tissue beneath pigment epithelium.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. Let's identify:
- Date: August 29 (maybe year unknown). Could wrap August 29.
- Gene/enzyme: ELOVL2 (gene name). Also maybe 24:5n-3 (fatty acid). Also APOE (protein). Also maybe acid sphingomyelinase (enzyme). But we need at most 4-5 double asterisks total. Choose most important: ELOVL2, 24:5n-3, APOE, August 29 (date). That's four.
We could also include Molecular Neurodegeneration as journal name? That's a citation, should be wrapped in single underscores, not double asterisks. So we keep that as Molecular Neurodegeneration.…
🔗 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.
Let's extract facts:
- Date: 29 августа (August 29) in Molecular Neurodegeneration (journal). So citation: Molecular Neurodegeneration, August 29? Actually they said "29 августа в Molecular Neurodegeneration вышла работа Эмили Том и соавторов". So we can put Molecular Neurodegeneration, August 29 (year? Not given; assume 2026? Not given. We'll just keep date as given: August 29. Could also include year if known but not in text. We'll keep as is.
- Authors: Emily Tom and coauthors.
- Study: pigment epithelium of retina.
- They tracked pathway from age-related lipid change to membrane repair and tissue remodeling beneath this layer.
- Pigment epithelium supplies photoreceptors and digests their spent parts.
- Between it and blood vessels lies Bruch's membrane; with age extracellular material accumulates at this boundary.
- In donor epithelium samples and retina of old mice, authors first saw age signature: genes related to membranes changed, and reserve of long-chain polyunsaturated fatty acids.
- Then they checked ELOVL2 — enzyme that elongates such fatty acids.
- When its activity reduced in cells or disrupted in mice, these lipids decreased, and ceramides increased.
- Membrane became more ordered: its lipids packed tighter, calcium level rose in cells.
- Simultaneously lysosomes accumulated at cell edge and fused with outer membrane, releasing part of contents outward.
- Lysosomes normally digest cellular material, but here authors consider their exit to surface as a way to patch damaged boundary.
- In cell layer where upper and lower sides can be measured separately, more acid sphingomyelinase was secreted downward, toward Bruch's membrane.
- This enzyme converts one membrane lipid into ceramides; in mutant mice at same boundary APOE protein accumulated, part of deposits.
- So authors link prolonged lysosomal repair with remodeling of extracellular space beneath pigment epithelium.
- Authors checked this chain in reverse, adding 24:5n-3 — direct product of ELOVL2.
- In 2025 work same team showed in old mice that its administration improved visual indicators and decreased deposits under pigment epithelium.
- In new work addition of 24:5n-3 to cells weakened signs of membrane stress and returned lysosomes from cell edge; after intravitreal injection in mice, ceramide accumulation decreased and pigment epithelium structure partially restored.
- By authors' model, deficiency of ELOVL2 products changes membrane properties, while prolonged lysosomal work maintains its integrity and simultaneously changes tissue beneath pigment epithelium.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. Let's identify:
- Date: August 29 (maybe year unknown). Could wrap August 29.
- Gene/enzyme: ELOVL2 (gene name). Also maybe 24:5n-3 (fatty acid). Also APOE (protein). Also maybe acid sphingomyelinase (enzyme). But we need at most 4-5 double asterisks total. Choose most important: ELOVL2, 24:5n-3, APOE, August 29 (date). That's four.
We could also include Molecular Neurodegeneration as journal name? That's a citation, should be wrapped in single underscores, not double asterisks. So we keep that as Molecular Neurodegeneration.…
🔗 Read original →
PubMed Central (PMC)
The lipid elongation enzyme ELOVL2 is a molecular regulator of aging in the retina
Methylation of the regulatory region of the elongation of very‐long‐chain fatty acids‐like 2 (ELOVL2) gene, an enzyme involved in elongation of long‐chain polyunsaturated fatty acids, is one of the most robust biomarkers of human age, but the ...
Essay on LessWrong Suggests Partial Brain Emulation May Boost AI Before Full Copy
On September 5, author TsviBT published an essay on LessWrong about whole brain emulation — a computer model that could replicate a person over long periods. He proposes measuring progress toward such a model by the intermediate data, methods, and simulations that appear earlier and who might benefit from them.
Partial emulations, which reproduce individual brain abilities but not a full personality, are easier to achieve than a complete copy. TsviBT calls the gap between useful fragments and a digital personality the “bad knee,” noting that these fragments could accelerate AI development before a full emulation exists, potentially outweighing its future benefits.
The discussion under the essay highlights that risk depends on the research route, contrasting models trained on brain activity recordings with connectomics — maps of neurons and their connections. The former learns to reproduce brain function directly; the latter describes the brain’s wiring. A Technical review 2025 explains this difference: connectivity maps show wiring, while functional models require activity data, suggesting training on organisms with both data types before scaling to larger mammalian brains.
TsviBT recommends evaluating each emulation program by the intermediate models, data, and methods it makes available prior to achieving full emulation.
🔗 Read original →
On September 5, author TsviBT published an essay on LessWrong about whole brain emulation — a computer model that could replicate a person over long periods. He proposes measuring progress toward such a model by the intermediate data, methods, and simulations that appear earlier and who might benefit from them.
Partial emulations, which reproduce individual brain abilities but not a full personality, are easier to achieve than a complete copy. TsviBT calls the gap between useful fragments and a digital personality the “bad knee,” noting that these fragments could accelerate AI development before a full emulation exists, potentially outweighing its future benefits.
The discussion under the essay highlights that risk depends on the research route, contrasting models trained on brain activity recordings with connectomics — maps of neurons and their connections. The former learns to reproduce brain function directly; the latter describes the brain’s wiring. A Technical review 2025 explains this difference: connectivity maps show wiring, while functional models require activity data, suggesting training on organisms with both data types before scaling to larger mammalian brains.
TsviBT recommends evaluating each emulation program by the intermediate models, data, and methods it makes available prior to achieving full emulation.
🔗 Read original →
Three Safety Checks Proposed for Self‑Driving Labs Before Real Experiments
An essay posted on LessWrong on September 4 describes a laboratory where a model selects the next experimental step and robotic equipment carries it out. The author argues that the entire chain turning a calculation into instrument commands must be examined for safety.
The essay separates the model’s capabilities from its authority. A model can detect danger and devise a plan, but access to materials, instruments, and settings decides which actions the plan will actually trigger in the physical lab. A safety check must consider both what the model can do and what the surrounding system permits.
Three sequential checks are proposed. First, test the model on recognizing hazardous directions and obeying given bans. Second, run the full experiment plan through a virtual model that incorporates instrument limits, incomplete data, and failures to see whether it remains safe. Third, perform a low‑risk task on real hardware to compare expected safety with observed results and refine the calculation before tackling more complex experiments.
In the LabShield benchmark suite, models scored 32.0 percentage points lower on average across 164 tasks in professional scenarios requiring independent reasoning than in multiple‑choice questions. This gap shows why the whole plan — including the model’s ability to give a safe answer — should be vetted before physical work.
After the initial three stages, the author recommends ongoing evaluation. Models, equipment, and software are updated, and new failure modes emerge in the lab; therefore the agent’s authority, instrument status, and ability to abort a procedure must be reassessed regularly.
🔗 Read original →
An essay posted on LessWrong on September 4 describes a laboratory where a model selects the next experimental step and robotic equipment carries it out. The author argues that the entire chain turning a calculation into instrument commands must be examined for safety.
The essay separates the model’s capabilities from its authority. A model can detect danger and devise a plan, but access to materials, instruments, and settings decides which actions the plan will actually trigger in the physical lab. A safety check must consider both what the model can do and what the surrounding system permits.
Three sequential checks are proposed. First, test the model on recognizing hazardous directions and obeying given bans. Second, run the full experiment plan through a virtual model that incorporates instrument limits, incomplete data, and failures to see whether it remains safe. Third, perform a low‑risk task on real hardware to compare expected safety with observed results and refine the calculation before tackling more complex experiments.
In the LabShield benchmark suite, models scored 32.0 percentage points lower on average across 164 tasks in professional scenarios requiring independent reasoning than in multiple‑choice questions. This gap shows why the whole plan — including the model’s ability to give a safe answer — should be vetted before physical work.
After the initial three stages, the author recommends ongoing evaluation. Models, equipment, and software are updated, and new failure modes emerge in the lab; therefore the agent’s authority, instrument status, and ability to abort a procedure must be reassessed regularly.
🔗 Read original →
Nature
Risks of AI scientists: prioritizing safeguarding over autonomy
Nature Communications - AI scientists powered by large language models and AI agents present both opportunities and risks in automatic scientific discovery. Here, the authors examine the...
Hippo–IGF2 pathway controls liver regeneration and tumor growth in mice
On September
🔗 Read original →
On September
🔗 Read original →
Bone marrow vascular endothelial RANK signaling drives age‑related inflammation in mice
On September 3, 2026 Yasuhiro Kobayashi’s group posted a preprint showing how age‑related inflammation begins in mouse bone marrow. They traced a cascade from the vascular‑endothelial protein RANK to the cytokine IL‑1β, which propagates the inflammatory signal. In this model IL‑1β amplified senescence of marrow stromal cells and shifted blood‑cell production toward myeloid lineages.
The team started with the RANKL–RANK–OPG system, where RANKL activates RANK and OPG sequesters RANKL to dampen signaling. In eight‑week mice lacking OPG they observed more senescence‑marker‑positive cells, excess neutrophils and fewer lymphocytes — a phenotype matching that of 32‑week control mice. Treatment with an anti‑RANKL antibody reduced senescence‑marker‑positive cells and alleviated the myeloid skew.
To pinpoint the site of action, the authors referred to their 2025 work linking RANK/RANKL to marrow‑vascular permeability and immune‑cell transmigration. Fluorescently labeled RANKL in 32‑week mice bound chiefly to the endothelium, and deleting RANK only in endothelial cells lowered senescence‑marker‑positive cells and the myeloid bias, while promoting new bone formation after marrow ablation
🔗 Read original →
On September 3, 2026 Yasuhiro Kobayashi’s group posted a preprint showing how age‑related inflammation begins in mouse bone marrow. They traced a cascade from the vascular‑endothelial protein RANK to the cytokine IL‑1β, which propagates the inflammatory signal. In this model IL‑1β amplified senescence of marrow stromal cells and shifted blood‑cell production toward myeloid lineages.
The team started with the RANKL–RANK–OPG system, where RANKL activates RANK and OPG sequesters RANKL to dampen signaling. In eight‑week mice lacking OPG they observed more senescence‑marker‑positive cells, excess neutrophils and fewer lymphocytes — a phenotype matching that of 32‑week control mice. Treatment with an anti‑RANKL antibody reduced senescence‑marker‑positive cells and alleviated the myeloid skew.
To pinpoint the site of action, the authors referred to their 2025 work linking RANK/RANKL to marrow‑vascular permeability and immune‑cell transmigration. Fluorescently labeled RANKL in 32‑week mice bound chiefly to the endothelium, and deleting RANK only in endothelial cells lowered senescence‑marker‑positive cells and the myeloid bias, while promoting new bone formation after marrow ablation
🔗 Read original →
PubMed Central (PMC)
The RANK/RANKL axis controls vascular dynamics in the bone marrow
Receptor activator of nuclear factor kappa B ligand (RANKL) is a multifunctional cytokine and has been attracting high levels of interest in broader fields of biomedical sciences. In this study, we showed that RANKL from C–X–C motif chemokine ligand ...
We need to translate Russian news post into natural English, format per rules.
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores (e.g., Nature Aging, July 2026).
We need to preserve facts, numbers, names, citations exactly.
Let's extract facts:
- Study: September 1 in The FASEB Journal (date and journal). So citation: The FASEB Journal, September 1? Actually they said "1 сентября в статье The FASEB Journal". So we need to wrap the citation in single underscores: The FASEB Journal, September 1.
- Researchers compared three light-dark cycles in female mice of one lab strain, fed high-fat diet.
- Each group had eight animals; varied duration of light-dark alternation.
- Mice have internal clocks: in constant darkness activity repeats with its own period called tau.
- Early in experiment tau of mice on high-fat diet was longer than that of mice on low-fat diet, before obesity appeared.
- This observation prompted question: would obesity timing be changed by external light schedule aligned with internal rhythm.
- For testing on high-fat diet they used: usual 24-hour day, cycle about 23.64 hours — close to tau — and cycle 23.28 hours.
- Light and dark parts of each cycle occupied equal time.
- At 24-hour regimen obesity recorded at 4.0 ± 1.1 weeks.
- At two shorter cycles — at 12.1 ± 1.6 and 11.3 ± 1.6 weeks.
- Obesity occurred in all three groups, and shorter days shifted its onset by about eight weeks.
- Accumulated energy intake between light regimens was similar.
- Mice with short cycles maintained higher locomotor activity from second week; body mass differences appeared later.
- Authors link this sequence to higher energy expenditure: timing difference of obesity arose on same high-fat diet and without difference in accumulated energy intake.
- After the shortest regimen, 23.28 hours, internal period in darkness also became shorter.
- After cycle close to tau, it remained at level of the cycle period and constant darkness.
- Delay of obesity occurred at both short regimens, so aftereffect of one of them does not explain whole result.
- In this model time to development of obesity depended on how external light schedule matched internal rhythm of mice.
We need to double asterisk key numbers, percentages, drug/company/gene names, dates. So we need to select at most 4-5 facts to bold.
Potential bold items: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1, maybe tau? But tau is a term not a number. Could bold the numbers: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1. That's 5 items.
We must not bold whole sentences; just wrap the numbers/dates.
We also need to wrap study/journal citation in single underscores: The FASEB Journal, September 1.
Now format: headline line, blank line, then paragraphs.
Headline: something like "Shorter Light-Dark Cycles Delay Obesity Onset in Mice". Under 90 chars.
Now body: we need short paragraphs 2-3 sentences each.
Let's craft maybe 4 paragraphs.
Paragraph 1: Introduce study, mention journal and date, mice, high-fat diet, three light-dark cycles.
Paragraph 2: Provide obesity onset times for each cycle, note similar energy intake.
Paragraph 3: Describe activity differences and link to energy expenditure, mention tau observations.
Paragraph 4: Summarize conclusion about external schedule matching internal rhythm.
Make sure to bold numbers/dates.
Double asterisk formatting: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1.…
🔗 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 facts, numbers, names, citations exactly.
Let's extract facts:
- Study: September 1 in The FASEB Journal (date and journal). So citation: The FASEB Journal, September 1? Actually they said "1 сентября в статье The FASEB Journal". So we need to wrap the citation in single underscores: The FASEB Journal, September 1.
- Researchers compared three light-dark cycles in female mice of one lab strain, fed high-fat diet.
- Each group had eight animals; varied duration of light-dark alternation.
- Mice have internal clocks: in constant darkness activity repeats with its own period called tau.
- Early in experiment tau of mice on high-fat diet was longer than that of mice on low-fat diet, before obesity appeared.
- This observation prompted question: would obesity timing be changed by external light schedule aligned with internal rhythm.
- For testing on high-fat diet they used: usual 24-hour day, cycle about 23.64 hours — close to tau — and cycle 23.28 hours.
- Light and dark parts of each cycle occupied equal time.
- At 24-hour regimen obesity recorded at 4.0 ± 1.1 weeks.
- At two shorter cycles — at 12.1 ± 1.6 and 11.3 ± 1.6 weeks.
- Obesity occurred in all three groups, and shorter days shifted its onset by about eight weeks.
- Accumulated energy intake between light regimens was similar.
- Mice with short cycles maintained higher locomotor activity from second week; body mass differences appeared later.
- Authors link this sequence to higher energy expenditure: timing difference of obesity arose on same high-fat diet and without difference in accumulated energy intake.
- After the shortest regimen, 23.28 hours, internal period in darkness also became shorter.
- After cycle close to tau, it remained at level of the cycle period and constant darkness.
- Delay of obesity occurred at both short regimens, so aftereffect of one of them does not explain whole result.
- In this model time to development of obesity depended on how external light schedule matched internal rhythm of mice.
We need to double asterisk key numbers, percentages, drug/company/gene names, dates. So we need to select at most 4-5 facts to bold.
Potential bold items: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1, maybe tau? But tau is a term not a number. Could bold the numbers: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1. That's 5 items.
We must not bold whole sentences; just wrap the numbers/dates.
We also need to wrap study/journal citation in single underscores: The FASEB Journal, September 1.
Now format: headline line, blank line, then paragraphs.
Headline: something like "Shorter Light-Dark Cycles Delay Obesity Onset in Mice". Under 90 chars.
Now body: we need short paragraphs 2-3 sentences each.
Let's craft maybe 4 paragraphs.
Paragraph 1: Introduce study, mention journal and date, mice, high-fat diet, three light-dark cycles.
Paragraph 2: Provide obesity onset times for each cycle, note similar energy intake.
Paragraph 3: Describe activity differences and link to energy expenditure, mention tau observations.
Paragraph 4: Summarize conclusion about external schedule matching internal rhythm.
Make sure to bold numbers/dates.
Double asterisk formatting: 4.0 ± 1.1 weeks, 12.1 ± 1.6 weeks, 11.3 ± 1.6 weeks, about eight weeks, September 1.…
🔗 Read original →
PubMed Central (PMC)
Photic Cycle Shorter Than or Equal to Endogenous tau Postpones Diet‐Induced Obesity in Mice and Shows a Robust Aftereffect
High‐fat diet (HFD)‐induced obesity (DIO) is preceded by disruptions in endogenous circadian rhythmicity, including lengthening of its period (tau). We previously demonstrated that housing mice under a light–dark cycle (T‐cycle) oscillating at their ...