Life Sciences

193 items

Experimental hematology & oncology Sep 15, 2026
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
Multimodal AI and single-cell transcriptomics integrate to construct a histopathological prognostic model for bladder cancer, revealing the RTN3-glycolysis axis in chemoresistance
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
bioRxiv Sep 15, 2026
Using repeated intratracheal inoculation of mice with recombinant Staphylococcus aureus serine protease-like protein B (SplB) or an inactive mutant, this study found that SplB sensitized mice and caused eosinophilic airway inflammation and hyperresponsiveness, that asthma development required both the proteolytic activity of SplB and a functional adaptive immune system, and that the soluble protease sensor IL-33 was necessary for eosinophil tissue invasion whereas the membrane-bound protease sensor PAR2 was not, leading the authors to propose a third mechanism in which S. aureus releases allergens such as SplB that sensitize individuals and lead to asthma.
Using repeated intratracheal inoculation of mice with recombinant Staphylococcus aureus serine protease-like protein B (SplB) or an inactive mutant, this study found that SplB sensitized mice and caused eosinophilic airway inflammation and hyperresponsiveness, that asthma development required both the proteolytic activity of SplB and a functional adaptive immune system, and that the soluble protease sensor IL-33 was necessary for eosinophil tissue invasion whereas the membrane-bound protease sensor PAR2 was not, leading the authors to propose a third mechanism in which S. aureus releases allergens such as SplB that sensitize individuals and lead to asthma.
The Staphylococcus aureus serine protease-like protein B is a potent allergen in a murine asthma model
Using repeated intratracheal inoculation of mice with recombinant Staphylococcus aureus serine protease-like protein B (SplB) or an inactive mutant, this study found that SplB sensitized mice and caused eosinophilic airway inflammation and hyperresponsiveness, that asthma development required both the proteolytic activity of SplB and a functional adaptive immune system, and that the soluble protease sensor IL-33 was necessary for eosinophil tissue invasion whereas the membrane-bound protease sensor PAR2 was not, leading the authors to propose a third mechanism in which S. aureus releases allergens such as SplB that sensitize individuals and lead to asthma.
Using repeated intratracheal inoculation of mice with recombinant Staphylococcus aureus serine protease-like protein B (SplB) or an inactive mutant, this study found that SplB sensitized mice and caused eosinophilic airway inflammation and hyperresponsiveness, that asthma development required both the proteolytic activity of SplB and a functional adaptive immune system, and that the soluble protease sensor IL-33 was necessary for eosinophil tissue invasion whereas the membrane-bound protease sensor PAR2 was not, leading the authors to propose a third mechanism in which S. aureus releases allergens such as SplB that sensitize individuals and lead to asthma.
bioRxiv Sep 15, 2026
Using cerastecin Cpd 4 as a template, this study applied two AI tools, Link-INVENT and AutoMolDesigner, for molecular design and chemical derivatization, leading to the discovery of the MsbA-targeted small molecule Y-11 with an MIC of 0.5 g/mL against A. baumannii, equivalent potency to Cpd4 against carbapenem-resistant A. baumannii, lower cytotoxicity, hemolysis, and spontaneous resistance frequency, effective reduction of bacterial loads in infected mice, and a proposed mechanism in which Y-11 inhibits lipooligosaccharide transport and impairs outer membrane formation, probably by competitively binding the substrate binding site of MsbA and modulating ATPase activity.
Using cerastecin Cpd 4 as a template, this study applied two AI tools, Link-INVENT and AutoMolDesigner, for molecular design and chemical derivatization, leading to the discovery of the MsbA-targeted small molecule Y-11 with an MIC of 0.5 g/mL against A. baumannii, equivalent potency to Cpd4 against carbapenem-resistant A. baumannii, lower cytotoxicity, hemolysis, and spontaneous resistance frequency, effective reduction of bacterial loads in infected mice, and a proposed mechanism in which Y-11 inhibits lipooligosaccharide transport and impairs outer membrane formation, probably by competitively binding the substrate binding site of MsbA and modulating ATPase activity.
Deep reinforcement learning-driven discovery of a MsbA-targeted small-molecule antibiotic for the treatment of Acinetobacter baumannii infection
Using cerastecin Cpd 4 as a template, this study applied two AI tools, Link-INVENT and AutoMolDesigner, for molecular design and chemical derivatization, leading to the discovery of the MsbA-targeted small molecule Y-11 with an MIC of 0.5 g/mL against A. baumannii, equivalent potency to Cpd4 against carbapenem-resistant A. baumannii, lower cytotoxicity, hemolysis, and spontaneous resistance frequency, effective reduction of bacterial loads in infected mice, and a proposed mechanism in which Y-11 inhibits lipooligosaccharide transport and impairs outer membrane formation, probably by competitively binding the substrate binding site of MsbA and modulating ATPase activity.
Using cerastecin Cpd 4 as a template, this study applied two AI tools, Link-INVENT and AutoMolDesigner, for molecular design and chemical derivatization, leading to the discovery of the MsbA-targeted small molecule Y-11 with an MIC of 0.5 g/mL against A. baumannii, equivalent potency to Cpd4 against carbapenem-resistant A. baumannii, lower cytotoxicity, hemolysis, and spontaneous resistance frequency, effective reduction of bacterial loads in infected mice, and a proposed mechanism in which Y-11 inhibits lipooligosaccharide transport and impairs outer membrane formation, probably by competitively binding the substrate binding site of MsbA and modulating ATPase activity.
Research Square Sep 15, 2026
In this retrospective multicenter study, an in-house deep learning segmentation model delineated the zona pellucida, inner cell mass, and trophectoderm and extracted 17 quantitative morphological indicators from 14,072 blastocyst images across seven Korean centers (after exclusions, 10,718 embryos for consensus grade prediction and 1,387 for fetal heart tone prediction), finding that morphology-based predicted grades agreed with consensus grades more closely than individual embryologists for developmental stage and inner cell mass, and that a quantitative morphology-based Random Forest model outperformed a manual consensus grade-based model for fetal heart tone prediction (AUROC 0.648 versus 0.610; DeLong's test p = 0.
In this retrospective multicenter study, an in-house deep learning segmentation model delineated the zona pellucida, inner cell mass, and trophectoderm and extracted 17 quantitative morphological indicators from 14,072 blastocyst images across seven Korean centers (after exclusions, 10,718 embryos for consensus grade prediction and 1,387 for fetal heart tone prediction), finding that morphology-based predicted grades agreed with consensus grades more closely than individual embryologists for developmental stage and inner cell mass, and that a quantitative morphology-based Random Forest model outperformed a manual consensus grade-based model for fetal heart tone prediction (AUROC 0.648 versus 0.610; DeLong's test p = 0.
Artificial intelligence-derived quantitative blastocyst morphology for objective embryo assessment and fetal heart tone stratification
In this retrospective multicenter study, an in-house deep learning segmentation model delineated the zona pellucida, inner cell mass, and trophectoderm and extracted 17 quantitative morphological indicators from 14,072 blastocyst images across seven Korean centers (after exclusions, 10,718 embryos for consensus grade prediction and 1,387 for fetal heart tone prediction), finding that morphology-based predicted grades agreed with consensus grades more closely than individual embryologists for developmental stage and inner cell mass, and that a quantitative morphology-based Random Forest model outperformed a manual consensus grade-based model for fetal heart tone prediction (AUROC 0.648 versus 0.610; DeLong's test p = 0.
In this retrospective multicenter study, an in-house deep learning segmentation model delineated the zona pellucida, inner cell mass, and trophectoderm and extracted 17 quantitative morphological indicators from 14,072 blastocyst images across seven Korean centers (after exclusions, 10,718 embryos for consensus grade prediction and 1,387 for fetal heart tone prediction), finding that morphology-based predicted grades agreed with consensus grades more closely than individual embryologists for developmental stage and inner cell mass, and that a quantitative morphology-based Random Forest model outperformed a manual consensus grade-based model for fetal heart tone prediction (AUROC 0.648 versus 0.610; DeLong's test p = 0.
bioRxiv Sep 15, 2026
The work first characterized fully human heavy-chain-only antibodies (HCAbs) independently of any HCAb-trained model, finding a reproducible distributional shift relative to conventional human VH domains localized predominantly to CDR1/2, CDR3 architecture and, where supported, a restricted framework region rather than widespread framework remodeling; on this basis the authors developed HCAbLM, described as the first foundation model pretrained specifically on a large-scale fully human HCAb repertoire using 31.
The work first characterized fully human heavy-chain-only antibodies (HCAbs) independently of any HCAb-trained model, finding a reproducible distributional shift relative to conventional human VH domains localized predominantly to CDR1/2, CDR3 architecture and, where supported, a restricted framework region rather than widespread framework remodeling; on this basis the authors developed HCAbLM, described as the first foundation model pretrained specifically on a large-scale fully human HCAb repertoire using 31.
A dedicated foundation model for fully human heavy-chain-only antibodies: HCAbLM learns sequence and functional grammar
The work first characterized fully human heavy-chain-only antibodies (HCAbs) independently of any HCAb-trained model, finding a reproducible distributional shift relative to conventional human VH domains localized predominantly to CDR1/2, CDR3 architecture and, where supported, a restricted framework region rather than widespread framework remodeling; on this basis the authors developed HCAbLM, described as the first foundation model pretrained specifically on a large-scale fully human HCAb repertoire using 31.
The work first characterized fully human heavy-chain-only antibodies (HCAbs) independently of any HCAb-trained model, finding a reproducible distributional shift relative to conventional human VH domains localized predominantly to CDR1/2, CDR3 architecture and, where supported, a restricted framework region rather than widespread framework remodeling; on this basis the authors developed HCAbLM, described as the first foundation model pretrained specifically on a large-scale fully human HCAb repertoire using 31.
Nature News Sep 15, 2026
A study in mice shows that after infection with gut-illness-causing bacteria (Citrobacter rodentium) or parasites (Schistosoma mansoni), CD4+ T cells from the gut migrate through the bloodstream to the meninges surrounding the central nervous system and take up residence there, still responding to a second round of infection more than a month later, suggesting they keep a record of past illness.
A study in mice shows that after infection with gut-illness-causing bacteria (Citrobacter rodentium) or parasites (Schistosoma mansoni), CD4+ T cells from the gut migrate through the bloodstream to the meninges surrounding the central nervous system and take up residence there, still responding to a second round of infection more than a month later, suggesting they keep a record of past illness.
Gut-infection-trained T cells migrate to the meninges and leave an immune memory
A study in mice shows that after infection with gut-illness-causing bacteria (Citrobacter rodentium) or parasites (Schistosoma mansoni), CD4+ T cells from the gut migrate through the bloodstream to the meninges surrounding the central nervous system and take up residence there, still responding to a second round of infection more than a month later, suggesting they keep a record of past illness.
A study in mice shows that after infection with gut-illness-causing bacteria (Citrobacter rodentium) or parasites (Schistosoma mansoni), CD4+ T cells from the gut migrate through the bloodstream to the meninges surrounding the central nervous system and take up residence there, still responding to a second round of infection more than a month later, suggesting they keep a record of past illness.
medRxiv Sep 14, 2026
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
AI-Based Synthetic Data in Biomedicine: A Decade of Growth and a Persistent Translation Gap
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.
Human gene therapy Sep 14, 2026
This Perspective article presents the InsightRP2 framework, an integrated translational strategy combining clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design, with the aim of facilitating development of a targeted gene therapy for RP2-associated retinitis pigmentosa.
This Perspective article presents the InsightRP2 framework, an integrated translational strategy combining clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design, with the aim of facilitating development of a targeted gene therapy for RP2-associated retinitis pigmentosa.
InsightRP2: An Interdisciplinary Framework for Therapy Development in RP2-Associated Retinopathy
This Perspective article presents the InsightRP2 framework, an integrated translational strategy combining clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design, with the aim of facilitating development of a targeted gene therapy for RP2-associated retinitis pigmentosa.
This Perspective article presents the InsightRP2 framework, an integrated translational strategy combining clinical data, artificial intelligence-supported imaging analysis, experimental disease modeling, and adeno-associated virus design, with the aim of facilitating development of a targeted gene therapy for RP2-associated retinitis pigmentosa.
发表出处待核验 Sep 14, 2026
This work builds a frozen, content-hashed data asset and a uniform scoring harness that maps seven MaveDB saturation genome editing (SGE) score sets to GRCh38, harmonises orientation and freezes them into immutable matrices, then evaluates nineteen variant effect predictors across sixteen strata using per-gene Spearman rho pooled by DerSimonian-Laird random-effects meta-analysis with per-stratum measurement-reliability estimates, attenuation correction, paired dependent-correlation tests and leave-one-gene-out validation, covering 64,178 variants and seven cancer susceptibility genes.
This work builds a frozen, content-hashed data asset and a uniform scoring harness that maps seven MaveDB saturation genome editing (SGE) score sets to GRCh38, harmonises orientation and freezes them into immutable matrices, then evaluates nineteen variant effect predictors across sixteen strata using per-gene Spearman rho pooled by DerSimonian-Laird random-effects meta-analysis with per-stratum measurement-reliability estimates, attenuation correction, paired dependent-correlation tests and leave-one-gene-out validation, covering 64,178 variants and seven cancer susceptibility genes.
functional-standard-atlas: an attenuation-corrected, territory-resolved benchmark of variant effect predictors against saturation genome editing
This work builds a frozen, content-hashed data asset and a uniform scoring harness that maps seven MaveDB saturation genome editing (SGE) score sets to GRCh38, harmonises orientation and freezes them into immutable matrices, then evaluates nineteen variant effect predictors across sixteen strata using per-gene Spearman rho pooled by DerSimonian-Laird random-effects meta-analysis with per-stratum measurement-reliability estimates, attenuation correction, paired dependent-correlation tests and leave-one-gene-out validation, covering 64,178 variants and seven cancer susceptibility genes.
This work builds a frozen, content-hashed data asset and a uniform scoring harness that maps seven MaveDB saturation genome editing (SGE) score sets to GRCh38, harmonises orientation and freezes them into immutable matrices, then evaluates nineteen variant effect predictors across sixteen strata using per-gene Spearman rho pooled by DerSimonian-Laird random-effects meta-analysis with per-stratum measurement-reliability estimates, attenuation correction, paired dependent-correlation tests and leave-one-gene-out validation, covering 64,178 variants and seven cancer susceptibility genes.
Cineca Institutional Research Information System (Tor Vergata University) Sep 14, 2026
This study devised a bioinformatics pipeline for a comparative analysis of mechanosensitive membrane proteins in lactic acid bacteria (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus) and a fungus (Pleurotus floridanus), generating a consensus sequence for P. floridanus via multiple sequence alignment of homologues and mapping it onto the genome with BLAST to delineate the gene region of interest, then producing protein structures with the AlphaFold3 artificial intelligence platform using the correct oligomeric state for each channel, thereby bridging the structural knowledge gap for mechanoreceptors in these non-model systems.
This study devised a bioinformatics pipeline for a comparative analysis of mechanosensitive membrane proteins in lactic acid bacteria (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus) and a fungus (Pleurotus floridanus), generating a consensus sequence for P. floridanus via multiple sequence alignment of homologues and mapping it onto the genome with BLAST to delineate the gene region of interest, then producing protein structures with the AlphaFold3 artificial intelligence platform using the correct oligomeric state for each channel, thereby bridging the structural knowledge gap for mechanoreceptors in these non-model systems.
Under Pressure: The Art of Sensing Acoustic Power in the Realm of Bacteria and Fungi
This study devised a bioinformatics pipeline for a comparative analysis of mechanosensitive membrane proteins in lactic acid bacteria (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus) and a fungus (Pleurotus floridanus), generating a consensus sequence for P. floridanus via multiple sequence alignment of homologues and mapping it onto the genome with BLAST to delineate the gene region of interest, then producing protein structures with the AlphaFold3 artificial intelligence platform using the correct oligomeric state for each channel, thereby bridging the structural knowledge gap for mechanoreceptors in these non-model systems.
This study devised a bioinformatics pipeline for a comparative analysis of mechanosensitive membrane proteins in lactic acid bacteria (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus) and a fungus (Pleurotus floridanus), generating a consensus sequence for P. floridanus via multiple sequence alignment of homologues and mapping it onto the genome with BLAST to delineate the gene region of interest, then producing protein structures with the AlphaFold3 artificial intelligence platform using the correct oligomeric state for each channel, thereby bridging the structural knowledge gap for mechanoreceptors in these non-model systems.

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