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 "статья с промежуточными данными регистра MASTRR: в десяти центрах США устройства Myriad применили у 411 пациентов с 474 сложными дефектами мягких тканей." No explicit journal name. Might be a study article but not given. We may need to wrap any citation like "статья" but we don't have journal name. Possibly we can treat "MASTRR" as registry name, not citation. No explicit journal citation given. So maybe we don't need any underscores. However we could still wrap the registry name? The rule: "Wrap study/journal citations and publication references in single underscores". If none present, we skip.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post.
Important facts: 411 patients, 474 defects, 10 US centers, Myriad device, made from sheep rumen extracellular matrix, median follow-up 27.1 weeks, 50 patients (12.2%) had 59 adverse events, superficial infection 2.9%, deep infection 0.7%, skin reaction (likely related) led to removal of 40% of scaffold, antihistamines, split-thickness skin graft, registry ongoing, planned size 800 participants, primary endpoint proportion with adverse events over three years, article published September 3 (maybe date). Also "почти 60% дефектов сохранялись больше месяца" (almost 60% defects persisted >1 month). Also participants often had type 2 diabetes and vascular diseases.
We need to select at most 4-5 double asterisk wrapped items. Choose the most important: number of patients (411), number of defects (474), median follow-up (27.1 weeks), percentage with adverse events (12.2%), planned registry size (800). Also maybe date Sept 3. But we have limit 4-5. Let's pick: 411 patients, 474 defects, 27.1 weeks, 12.2%, 800 participants. That's 5.
We need to ensure we don't wrap whole sentence. Just wrap those numbers/percentages.
Also need to wrap drug/company/gene names: Myriad is a device name; could be considered a company/device name; we could wrap it but that would increase count. We already have 5 items; maybe we replace one with Myriad? But we need to keep numbers. The rule says "Wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post". So we can include drug/company names as part of the limited count. We need to decide which to include. Probably include Myriad as it's important. But we also need numbers. Let's see if we can fit 5 items: Myriad, 411 patients, 27.1 weeks, 12.2%, 800 participants. That's 5. Could also include 474 defects but we would exceed. Maybe we can combine "411 patients with 474 defects" as one phrase? But that would be a whole sentence? Not exactly; we could wrap "411 patients" and "474 defects" separately but that's two. Could we wrap "411 patients, 474 defects" as one? That includes two numbers but still one wrapped segment. The rule says wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates. It doesn't forbid wrapping a phrase containing multiple numbers. So we could wrap "411 patients with 474 defects" as one item. That counts as one. Then we have Myriad, 27.1 weeks, 12.2%, 800 participants = total 5. Good.…
🔗 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 "статья с промежуточными данными регистра MASTRR: в десяти центрах США устройства Myriad применили у 411 пациентов с 474 сложными дефектами мягких тканей." No explicit journal name. Might be a study article but not given. We may need to wrap any citation like "статья" but we don't have journal name. Possibly we can treat "MASTRR" as registry name, not citation. No explicit journal citation given. So maybe we don't need any underscores. However we could still wrap the registry name? The rule: "Wrap study/journal citations and publication references in single underscores". If none present, we skip.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post.
Important facts: 411 patients, 474 defects, 10 US centers, Myriad device, made from sheep rumen extracellular matrix, median follow-up 27.1 weeks, 50 patients (12.2%) had 59 adverse events, superficial infection 2.9%, deep infection 0.7%, skin reaction (likely related) led to removal of 40% of scaffold, antihistamines, split-thickness skin graft, registry ongoing, planned size 800 participants, primary endpoint proportion with adverse events over three years, article published September 3 (maybe date). Also "почти 60% дефектов сохранялись больше месяца" (almost 60% defects persisted >1 month). Also participants often had type 2 diabetes and vascular diseases.
We need to select at most 4-5 double asterisk wrapped items. Choose the most important: number of patients (411), number of defects (474), median follow-up (27.1 weeks), percentage with adverse events (12.2%), planned registry size (800). Also maybe date Sept 3. But we have limit 4-5. Let's pick: 411 patients, 474 defects, 27.1 weeks, 12.2%, 800 participants. That's 5.
We need to ensure we don't wrap whole sentence. Just wrap those numbers/percentages.
Also need to wrap drug/company/gene names: Myriad is a device name; could be considered a company/device name; we could wrap it but that would increase count. We already have 5 items; maybe we replace one with Myriad? But we need to keep numbers. The rule says "Wrap the few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks. Be selective: at most 4-5 per post". So we can include drug/company names as part of the limited count. We need to decide which to include. Probably include Myriad as it's important. But we also need numbers. Let's see if we can fit 5 items: Myriad, 411 patients, 27.1 weeks, 12.2%, 800 participants. That's 5. Could also include 474 defects but we would exceed. Maybe we can combine "411 patients with 474 defects" as one phrase? But that would be a whole sentence? Not exactly; we could wrap "411 patients" and "474 defects" separately but that's two. Could we wrap "411 patients, 474 defects" as one? That includes two numbers but still one wrapped segment. The rule says wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates. It doesn't forbid wrapping a phrase containing multiple numbers. So we could wrap "411 patients with 474 defects" as one item. That counts as one. Then we have Myriad, 27.1 weeks, 12.2%, 800 participants = total 5. Good.…
🔗 Read original →
PubMed Central (PMC)
The Role and Efficacy of NovoSorb Biodegradable Temporizing Matrix in Complex Reconstructive Wounds: A Systematic Review
Managing complex wounds with exposed bone, tendons, metalware, or irradiated tissue is challenging. NovoSorb Biodegradable Temporizing Matrix (BTM) offers an alternative by promoting vascularization and neodermis formation. This review assessed the ...
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). There's no citation in the text except maybe Post Bulletin? It's a news source, not a study/journal. So we may not need underscores unless we treat Post Bulletin as a publication reference? The rule says wrap study/journal citations and publication references in single underscores. Post Bulletin is a publication, so we can wrap it: Post Bulletin. But it's a news source; still a publication reference. We'll do that.
We need to preserve all facts, numbers, names, citations exactly.
Important facts: United Therapeutics, completed facility in Stuartville on September 1, cost $110 million, facility designed for 150 genetically edited pigs, organs for human transplantation, xenotransplantation definition, facility size 65,000 sq ft, about 25 employees, labs, veterinary clinic, two surgical blocks, Rodney Lamb (technical operations head of xenoprogram) compared clean zone to "building within a building", internal clean zone separated, staff work with animals in protective suits, equipment and materials sterilized with hydrogen peroxide before entry, these procedures maintain conditions for pigs to become donors for patients, adjacent to completed center company purchased 207 acres for future swine organ production complexes, first center now ready to accept animals.
We need to bold at most 4-5 key facts: maybe the date, cost, number of pigs, facility size, acres purchased. That's 5.
We need to bold them with double asterisks. Ensure not bold whole sentence.
We'll embed bold around the numbers/dates etc.
Let's craft headline: "United Therapeutics Completes $110 Million Pig‑Organ Facility in Stuartville". Count characters: Let's count: United(6) + space=1 =>7, Therapeutics(12)=>19, space=20, Completes(9)=>29, space=30, $110(4)=>34, space=35, Million(7)=>42, space=43, Pig‑Organ(9 inc hyphen?) Let's count: Pig‑Organ (P i g ‑ O r g a n) that's 9 inc hyphen? Actually "Pig‑Organ": 3+1+6=10? Let's just approximate. We'll be under 90.
Now blank line.
Paragraph 1: mention completion date Sept 1, cost $110M, facility for 150 genetically edited pigs, purpose xenotransplantation. We'll bold date, cost, number of pigs.
Paragraph 2: definition of xenotransplantation, mention facility size 65,000 sq ft, about 25 employees, labs, vet clinic, two surgical blocks. Bold facility size maybe.
Paragraph 3: quote Rodney Lamb, description of clean zone as "building within a building", internal clean zone separated, staff wear protective suits, equipment sterilized with hydrogen peroxide. Possibly bold nothing else.
Paragraph 4: mention adjacent purchase of 207 acres for future complexes, first center now ready to accept animals. Bold acres.
We need to ensure at most 4-5 bolded items total. Let's choose: September 1, $110 million, 150 genetically edited pigs, 65,000 square feet, 207 acres. That's 5.
We need to wrap them in double asterisks exactly.
Now ensure we don't bold whole sentence; just those phrases.
Now need to wrap study/journal citations and publication references in single underscores. We have Post Bulletin as a publication reference. We'll wrap that.
Now produce final output.
Check formatting: first line headline, then blank line, then paragraphs separated by blank lines.
Paragraphs: 2-3 sentences each.
Let's craft.…
🔗 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). There's no citation in the text except maybe Post Bulletin? It's a news source, not a study/journal. So we may not need underscores unless we treat Post Bulletin as a publication reference? The rule says wrap study/journal citations and publication references in single underscores. Post Bulletin is a publication, so we can wrap it: Post Bulletin. But it's a news source; still a publication reference. We'll do that.
We need to preserve all facts, numbers, names, citations exactly.
Important facts: United Therapeutics, completed facility in Stuartville on September 1, cost $110 million, facility designed for 150 genetically edited pigs, organs for human transplantation, xenotransplantation definition, facility size 65,000 sq ft, about 25 employees, labs, veterinary clinic, two surgical blocks, Rodney Lamb (technical operations head of xenoprogram) compared clean zone to "building within a building", internal clean zone separated, staff work with animals in protective suits, equipment and materials sterilized with hydrogen peroxide before entry, these procedures maintain conditions for pigs to become donors for patients, adjacent to completed center company purchased 207 acres for future swine organ production complexes, first center now ready to accept animals.
We need to bold at most 4-5 key facts: maybe the date, cost, number of pigs, facility size, acres purchased. That's 5.
We need to bold them with double asterisks. Ensure not bold whole sentence.
We'll embed bold around the numbers/dates etc.
Let's craft headline: "United Therapeutics Completes $110 Million Pig‑Organ Facility in Stuartville". Count characters: Let's count: United(6) + space=1 =>7, Therapeutics(12)=>19, space=20, Completes(9)=>29, space=30, $110(4)=>34, space=35, Million(7)=>42, space=43, Pig‑Organ(9 inc hyphen?) Let's count: Pig‑Organ (P i g ‑ O r g a n) that's 9 inc hyphen? Actually "Pig‑Organ": 3+1+6=10? Let's just approximate. We'll be under 90.
Now blank line.
Paragraph 1: mention completion date Sept 1, cost $110M, facility for 150 genetically edited pigs, purpose xenotransplantation. We'll bold date, cost, number of pigs.
Paragraph 2: definition of xenotransplantation, mention facility size 65,000 sq ft, about 25 employees, labs, vet clinic, two surgical blocks. Bold facility size maybe.
Paragraph 3: quote Rodney Lamb, description of clean zone as "building within a building", internal clean zone separated, staff wear protective suits, equipment sterilized with hydrogen peroxide. Possibly bold nothing else.
Paragraph 4: mention adjacent purchase of 207 acres for future complexes, first center now ready to accept animals. Bold acres.
We need to ensure at most 4-5 bolded items total. Let's choose: September 1, $110 million, 150 genetically edited pigs, 65,000 square feet, 207 acres. That's 5.
We need to wrap them in double asterisks exactly.
Now ensure we don't bold whole sentence; just those phrases.
Now need to wrap study/journal citations and publication references in single underscores. We have Post Bulletin as a publication reference. We'll wrap that.
Now produce final output.
Check formatting: first line headline, then blank line, then paragraphs separated by blank lines.
Paragraphs: 2-3 sentences each.
Let's craft.…
🔗 Read original →
We need to translate Russian news post into natural fluent English, format per rules.
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026).
We need to preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: dates: "4 сентября авторы препринта описали SniffCell." => September 4 (year not given). Could be 2024? Not given. We'll keep date as September 4.
Numbers: average accuracy 80.38%; 16 physically sorted fractions; 83 of 92 DNA sites; 172 donors; median proportion 29.2% for neuronal events and 66.7% for oligodendrocytic events; atlas of methylation 2023 gathered 39 cell groups from 205 healthy samples; SniffCell expanded atlas for eight tissues.
Also key names: SniffCell method; T- and B-cell receptors; tandem repeats; GAA triplet; frontal cortex; neurons and oligodendrocytes; somatic variant.
We need to select at most 4-5 double asterisk facts. Choose maybe: September 4, 80.38%, 172 donors, 29.2%, 66.7%. That's 5.
But also maybe include "83 of 92 sites". Could replace one. Let's decide: we need at most 4-5. Choose 5: September 4, 80.38%, 172 donors, 29.2%, 66.7%.
Wrap each in double asterisks.
Now citations: The text mentions "атлас метилирования 2023 года , который собрал 39 групп клеток из 205 здоровых образцов." That's a reference; we need to wrap study/journal citations and publication references in single underscores. This is not a journal citation but a dataset/reference. We'll treat as methylation atlas 2023 maybe. The instruction: wrap study/journal citations and publication references in single underscores. So we can put methylation atlas 2023 (or 2023 methylation atlas). Also maybe "препринт SniffCell" is a preprint; not a journal citation. We'll just underscore the atlas reference.
Also maybe "обзор о геномной мозаике" is a review; we could underscore that as genomic mosaic review. But we need to be selective; maybe just one underscore.
We'll include methylation atlas 2023 as the citation.
Now produce translation.
First line headline: something like "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters: Let's count: "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters including spaces: Let's count: S(1)n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52 (space53)a54n55d56 (space57)f58i59n60d61s62 (space63)r64e65p66e67a68t69 (space70)e71x72p73a74n75s76i77o78n79s80 (space81)i82n83 (space84)n85e86u87r88o89n90s91. That's 91 characters, okay under 90? Actually 91 > 90. Need under 90. Let's shorten: "SniffCell links DNA methylation to cell types, finds neuronal repeat expansions". Count: S1n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52, (space53)f54i55n56d57s58 (space59)n60e61u62r63o64n65a66l67 (space68)r69e70p71e72a73t74 (space75)e76x77p78a79n80s81i82o83n84s85. That's 85 characters. Good.
Now blank line.
Now body paragraphs.
We need to preserve facts. Let's craft paragraphs 2-3 sentences each.…
🔗 Read original →
First line: short headline under 90 chars, no markdown, no '#'.
Then blank line, then body split into short paragraphs (2-3 sentences each), separated by blank lines.
Wrap few genuinely important facts — key numbers, percentages, drug/company/gene names, dates — in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026).
We need to preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: dates: "4 сентября авторы препринта описали SniffCell." => September 4 (year not given). Could be 2024? Not given. We'll keep date as September 4.
Numbers: average accuracy 80.38%; 16 physically sorted fractions; 83 of 92 DNA sites; 172 donors; median proportion 29.2% for neuronal events and 66.7% for oligodendrocytic events; atlas of methylation 2023 gathered 39 cell groups from 205 healthy samples; SniffCell expanded atlas for eight tissues.
Also key names: SniffCell method; T- and B-cell receptors; tandem repeats; GAA triplet; frontal cortex; neurons and oligodendrocytes; somatic variant.
We need to select at most 4-5 double asterisk facts. Choose maybe: September 4, 80.38%, 172 donors, 29.2%, 66.7%. That's 5.
But also maybe include "83 of 92 sites". Could replace one. Let's decide: we need at most 4-5. Choose 5: September 4, 80.38%, 172 donors, 29.2%, 66.7%.
Wrap each in double asterisks.
Now citations: The text mentions "атлас метилирования 2023 года , который собрал 39 групп клеток из 205 здоровых образцов." That's a reference; we need to wrap study/journal citations and publication references in single underscores. This is not a journal citation but a dataset/reference. We'll treat as methylation atlas 2023 maybe. The instruction: wrap study/journal citations and publication references in single underscores. So we can put methylation atlas 2023 (or 2023 methylation atlas). Also maybe "препринт SniffCell" is a preprint; not a journal citation. We'll just underscore the atlas reference.
Also maybe "обзор о геномной мозаике" is a review; we could underscore that as genomic mosaic review. But we need to be selective; maybe just one underscore.
We'll include methylation atlas 2023 as the citation.
Now produce translation.
First line headline: something like "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters: Let's count: "SniffCell method links DNA methylation to cell types and finds repeat expansions in neurons". Count characters including spaces: Let's count: S(1)n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52 (space53)a54n55d56 (space57)f58i59n60d61s62 (space63)r64e65p66e67a68t69 (space70)e71x72p73a74n75s76i77o78n79s80 (space81)i82n83 (space84)n85e86u87r88o89n90s91. That's 91 characters, okay under 90? Actually 91 > 90. Need under 90. Let's shorten: "SniffCell links DNA methylation to cell types, finds neuronal repeat expansions". Count: S1n2i3f4f5C6e7l8l9 (space10)m11e12t13h14o15d16 (space17)l18i19n20k21s22 (space23)D24N25A26 (space27)m28e29t30h31y32l33a34t35i36o37n38 (space39)t40o41 (space42)c43e44l45l46 (space47)t48y49p50e51s52, (space53)f54i55n56d57s58 (space59)n60e61u62r63o64n65a66l67 (space68)r69e70p71e72a73t74 (space75)e76x77p78a79n80s81i82o83n84s85. That's 85 characters. Good.
Now blank line.
Now body paragraphs.
We need to preserve facts. Let's craft paragraphs 2-3 sentences each.…
🔗 Read original →
medRxiv
Cell-type-resolved somatic variant discovery from bulk long-read sequencing
Somatic mutations arise throughout life, with functional consequences tied to the cell populations in which they occur. Genome-wide studies measure somatic variations in bulk tissue, whereas single-cell approaches resolve cell identity but provide limited…
VisionECG builds 3D left‑ventricle model from ECG
On September 4, researchers posted a preprint on medRxiv preprint, September 4 describing VisionECG, a model trained on 71,132 ECG‑MRI pairs from the UK Biobank. The model learns to generate a sequence of 50 successive 3‑D surfaces of the left ventricle from a standard 12‑lead ECG and eight basic subject characteristics.
For 5,000 patients with both ECG and echocardiography, the model‑derived ejection fraction was compared to ultrasound measurements. From the reconstructed ventricular motion the model can compute volume, mass, wall thickness, ejection fraction and myocardial strain.
On an external test set of 2,000 participants the median relative error of volume over the cardiac cycle was 8.34%, and wall‑thickness error was 9.54%. When the individual ECG was replaced by the cohort average, the volume error in dilated cardiomyopathy rose from 12.59% to 20.29%, showing the model relies on person‑specific electrical activity.
Ejection fraction — the fraction of blood expelled by the left ventricle each beat — was able to distinguish reduced function at thresholds of 50%, 45% and 35% with AUC values ranging from 0.79–0.81. AUC measures how well the model separates groups.
In a follow‑up cohort of 17,566 subjects, the same features yielded a five‑year heart‑failure forecast with a C‑index of 0.76, compared with 0.63 for a forecast based on conventional ECG parameters. A single predicted ventricular‑motion sequence thus provides both anatomical measurements and risk prediction.
🔗 Read original →
On September 4, researchers posted a preprint on medRxiv preprint, September 4 describing VisionECG, a model trained on 71,132 ECG‑MRI pairs from the UK Biobank. The model learns to generate a sequence of 50 successive 3‑D surfaces of the left ventricle from a standard 12‑lead ECG and eight basic subject characteristics.
For 5,000 patients with both ECG and echocardiography, the model‑derived ejection fraction was compared to ultrasound measurements. From the reconstructed ventricular motion the model can compute volume, mass, wall thickness, ejection fraction and myocardial strain.
On an external test set of 2,000 participants the median relative error of volume over the cardiac cycle was 8.34%, and wall‑thickness error was 9.54%. When the individual ECG was replaced by the cohort average, the volume error in dilated cardiomyopathy rose from 12.59% to 20.29%, showing the model relies on person‑specific electrical activity.
Ejection fraction — the fraction of blood expelled by the left ventricle each beat — was able to distinguish reduced function at thresholds of 50%, 45% and 35% with AUC values ranging from 0.79–0.81. AUC measures how well the model separates groups.
In a follow‑up cohort of 17,566 subjects, the same features yielded a five‑year heart‑failure forecast with a C‑index of 0.76, compared with 0.63 for a forecast based on conventional ECG parameters. A single predicted ventricular‑motion sequence thus provides both anatomical measurements and risk prediction.
🔗 Read original →
medRxiv
Reconstructing synthetic hearts from ECG using flow matching
Cardiac imaging enables quantitative assessment of cardiac structure and function but remains constrained by cost, infrastructure and specialist expertise. In contrast, electrocardiogram (ECG) is widely accessible yet underexploited, despite encoding latent…
Arcadia’s Attempt to Turn GFP Orange Yields Only Green Variant
Arcadia Science published a study on 4 September about generating fluorescent proteins with a set colour and brightness. The team tried to shift the green fluorescent protein EGFP toward orange by altering amino acids around the chromophore.
An algorithm directed amino‑acid substitutions toward the target spectrum by comparing similarity to natural fluorescent‑protein variants and the closeness of predicted excitation/emission peaks. For five orange candidates the researchers added a brightness check, using a classifier that labelled variants as bright or dim based on their
🔗 Read original →
Arcadia Science published a study on 4 September about generating fluorescent proteins with a set colour and brightness. The team tried to shift the green fluorescent protein EGFP toward orange by altering amino acids around the chromophore.
An algorithm directed amino‑acid substitutions toward the target spectrum by comparing similarity to natural fluorescent‑protein variants and the closeness of predicted excitation/emission peaks. For five orange candidates the researchers added a brightness check, using a classifier that labelled variants as bright or dim based on their
🔗 Read original →
Insilico Reports 83% Accuracy for TargetPro+ in Predicting Clinical Drug Targets
On September 4, Insilico Medicine
🔗 Read original →
On September 4, Insilico Medicine
🔗 Read original →
PubMed Central (PMC)
Advancing target discovery through disease-specific integration of multi-modal target identification models and comprehensive benchmarking…
Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and ...
SIRT6 RNA stability varies with age and Alzheimer's disease
A preprint released on 7 September describes SIRT6.db, an open database of experiments on six species that links SIRT6 changes to the activity of other genes. The resource lets researchers test whether SIRT6 RNA levels reflect the cellular and tissue aging state. SIRT6 is a nuclear protein that regulates chromatin — the packaging of DNA.
In a study
🔗 Read original →
A preprint released on 7 September describes SIRT6.db, an open database of experiments on six species that links SIRT6 changes to the activity of other genes. The resource lets researchers test whether SIRT6 RNA levels reflect the cellular and tissue aging state. SIRT6 is a nuclear protein that regulates chromatin — the packaging of DNA.
In a study
🔗 Read original →
We need to translate Russian news post into English, format per rules.
We need a 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: Thomas Kaiser, date 8 сентября (September 8), column BioSpace, physician, medicinal chemist, ML specialist. He discusses conditions where foundation models can help drug development. Foundation model learns on broad open data then fine-tuned for specific task. He proposes checking quality of scientific results it was trained on and preservation of link between molecular structure and its property in new chemical series. Engineer can configure one version of model so that for same query it responds identically. This repeatability speaks to computation tuning. Scientific publication may report a link that further research cannot reproduce, and model will stably repeat it. Kaiser formulates risk: "Reproducible link based on non-reproducible science, alas, does not become progress." Repeatability of answer and verification and checking scientific link are two different tasks. Second risk Kaiser shows on small molecules — comparatively small organic compounds. Chemical series — family of molecules with similar structure. One and the same small structural edit can differently change property of molecule in two series. At Avicenna Biosciences Kaiser describes such problem: a rule useful for prediction in one series may throw model off in another, because for this edit the causal link with molecular property changes. Therefore Kaiser proposes setting foundation model a narrow scientific question, in which chemical context already given. Among potential tasks he names searching biological networks — linked genes and proteins that could become drug targets — and analyzing clinical data for patient selection. Model can also search link between measured treatment outcomes, patient selection, and preclinical models reproducing disease signs. Experimental verification of these links and careful data assessment Kaiser leaves to researcher.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. Important facts: date September 8 (8 сентября). Company names: BioSpace (maybe a column, but it's a publication), Avicenna Biosciences. Possibly also "Thomas Kaiser". Also maybe "small molecules". But we need at most 4-5 double asterisks. Choose: September 8, BioSpace, Avicenna Biosciences, maybe Thomas Kaiser? But name may be considered important. However we need to be selective. Let's pick date, column, company, maybe "small molecules"? But small molecules is a class not a specific name. Could also include "foundation model". But that's generic. Let's choose: September 8, BioSpace, Avicenna Biosciences, Thomas Kaiser (maybe 4). Ensure not whole sentence.…
🔗 Read original →
We need a 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: Thomas Kaiser, date 8 сентября (September 8), column BioSpace, physician, medicinal chemist, ML specialist. He discusses conditions where foundation models can help drug development. Foundation model learns on broad open data then fine-tuned for specific task. He proposes checking quality of scientific results it was trained on and preservation of link between molecular structure and its property in new chemical series. Engineer can configure one version of model so that for same query it responds identically. This repeatability speaks to computation tuning. Scientific publication may report a link that further research cannot reproduce, and model will stably repeat it. Kaiser formulates risk: "Reproducible link based on non-reproducible science, alas, does not become progress." Repeatability of answer and verification and checking scientific link are two different tasks. Second risk Kaiser shows on small molecules — comparatively small organic compounds. Chemical series — family of molecules with similar structure. One and the same small structural edit can differently change property of molecule in two series. At Avicenna Biosciences Kaiser describes such problem: a rule useful for prediction in one series may throw model off in another, because for this edit the causal link with molecular property changes. Therefore Kaiser proposes setting foundation model a narrow scientific question, in which chemical context already given. Among potential tasks he names searching biological networks — linked genes and proteins that could become drug targets — and analyzing clinical data for patient selection. Model can also search link between measured treatment outcomes, patient selection, and preclinical models reproducing disease signs. Experimental verification of these links and careful data assessment Kaiser leaves to researcher.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. Important facts: date September 8 (8 сентября). Company names: BioSpace (maybe a column, but it's a publication), Avicenna Biosciences. Possibly also "Thomas Kaiser". Also maybe "small molecules". But we need at most 4-5 double asterisks. Choose: September 8, BioSpace, Avicenna Biosciences, maybe Thomas Kaiser? But name may be considered important. However we need to be selective. Let's pick date, column, company, maybe "small molecules"? But small molecules is a class not a specific name. Could also include "foundation model". But that's generic. Let's choose: September 8, BioSpace, Avicenna Biosciences, Thomas Kaiser (maybe 4). Ensure not whole sentence.…
🔗 Read original →
BioSpace
Opinion: AI will only revolutionize drug discovery and development if we play to its strengths
Foundation models will change the way we study the natural, applied and clinical sciences, but only under certain conditions, and not in the way that some AI leaders claim.
We need to translate Russian news into English, format per rules.
First line: short headline under 90 chars, no markdown. Then blank line, then body paragraphs short (2-3 sentences each), separated by blank lines.
We need to wrap few genuinely important facts (key numbers, percentages, drug/company/gene names, dates) in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026).
We must preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: FDA authorized eGenesis to test genetically prepared pig kidney on 33 people on September 3. eGenesis reported: in expanded access program, genetically prepared pig kidney EGEN-2784 given to five patients with kidney failure. Three had kidney working without dialysis >8 months; company expects to start trial in 2027 on 33 people from human kidney waiting list. Severe kidney failure requires dialysis or waiting for donor kidney. In September 3 release, eGenesis says from March 2024 they transplanted kidney to five patients via expanded access. Three kidney worked without dialysis >8 months, one >9 months; two then received human donor kidneys. Xenotransplantation is transplant of animal organ to human. One transition from pig kidney to human described in article in medical journal The Lancet. Tim Andrews received pig kidney Jan 25 2025; 271 days he was dialysis-free. Damage to small vessels caused graft failure, removed. After 82 days Andrews received human kidney, which started immediately. In EGEN-2784 cells, three pig antigens removed — molecules by which immune system recognizes foreign tissue. According to eGenesis, seven added human genes should regulate immune response, inflammation, and blood clotting. eGenesis says FDA authorized trial RESTORE for 33 patients aged 50-70 already on human kidney waiting list. Company expects to start study Q1 2027.
We need to select at most 4-5 double-asterisk facts. Likely: September 3, 33 patients, EGEN-2784, five patients, >8 months, >9 months, March 2024, Tim Andrews, 271 days, January 25 2025, first quarter 2027, RESTORE, 50-70 years. But we need only 4-5. Choose most salient: FDA authorized test on 33 patients on September 3; five patients received EGEN-2784 via expanded access since March 2024; three kidneys functioned without dialysis >8 months (one >9 months); FDA authorized RESTORE trial for 33 patients aged 50‑70 to start Q1 2027. That's 5 double-asterisk facts.
We need to ensure we don't wrap whole sentences. So we embed the markers around those specific phrases.
Citations: The Lancet article. Wrap in single underscores: The Lancet.
Also maybe mention "eGenesis" but that's a company name; not required to be underscored.
Now produce translation.
Headline: something like "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients". Under 90 chars.
Let's count: "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients" length: count characters: F(1) D2 A3 space4 C5 l6 e7 a8 r9 s10 space11 e12 G13 e14 n15 e16 s17 i18 s19 space20 t21 o22 space23 T24 e25 s26 t27 space28 P29 i30 g31 space32 K33 i34 d35 n36 e37 y38 space39 E40 G41 E42 N43 -44 2 45 7 46 8 47 4 48 space49 i50 n51 space52 3 53 3 54 space55 P56 a57 t58 i59 e60 n61 t62 s63. That's about 63 chars. Good.
Now blank line.
Now body paragraphs.…
🔗 Read original →
First line: short headline under 90 chars, no markdown. Then blank line, then body paragraphs short (2-3 sentences each), separated by blank lines.
We need to wrap few genuinely important facts (key numbers, percentages, drug/company/gene names, dates) in double asterisks . At most 4-5 per post, never a whole sentence.
Wrap study/journal citations and publication references in single underscores _ (e.g., Nature Aging, July 2026).
We must preserve all facts, numbers, names, citations exactly. No added commentary.
We need to identify important facts: FDA authorized eGenesis to test genetically prepared pig kidney on 33 people on September 3. eGenesis reported: in expanded access program, genetically prepared pig kidney EGEN-2784 given to five patients with kidney failure. Three had kidney working without dialysis >8 months; company expects to start trial in 2027 on 33 people from human kidney waiting list. Severe kidney failure requires dialysis or waiting for donor kidney. In September 3 release, eGenesis says from March 2024 they transplanted kidney to five patients via expanded access. Three kidney worked without dialysis >8 months, one >9 months; two then received human donor kidneys. Xenotransplantation is transplant of animal organ to human. One transition from pig kidney to human described in article in medical journal The Lancet. Tim Andrews received pig kidney Jan 25 2025; 271 days he was dialysis-free. Damage to small vessels caused graft failure, removed. After 82 days Andrews received human kidney, which started immediately. In EGEN-2784 cells, three pig antigens removed — molecules by which immune system recognizes foreign tissue. According to eGenesis, seven added human genes should regulate immune response, inflammation, and blood clotting. eGenesis says FDA authorized trial RESTORE for 33 patients aged 50-70 already on human kidney waiting list. Company expects to start study Q1 2027.
We need to select at most 4-5 double-asterisk facts. Likely: September 3, 33 patients, EGEN-2784, five patients, >8 months, >9 months, March 2024, Tim Andrews, 271 days, January 25 2025, first quarter 2027, RESTORE, 50-70 years. But we need only 4-5. Choose most salient: FDA authorized test on 33 patients on September 3; five patients received EGEN-2784 via expanded access since March 2024; three kidneys functioned without dialysis >8 months (one >9 months); FDA authorized RESTORE trial for 33 patients aged 50‑70 to start Q1 2027. That's 5 double-asterisk facts.
We need to ensure we don't wrap whole sentences. So we embed the markers around those specific phrases.
Citations: The Lancet article. Wrap in single underscores: The Lancet.
Also maybe mention "eGenesis" but that's a company name; not required to be underscored.
Now produce translation.
Headline: something like "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients". Under 90 chars.
Let's count: "FDA Clears eGenesis to Test Pig Kidney EGEN-2784 in 33 Patients" length: count characters: F(1) D2 A3 space4 C5 l6 e7 a8 r9 s10 space11 e12 G13 e14 n15 e16 s17 i18 s19 space20 t21 o22 space23 T24 e25 s26 t27 space28 P29 i30 g31 space32 K33 i34 d35 n36 e37 y38 space39 E40 G41 E42 N43 -44 2 45 7 46 8 47 4 48 space49 i50 n51 space52 3 53 3 54 space55 P56 a57 t58 i59 e60 n61 t62 s63. That's about 63 chars. Good.
Now blank line.
Now body paragraphs.…
🔗 Read original →
The Lancet
Porcine kidney xenotransplantation as a bridge to allotransplantation: a first-in-human study
This case shows that porcine kidney xenotransplantation can provide prolonged renal
support and be discontinued without clinically significant allosensitisation or zoonotic
infection. Early cellular rejection resolved with treatment, whereas later graft failure…
support and be discontinued without clinically significant allosensitisation or zoonotic
infection. Early cellular rejection resolved with treatment, whereas later graft failure…
We need to translate Russian news into English, format with headline line under 90 chars, blank line, then short paragraphs 2-3 sentences each, separated by blank lines. Wrap few important facts (key numbers, percentages, drug/company/gene names, dates) in double asterisks, at most 4-5 per post. Wrap study/journal citations and publication references in single underscores (e.g., Nature Aging, July 2026). There are no explicit citations in the Russian text; we should not add any. So we will not use underscores unless there is a citation; none present. So we will not use underscores.
We need to select at most 4-5 double-asterisk facts. Important facts: 9 billion possible SNVs, 27,000 predictions per mutation, 100 million indels, 2,500 motifs, AVI score definition, 98% noncoding, 2% coding, test on patients, narrowing from 526 to 4 candidates in UK Biobank of 54,000 people. Also mention DeepMind, AlphaGenome Atlas, free for academics, commercial via Google Cloud soon. Need to pick maybe 5 facts: 9 billion SNVs, 27k predictions per mutation, 100 million indels, AVI score definition (AVI 10 top 10%, AVI 30 top 0.1%), 98% noncoding, 2% coding (that's two numbers but could count as one fact? maybe combine). We need at most 4-5; we can choose 5: 9 billion SNVs, 27k predictions per mutation, 100 million indels, AVI score definition (maybe two subpoints but could be considered one fact), 98% noncoding / 2% coding (maybe considered one). That's 5.
We must wrap each fact in double asterisks. Ensure we don't wrap whole sentences. So embed numbers etc.
We need to keep the translation natural. Let's craft.
Headline: maybe "DeepMind launches AlphaGenome Atlas, AI‑driven catalog of 9 billion human SNVs". Under 90 chars.
Then blank line.
Paragraph 1: describe database, free for academics, commercial via Google Cloud soon.
Paragraph 2: how built: AlphaGenome network analyzed reference genome, each base substitution yields predictions, each SNV has avg 27k predictions across cell types, etc.
Paragraph 3: includes indels, motifs, noncoding vs coding insight.
Paragraph 4: AVI score explanation.
Paragraph 5: real‑patient test results, narrowing candidates.
Make sure each paragraph 2-3 sentences.
Now embed double asterisks around selected facts.
Facts:
- 9 billion possible single‑nucleotide mutations
- each SNV accompanied by ≈27 000 individual predictions
- >100 million insertions and deletions
- AVI score: AVI 10 = top 10 % impact, AVI 30 = top 0.1 % impact
- 98 % of the genome is non‑coding (regulatory), only 2 % codes for proteins
We need to ensure we don't exceed 4-5 facts; we have 5 facts (the last includes two percentages but it's one fact about composition). Good.
Now ensure we don't accidentally double asterisk other numbers like 54 000, 526, 4, etc. Those are not to be highlighted per rule (we can leave them plain). Also avoid wrapping whole sentences.
Now produce final answer.
Check length of headline: "DeepMind launches AlphaGenome Atlas, AI‑driven catalog of 9 billion human SNVs". Count characters: Let's count roughly: DeepMind (8) + space (1) =9, launches (8) =>17, space=18, AlphaGenome (11) =>29, space=30, Atlas, (6 inc comma?) Actually "Atlas," 6 inc comma =>36, space=37, AI‑driven (9 inc hyphen?) =>46, space=47, catalog (7)=>54, space=55, of (2)=>57, space=58, 9 billion (maybe 10 inc space?) Actually "9 billion" includes a space? We have space before 9? Already have space after of. So "9 billion" is 10 inc space? Let's just trust it's under 90. It's fine.
Now paragraphs.
Paragraph 1: "DeepMind has announced the release of the AlphaGenome Atlas, a free scientific catalogue that stores AI‑generated predictions of the biological consequences of every possible single‑nucleotide change in the human genome. Academic researchers can access the database at no cost, while commercial access via Google Cloud is slated to launch in the near future."…
🔗 Read original →
We need to select at most 4-5 double-asterisk facts. Important facts: 9 billion possible SNVs, 27,000 predictions per mutation, 100 million indels, 2,500 motifs, AVI score definition, 98% noncoding, 2% coding, test on patients, narrowing from 526 to 4 candidates in UK Biobank of 54,000 people. Also mention DeepMind, AlphaGenome Atlas, free for academics, commercial via Google Cloud soon. Need to pick maybe 5 facts: 9 billion SNVs, 27k predictions per mutation, 100 million indels, AVI score definition (AVI 10 top 10%, AVI 30 top 0.1%), 98% noncoding, 2% coding (that's two numbers but could count as one fact? maybe combine). We need at most 4-5; we can choose 5: 9 billion SNVs, 27k predictions per mutation, 100 million indels, AVI score definition (maybe two subpoints but could be considered one fact), 98% noncoding / 2% coding (maybe considered one). That's 5.
We must wrap each fact in double asterisks. Ensure we don't wrap whole sentences. So embed numbers etc.
We need to keep the translation natural. Let's craft.
Headline: maybe "DeepMind launches AlphaGenome Atlas, AI‑driven catalog of 9 billion human SNVs". Under 90 chars.
Then blank line.
Paragraph 1: describe database, free for academics, commercial via Google Cloud soon.
Paragraph 2: how built: AlphaGenome network analyzed reference genome, each base substitution yields predictions, each SNV has avg 27k predictions across cell types, etc.
Paragraph 3: includes indels, motifs, noncoding vs coding insight.
Paragraph 4: AVI score explanation.
Paragraph 5: real‑patient test results, narrowing candidates.
Make sure each paragraph 2-3 sentences.
Now embed double asterisks around selected facts.
Facts:
- 9 billion possible single‑nucleotide mutations
- each SNV accompanied by ≈27 000 individual predictions
- >100 million insertions and deletions
- AVI score: AVI 10 = top 10 % impact, AVI 30 = top 0.1 % impact
- 98 % of the genome is non‑coding (regulatory), only 2 % codes for proteins
We need to ensure we don't exceed 4-5 facts; we have 5 facts (the last includes two percentages but it's one fact about composition). Good.
Now ensure we don't accidentally double asterisk other numbers like 54 000, 526, 4, etc. Those are not to be highlighted per rule (we can leave them plain). Also avoid wrapping whole sentences.
Now produce final answer.
Check length of headline: "DeepMind launches AlphaGenome Atlas, AI‑driven catalog of 9 billion human SNVs". Count characters: Let's count roughly: DeepMind (8) + space (1) =9, launches (8) =>17, space=18, AlphaGenome (11) =>29, space=30, Atlas, (6 inc comma?) Actually "Atlas," 6 inc comma =>36, space=37, AI‑driven (9 inc hyphen?) =>46, space=47, catalog (7)=>54, space=55, of (2)=>57, space=58, 9 billion (maybe 10 inc space?) Actually "9 billion" includes a space? We have space before 9? Already have space after of. So "9 billion" is 10 inc space? Let's just trust it's under 90. It's fine.
Now paragraphs.
Paragraph 1: "DeepMind has announced the release of the AlphaGenome Atlas, a free scientific catalogue that stores AI‑generated predictions of the biological consequences of every possible single‑nucleotide change in the human genome. Academic researchers can access the database at no cost, while commercial access via Google Cloud is slated to launch in the near future."…
🔗 Read original →
Google
AlphaGenome
AlphaGenome – Access Google DeepMind’s unifying genomics model for deciphering DNA function.
Heart MRI framing and processing shape genetic discovery in UK Biobank study
On September 4 Devanj Pandey and Wagish Narasimhan posted a preprint analyzing cardiac MRI from 69,932 participants in the UK Biobank. Their self‑supervised model reconstructed masked frames of the cine‑MRI videos without physician labels and used the resulting numerical features to search for DNA‑variant associations.
Cine‑MRI shows a beating heart that occupies only about one‑quarter to one‑third of each frame, with the rest filled by chest, spine and acquisition details. The model turned each participant’s three cine‑MRI projections into a set of 2,304 numbers, which were then compressed to the 20 principal components representing the largest differences between individuals.
For predicting the scan site the model achieved R² = 0.55, and for predicting left‑ventricular ejection fraction it reached R² = 0.37; higher R² indicates more accurate prediction. These values show how much of the variance in each trait is captured by the imaging‑derived features.
Before the genome‑wide search the researchers cropped the frames to the moving heart region, removing roughly 65–75% of the chest field, and regressed out age, sex, body‑mass index, height and scan site from all 2,304 features. Only after this cleanup were the 20 principal components recomputed for the genetic analysis.
Using the full feature set they identified 101 genome loci, including all 11 previously known heart‑related regions. A later adjustment that corrected the principal components yielded 60 loci and one novel candidate. In a hold‑out sample of 13,717 participants whose scans were not used for model training, the effect sizes of the top variants matched those from the full sample.
🔗 Read original →
On September 4 Devanj Pandey and Wagish Narasimhan posted a preprint analyzing cardiac MRI from 69,932 participants in the UK Biobank. Their self‑supervised model reconstructed masked frames of the cine‑MRI videos without physician labels and used the resulting numerical features to search for DNA‑variant associations.
Cine‑MRI shows a beating heart that occupies only about one‑quarter to one‑third of each frame, with the rest filled by chest, spine and acquisition details. The model turned each participant’s three cine‑MRI projections into a set of 2,304 numbers, which were then compressed to the 20 principal components representing the largest differences between individuals.
For predicting the scan site the model achieved R² = 0.55, and for predicting left‑ventricular ejection fraction it reached R² = 0.37; higher R² indicates more accurate prediction. These values show how much of the variance in each trait is captured by the imaging‑derived features.
Before the genome‑wide search the researchers cropped the frames to the moving heart region, removing roughly 65–75% of the chest field, and regressed out age, sex, body‑mass index, height and scan site from all 2,304 features. Only after this cleanup were the 20 principal components recomputed for the genetic analysis.
Using the full feature set they identified 101 genome loci, including all 11 previously known heart‑related regions. A later adjustment that corrected the principal components yielded 60 loci and one novel candidate. In a hold‑out sample of 13,717 participants whose scans were not used for model training, the effect sizes of the top variants matched those from the full sample.
🔗 Read original →
medRxiv
Field-of-view confounding shapes genetic discovery from self-supervised cardiac-imaging phenotypes
Self-supervised models increasingly convert medical images into quantitative phenotypes for biological discovery, but statistical reproducibility does not establish that a learned phenotype represents the intended anatomy. We trained a video masked-autoencoder…
John Tower proposes model linking adaptation, aging, and sexual conflict
On September 7, biologist John Tower of the University of Southern California posted a preprint on bioRxiv outlining a model that connects adaptive state switching with aging and sexual conflict. He describes how a regulated exit from a maintained state can be beneficial now but costly later, with cells needing to preserve functional configurations while sometimes requiring a different response
🔗 Read original →
On September 7, biologist John Tower of the University of Southern California posted a preprint on bioRxiv outlining a model that connects adaptive state switching with aging and sexual conflict. He describes how a regulated exit from a maintained state can be beneficial now but costly later, with cells needing to preserve functional configurations while sometimes requiring a different response
🔗 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 leading '#'.
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:
- In developing mouse heart, 23 of 50 tested genes altered cardiomyocyte maturation programs.
- September 7 2026 article in Nature Cardiovascular Research about PIP-seq method linking gene knockout to cardiomyocyte RNA changes.
- Pilot screen: 23 genes altered at least one maturation program; 21 of them were previously uncharacterized regulators.
- After birth, cardiomyocytes grow, organize sarcomeres, change energy metabolism and electrical properties.
- Authors tracked transition at birth and at 7, 14, 21 days: read RNA of 67 thousand nuclei and mapped position of 402 thousand cells on heart sections.
- Largest gene activity changes occurred in first 14 days; therefore knockout effect evaluated at day 14.
- To test candidate function, injected AAV9 library into hearts of newborn mice; each construct carried two CRISPR guide RNAs leading Cas9 to target gene.
- After tissue fixation, PIP-seq simultaneously identified in nucleus the guide, editing marker of its gene, and RNA produced after intervention. So one experiment linked specific gene knockout to cardiomyocyte response within heart.
- Screen yielded 5,706 nuclei with assigned guide.
- Genes Srf and Ppargc1a, whose role in maturation already known, served as positive controls and gave expected maturation program disruptions.
- Knocking out Rad21, a gene linked to DNA packaging, altered activity of genes related to contractile fiber assembly, electrical properties, and fatty acid metabolism.
- Authors separately knocked out gene Rreb1.
- By day 35 after birth, cardiomyocytes with Rreb1 knockout were shorter, and their T-tubules — membrane channels conducting electrical signal into cell — more often had disrupted structure.
- Mitochondria of these cells consumed less oxygen at maximal load.
- Mice with Rreb1 knockout showed reduced fractional shortening and ejection fraction — two measures of cardiac contractile function on ultrasound.
- Separate validation linked screen finding to changes in cell structure, its energetic work, and heart function.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post.
Select important facts: numbers: 23 of 50 genes, September 7 2026, 67 thousand nuclei, 402 thousand cells, first 14 days, day 14 evaluation, 5,706 nuclei, day 35, fractional shortening/ejection fraction (maybe not numeric). Gene names: Srf, Ppargc1a, Rad21, Rreb1. Also AAV9. Also PIP-seq. Also Nature Cardiovascular Research.
We need to limit double asterisks to at most 4-5 per post. So we need to be selective: maybe highlight: 23 of 50 genes, September 7 2026, 67 000 nuclei, 402 000 cells, day 14, 5 706 nuclei, day 35, Srf, Ppargc1a, Rad21, Rreb1... That's many. We need only 4-5 total. So we must choose only a few key facts.
Which are most important? Probably the screen results: 23 of 50 genes altered; 21 previously uncharacterized; the date; the number of nuclei/cells; the day 14 evaluation; the day 35 phenotype; maybe the gene names of interest: Rreb1.
But we need to keep double asterisks limited. Let's decide to wrap: 23 of 50 genes, September 7 2026, 67 000 nuclei, 402 000 cells, day 14 (or day 35). That's 5.…
🔗 Read original →
First line: short headline under 90 chars, no markdown, no leading '#'.
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:
- In developing mouse heart, 23 of 50 tested genes altered cardiomyocyte maturation programs.
- September 7 2026 article in Nature Cardiovascular Research about PIP-seq method linking gene knockout to cardiomyocyte RNA changes.
- Pilot screen: 23 genes altered at least one maturation program; 21 of them were previously uncharacterized regulators.
- After birth, cardiomyocytes grow, organize sarcomeres, change energy metabolism and electrical properties.
- Authors tracked transition at birth and at 7, 14, 21 days: read RNA of 67 thousand nuclei and mapped position of 402 thousand cells on heart sections.
- Largest gene activity changes occurred in first 14 days; therefore knockout effect evaluated at day 14.
- To test candidate function, injected AAV9 library into hearts of newborn mice; each construct carried two CRISPR guide RNAs leading Cas9 to target gene.
- After tissue fixation, PIP-seq simultaneously identified in nucleus the guide, editing marker of its gene, and RNA produced after intervention. So one experiment linked specific gene knockout to cardiomyocyte response within heart.
- Screen yielded 5,706 nuclei with assigned guide.
- Genes Srf and Ppargc1a, whose role in maturation already known, served as positive controls and gave expected maturation program disruptions.
- Knocking out Rad21, a gene linked to DNA packaging, altered activity of genes related to contractile fiber assembly, electrical properties, and fatty acid metabolism.
- Authors separately knocked out gene Rreb1.
- By day 35 after birth, cardiomyocytes with Rreb1 knockout were shorter, and their T-tubules — membrane channels conducting electrical signal into cell — more often had disrupted structure.
- Mitochondria of these cells consumed less oxygen at maximal load.
- Mice with Rreb1 knockout showed reduced fractional shortening and ejection fraction — two measures of cardiac contractile function on ultrasound.
- Separate validation linked screen finding to changes in cell structure, its energetic work, and heart function.
We need to wrap key numbers, percentages, drug/company/gene names, dates in double asterisks. At most 4-5 per post.
Select important facts: numbers: 23 of 50 genes, September 7 2026, 67 thousand nuclei, 402 thousand cells, first 14 days, day 14 evaluation, 5,706 nuclei, day 35, fractional shortening/ejection fraction (maybe not numeric). Gene names: Srf, Ppargc1a, Rad21, Rreb1. Also AAV9. Also PIP-seq. Also Nature Cardiovascular Research.
We need to limit double asterisks to at most 4-5 per post. So we need to be selective: maybe highlight: 23 of 50 genes, September 7 2026, 67 000 nuclei, 402 000 cells, day 14, 5 706 nuclei, day 35, Srf, Ppargc1a, Rad21, Rreb1... That's many. We need only 4-5 total. So we must choose only a few key facts.
Which are most important? Probably the screen results: 23 of 50 genes altered; 21 previously uncharacterized; the date; the number of nuclei/cells; the day 14 evaluation; the day 35 phenotype; maybe the gene names of interest: Rreb1.
But we need to keep double asterisks limited. Let's decide to wrap: 23 of 50 genes, September 7 2026, 67 000 nuclei, 402 000 cells, day 14 (or day 35). That's 5.…
🔗 Read original →
Nature
Spatially guided in vivo single-cell functional genomics of postnatal heart
Nature Cardiovascular Research - By integrating single-nucleus RNA sequencing and spatial transcriptomics, Wang, Dong, Song et al. generated a high-resolution spatiotemporal atlas of the postnatal...
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 "технической работе команды" but no explicit journal citation. It mentions "В данных Британского биобанка UK Biobank — 54 189 участников и уровни 2 028 белков в крови". No citation.
Thus we may have no citations to wrap. But we can still wrap any mention like "UK Biobank" maybe not needed. The rule: wrap study/journal citations and publication references in single underscores. If none, we just don't use underscores.
We need to select at most 4-5 important facts to wrap in . Likely: 9 billion possible single-letter DNA substitutions, date September 8, AVI score, DNM1 gene, 12 intron variants, 3 previously described pathogenic variants, 54,189 participants, 2,028 blood proteins, 728 links vs 595 baseline, 22% increase.
We need to be selective: at most 4-5 per post. Choose maybe: 9 billion, September 8, DNM1 gene, 12 intron variants (3 pathogenic), 54,189 participants, 2,028 proteins, 728 links, 22% increase. That's many. Need limit to 4-5.
We can combine some: maybe wrap "9 billion", "8 September", "DNM1", "54 189 participants", "22 % increase". That's 5.
Alternatively include "AVI score". But we need to be selective. Let's decide: 9 billion, 8 September, DNM1, 54 189 participants, 22 % increase. That's five.
We must ensure we don't wrap whole sentence. Just wrap those tokens.
Now produce translation.
First line headline: maybe "DeepMind releases AlphaGenome Atlas mapping 9 billion DNA single‑letter changes". Count chars: Let's count: "DeepMind releases AlphaGenome Atlas mapping 9 billion DNA single‑letter changes". Count characters including spaces: DeepMind(8) + space=1 =>9, releases(8)=>17, space=18, AlphaGenome(11)=>29, space=30, Atlas(5)=>35, space=36, mapping(6)=>42, space=43, 9 billion (maybe "9 billion" counts as 2? "9" plus space? Actually "9 billion": "9" (1) + non-breaking space? We'll count as "9" (1) + space (1) + "billion"(7)=9 => total 43+9=52, space=53, DNA(3)=56, space=57, single‑letter (maybe hyphen counts) "single‑letter": 13? Let's approximate: s(1)i2n3g4l5e6‑7l8e9t10t11e12r13 =>13, space => after that? Actually "single‑letter" then space then "changes". So add 13 =>70, space=71, changes(7)=78. So under 90. Good.
Now blank line.
Now body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft translation:
Paragraph 1: Introduce AlphaGenome Atlas, previous model, new map.
Paragraph 2: Explain purpose, AVI score, components.
Paragraph 3: Example with DNM1 gene, splicing, experimental validation.
Paragraph 4: UK Biobank analysis, grouping rare non-coding variants, results.
Paragraph 5: Note that model input is DNA sequence, need experimental verification.
Make sure each paragraph 2-3 sentences.
Now incorporate double asterisks around selected facts.
We need to ensure we don't accidentally wrap whole sentence.
We'll embed 9 billion, 8 September, DNM1, 54 189 participants, 22 % increase.
Check each appears only once.
Now produce final.
Let's draft:…
🔗 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 "технической работе команды" but no explicit journal citation. It mentions "В данных Британского биобанка UK Biobank — 54 189 участников и уровни 2 028 белков в крови". No citation.
Thus we may have no citations to wrap. But we can still wrap any mention like "UK Biobank" maybe not needed. The rule: wrap study/journal citations and publication references in single underscores. If none, we just don't use underscores.
We need to select at most 4-5 important facts to wrap in . Likely: 9 billion possible single-letter DNA substitutions, date September 8, AVI score, DNM1 gene, 12 intron variants, 3 previously described pathogenic variants, 54,189 participants, 2,028 blood proteins, 728 links vs 595 baseline, 22% increase.
We need to be selective: at most 4-5 per post. Choose maybe: 9 billion, September 8, DNM1 gene, 12 intron variants (3 pathogenic), 54,189 participants, 2,028 proteins, 728 links, 22% increase. That's many. Need limit to 4-5.
We can combine some: maybe wrap "9 billion", "8 September", "DNM1", "54 189 participants", "22 % increase". That's 5.
Alternatively include "AVI score". But we need to be selective. Let's decide: 9 billion, 8 September, DNM1, 54 189 participants, 22 % increase. That's five.
We must ensure we don't wrap whole sentence. Just wrap those tokens.
Now produce translation.
First line headline: maybe "DeepMind releases AlphaGenome Atlas mapping 9 billion DNA single‑letter changes". Count chars: Let's count: "DeepMind releases AlphaGenome Atlas mapping 9 billion DNA single‑letter changes". Count characters including spaces: DeepMind(8) + space=1 =>9, releases(8)=>17, space=18, AlphaGenome(11)=>29, space=30, Atlas(5)=>35, space=36, mapping(6)=>42, space=43, 9 billion (maybe "9 billion" counts as 2? "9" plus space? Actually "9 billion": "9" (1) + non-breaking space? We'll count as "9" (1) + space (1) + "billion"(7)=9 => total 43+9=52, space=53, DNA(3)=56, space=57, single‑letter (maybe hyphen counts) "single‑letter": 13? Let's approximate: s(1)i2n3g4l5e6‑7l8e9t10t11e12r13 =>13, space => after that? Actually "single‑letter" then space then "changes". So add 13 =>70, space=71, changes(7)=78. So under 90. Good.
Now blank line.
Now body paragraphs.
We need to split into short paragraphs 2-3 sentences each.
Let's craft translation:
Paragraph 1: Introduce AlphaGenome Atlas, previous model, new map.
Paragraph 2: Explain purpose, AVI score, components.
Paragraph 3: Example with DNM1 gene, splicing, experimental validation.
Paragraph 4: UK Biobank analysis, grouping rare non-coding variants, results.
Paragraph 5: Note that model input is DNA sequence, need experimental verification.
Make sure each paragraph 2-3 sentences.
Now incorporate double asterisks around selected facts.
We need to ensure we don't accidentally wrap whole sentence.
We'll embed 9 billion, 8 September, DNM1, 54 189 participants, 22 % increase.
Check each appears only once.
Now produce final.
Let's draft:…
🔗 Read original →
Nature
Advancing regulatory variant effect prediction with AlphaGenome
Nature - AlphaGenome, a deep learning model that inputs 1-Mb DNA sequence to predict functional genomic tracks at single-base resolution across diverse modalities, outperforms existing models in...
MetaGNN authors retract claim that reaction links boosted model accuracy
In a September 7 preprint, the authors of MetaGNN retracted their earlier claim that incorporating reaction‑network links improved model performance by 0.105 AUROC. They said the claim was based on a flawed benchmark.
The model was evaluated on 5,925 reactions labeled ‘active’ by applying a threshold to the input gene‑activity scores. Simply ranking those scores gives an AUROC of 1.0000, indicating the benchmark only tested whether the model could reproduce the rule used to create the labels.
For a separate set of 4,675 reactions, the labels could be reproduced exactly from a fixed RandomState(42) generator, and the same label set appeared for every patient. In colon‑cancer analyses, the input features for 624 patients were all zeros, so the model had no patient‑specific variation to learn from.
After reconstructing the input data, MetaGNN obtained an AUROC of 0.5800. Ranking the raw gene‑expression values yielded 0.6342, while a baseline that only records whether a gene is expressed gave 0.608
🔗 Read original →
In a September 7 preprint, the authors of MetaGNN retracted their earlier claim that incorporating reaction‑network links improved model performance by 0.105 AUROC. They said the claim was based on a flawed benchmark.
The model was evaluated on 5,925 reactions labeled ‘active’ by applying a threshold to the input gene‑activity scores. Simply ranking those scores gives an AUROC of 1.0000, indicating the benchmark only tested whether the model could reproduce the rule used to create the labels.
For a separate set of 4,675 reactions, the labels could be reproduced exactly from a fixed RandomState(42) generator, and the same label set appeared for every patient. In colon‑cancer analyses, the input features for 624 patients were all zeros, so the model had no patient‑specific variation to learn from.
After reconstructing the input data, MetaGNN obtained an AUROC of 0.5800. Ranking the raw gene‑expression values yielded 0.6342, while a baseline that only records whether a gene is expressed gave 0.608
🔗 Read original →
bioRxiv
Benchmark validity in graph neural network scoring of metabolic reaction activity on Recon3D: detecting label leakage, memorized…
Context-specific genome-scale metabolic modeling begins with scoring which of the approximately 10,600 human reactions are active in a patient's tumor. Methods in this literature are routinely benchmarked against activity labels obtained by thresholding the…
Boosting ALDOC enzyme improves heart function and reduces scar after heart attack in mice
On September 8, the journal Cell Death & Disease reported that researchers administered the Aldoc gene to adult male mice using an AAV9 vector immediately after inducing a myocardial infarction by ligating the left anterior descending coronary artery. Each group contained six mice, with controls receiving the same vector lacking Aldoc.
Twenty‑eight days later, the Aldoc‑treated hearts showed an ejection fraction of 56.74%, compared with 36.50% in controls.
The scar occupied 21.80% of the left‑ventricular area versus 41.95% in the control group.
The authors traced the effect to ALDOC’s interaction with the RNA‑processing protein RBM39; excess ALDOC reduced ubiquitination of RBM39, stabilizing it and thereby enhancing PI3K/AKT signaling, a pathway linked to cell division. In cell experiments, inhibiting PI3K/AKT weakened cardiomyocyte proliferation markers, and knocking down Rbm39 in the infarct model blunted the functional and scar‑size improvements conferred by ALDOC overexpression.
Earlier work showed that ALDOC levels are high in newborn mouse hearts, drop sharply by day seven, and rise again after cardiac injury; overexpressing ALDOC in neonatal cardiomyocytes increased glucose breakdown and proliferation signs, while its suppression impaired these processes. After apical resection in newborn mice, ALDOC knockdown led to poorer pumping function and larger scars at 28 days, linking the neonatal regenerative phenotype to the adult infarct rescue.
🔗 Read original →
On September 8, the journal Cell Death & Disease reported that researchers administered the Aldoc gene to adult male mice using an AAV9 vector immediately after inducing a myocardial infarction by ligating the left anterior descending coronary artery. Each group contained six mice, with controls receiving the same vector lacking Aldoc.
Twenty‑eight days later, the Aldoc‑treated hearts showed an ejection fraction of 56.74%, compared with 36.50% in controls.
The scar occupied 21.80% of the left‑ventricular area versus 41.95% in the control group.
The authors traced the effect to ALDOC’s interaction with the RNA‑processing protein RBM39; excess ALDOC reduced ubiquitination of RBM39, stabilizing it and thereby enhancing PI3K/AKT signaling, a pathway linked to cell division. In cell experiments, inhibiting PI3K/AKT weakened cardiomyocyte proliferation markers, and knocking down Rbm39 in the infarct model blunted the functional and scar‑size improvements conferred by ALDOC overexpression.
Earlier work showed that ALDOC levels are high in newborn mouse hearts, drop sharply by day seven, and rise again after cardiac injury; overexpressing ALDOC in neonatal cardiomyocytes increased glucose breakdown and proliferation signs, while its suppression impaired these processes. After apical resection in newborn mice, ALDOC knockdown led to poorer pumping function and larger scars at 28 days, linking the neonatal regenerative phenotype to the adult infarct rescue.
🔗 Read original →
Nature
ALDOC promotes cardiomyocyte proliferation and heart regeneration via suppressing RBM39-ubiquitination
Cell Death & Disease - ALDOC promotes cardiomyocyte proliferation and heart regeneration via suppressing RBM39-ubiquitination
Language models produce many erroneous references in cardiology reviews
The preprint examined 270 medical reviews, finding that 76.8% of erroneous links pointed to a real but different article; it was posted on medRxiv on 4 September. Researchers used three language models with web‑search to write cardiology reviews and checked 8050 references against PubMed.
One review placed PMID 17565084 next to a paper on cardiac resynchronization therapy, but that PubMed record actually opens a JAMA article about NIH grants for early‑career physician‑researchers. Verifying a reference requires first matching the identifier to the named article, then checking whether that article supports the claim it was cited for.
Each of the three models wrote ten reviews for each of nine cardiology topics; each review was 600–900 words long and contained 30 references. Of the 8036 references that had a PMID or DOI, problematic rates were 11.5% for GPT‑5.5, 29.2% for Claude Opus 4.8, and 29.6% for Gemini 3.5 Flash. Most problems were substitutions: a valid PMID or DOI attached to the wrong article, accounting for 76.8% of all errors.
The authors applied CoVe – a chain‑of‑verification method where the model breaks a reference into separate questions, retrieves the PubMed record by the given number, independently searches for the article by title and first author, then compares authors, journal, year, volume and pages. In a set of 270 references annotated by a cardiologist, CoVe found 60 of 62 problems, including all 51 substitutions and all four fabricated articles; three correct links were mistakenly flagged as problematic.
Separately, the team matched nine statements from the reviews with the abstracts of the cited articles. Reference checking consists of two steps: the identifier and title must lead to the same article, and that article must actually support the statement in the text.
🔗 Read original →
The preprint examined 270 medical reviews, finding that 76.8% of erroneous links pointed to a real but different article; it was posted on medRxiv on 4 September. Researchers used three language models with web‑search to write cardiology reviews and checked 8050 references against PubMed.
One review placed PMID 17565084 next to a paper on cardiac resynchronization therapy, but that PubMed record actually opens a JAMA article about NIH grants for early‑career physician‑researchers. Verifying a reference requires first matching the identifier to the named article, then checking whether that article supports the claim it was cited for.
Each of the three models wrote ten reviews for each of nine cardiology topics; each review was 600–900 words long and contained 30 references. Of the 8036 references that had a PMID or DOI, problematic rates were 11.5% for GPT‑5.5, 29.2% for Claude Opus 4.8, and 29.6% for Gemini 3.5 Flash. Most problems were substitutions: a valid PMID or DOI attached to the wrong article, accounting for 76.8% of all errors.
The authors applied CoVe – a chain‑of‑verification method where the model breaks a reference into separate questions, retrieves the PubMed record by the given number, independently searches for the article by title and first author, then compares authors, journal, year, volume and pages. In a set of 270 references annotated by a cardiologist, CoVe found 60 of 62 problems, including all 51 substitutions and all four fabricated articles; three correct links were mistakenly flagged as problematic.
Separately, the team matched nine statements from the reviews with the abstracts of the cited articles. Reference checking consists of two steps: the identifier and title must lead to the same article, and that article must actually support the statement in the text.
🔗 Read original →
medRxiv
Citation reliability of frontier large language models in medical writing and its automated verification
Large language models (LLMs) are increasingly used to draft medical manuscripts, yet their citations are unreliable and clinicians lack a validated way to verify them. We evaluated three frontier LLMs, Claude Opus 4.8, GPT-5.5, and Gemini 3.5 Flash, generating…
Navitoclax reduces scar size in aged mice after heart attack
In the preprint, Navitoclax reduced scar formation in 15‑month‑old mice following myocardial infarction. The authors tested the drug in middle‑aged male mice after ischemia‑reperfusion: the coronary artery was occluded for one hour, then reperfusion was restored.
Parallel to the mouse work, they re‑analyzed spatial
🔗 Read original →
In the preprint, Navitoclax reduced scar formation in 15‑month‑old mice following myocardial infarction. The authors tested the drug in middle‑aged male mice after ischemia‑reperfusion: the coronary artery was occluded for one hour, then reperfusion was restored.
Parallel to the mouse work, they re‑analyzed spatial
🔗 Read original →
Researchsquare
An error has occurred
Research Square is a preprint platform that makes research communication faster, fairer, and more useful.
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 have a preprint posted on Research Square on September 8. That's a citation/reference. Should be wrapped in single underscores: Research Square, September 8 maybe. Also maybe mention the date of posting. Also mention numbers: 138 compounds, seven candidates, AS191, 16 days for spheroid formation, 48 hours after TGF-β, COL1A1 gene activity, 23 mice, four groups, 28 days daily CCl4, etc. Need to select at most 4-5 important facts to bold. Choose: 138 compounds, seven candidates, AS191, 16 days, 48 hours, 23 mice, 28 days. That's many; need max 4-5. So pick maybe: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Or maybe include 48 hours as important. Let's decide: we need at most 4-5. Choose 5: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Ensure not bold whole sentence.
Now citations: The preprint posted on Research Square on September 8. Wrap that in single underscores: Research Square, September 8. Also maybe mention the journal? It's a preprint, not a journal. So just that.
Now produce headline: maybe "Three‑cell liver organoid screens compounds, identifies AS191 as antifibrotic candidate". Count characters: Let's count: "Three‑cell liver organoid screens compounds, identifies AS191 as antifibrotic candidate". Count: T(1)h2r3e4e5‑6c7e8l9l10 11l12i13v14e15 16o17r18g19a20n21i22d23 24s25c26r27e28e29n30s31 32c33o34m35p36o37u38n39d40s41,42 43i44d45e46n47t48i49f50i51e52s53 54A55S561 57 58a59s60 61a62n63t64i65f66i67b68r69o70t71i72c73 74c75a76n77d78i79d80a81t82e83. That's 83 characters approx, under 90. Good.
Now body paragraphs: need short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: describe the organoid model: three cell types, spheroid formation over 16 days, TGF-β addition, COL1A1 upregulation after 48 hours as readout.
Paragraph 2: screening 138 compounds, seven reduced COL1A1 signal, AS191 selected as aspirin derivative, mechanism via reduced phosphorylation of PI3K/AKT/GSK3β.
Paragraph 3: in vivo test: 23 mice, four groups, CCl4 liver injury for 28 days, AS191 given from day 1 in one group, outcomes: less collagen, inflammatory cells, lower ALT/AST/ALP, reduced PI3K/AKT phosphorylation.
Paragraph 4: note that AS191 tested during ongoing injury, preprint posted on Research Square September 8.
Make sure to bold selected facts: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Also maybe bold 28 days? But we limited to 5. We'll stick with those.
Now ensure we don't bold whole sentences. We'll embed bold within sentences.
Now citations: wrap Research Square, September 8 in single underscores.
Now produce final answer.
Check for any extra formatting: first line headline, then blank line, then paragraphs separated by blank lines. No markdown besides asterisks and underscores. Ensure no leading/trailing spaces.
Let's craft.
Paragraph 1: "Researchers built a three‑cell liver organoid by co‑culturing hepatocytes, endothelial cells and hepatic stellate cells. The cells self‑assembled into a spheroid over 16 days. Adding TGF‑β activated stellate cells, and after 48 hours the COL1A1 gene — a marker of collagen I — showed increased fluorescence, providing a readout for fibrotic activity."…
🔗 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 have a preprint posted on Research Square on September 8. That's a citation/reference. Should be wrapped in single underscores: Research Square, September 8 maybe. Also maybe mention the date of posting. Also mention numbers: 138 compounds, seven candidates, AS191, 16 days for spheroid formation, 48 hours after TGF-β, COL1A1 gene activity, 23 mice, four groups, 28 days daily CCl4, etc. Need to select at most 4-5 important facts to bold. Choose: 138 compounds, seven candidates, AS191, 16 days, 48 hours, 23 mice, 28 days. That's many; need max 4-5. So pick maybe: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Or maybe include 48 hours as important. Let's decide: we need at most 4-5. Choose 5: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Ensure not bold whole sentence.
Now citations: The preprint posted on Research Square on September 8. Wrap that in single underscores: Research Square, September 8. Also maybe mention the journal? It's a preprint, not a journal. So just that.
Now produce headline: maybe "Three‑cell liver organoid screens compounds, identifies AS191 as antifibrotic candidate". Count characters: Let's count: "Three‑cell liver organoid screens compounds, identifies AS191 as antifibrotic candidate". Count: T(1)h2r3e4e5‑6c7e8l9l10 11l12i13v14e15 16o17r18g19a20n21i22d23 24s25c26r27e28e29n30s31 32c33o34m35p36o37u38n39d40s41,42 43i44d45e46n47t48i49f50i51e52s53 54A55S561 57 58a59s60 61a62n63t64i65f66i67b68r69o70t71i72c73 74c75a76n77d78i79d80a81t82e83. That's 83 characters approx, under 90. Good.
Now body paragraphs: need short paragraphs 2-3 sentences each.
We'll produce maybe 4 paragraphs.
Paragraph 1: describe the organoid model: three cell types, spheroid formation over 16 days, TGF-β addition, COL1A1 upregulation after 48 hours as readout.
Paragraph 2: screening 138 compounds, seven reduced COL1A1 signal, AS191 selected as aspirin derivative, mechanism via reduced phosphorylation of PI3K/AKT/GSK3β.
Paragraph 3: in vivo test: 23 mice, four groups, CCl4 liver injury for 28 days, AS191 given from day 1 in one group, outcomes: less collagen, inflammatory cells, lower ALT/AST/ALP, reduced PI3K/AKT phosphorylation.
Paragraph 4: note that AS191 tested during ongoing injury, preprint posted on Research Square September 8.
Make sure to bold selected facts: 138 compounds, seven candidates, AS191, 16 days, 23 mice. Also maybe bold 28 days? But we limited to 5. We'll stick with those.
Now ensure we don't bold whole sentences. We'll embed bold within sentences.
Now citations: wrap Research Square, September 8 in single underscores.
Now produce final answer.
Check for any extra formatting: first line headline, then blank line, then paragraphs separated by blank lines. No markdown besides asterisks and underscores. Ensure no leading/trailing spaces.
Let's craft.
Paragraph 1: "Researchers built a three‑cell liver organoid by co‑culturing hepatocytes, endothelial cells and hepatic stellate cells. The cells self‑assembled into a spheroid over 16 days. Adding TGF‑β activated stellate cells, and after 48 hours the COL1A1 gene — a marker of collagen I — showed increased fluorescence, providing a readout for fibrotic activity."…
🔗 Read original →
europepmc.org
Europe PMC
Europe PMC is an archive of life sciences journal literature.