Life Sciences

193 items

bioRxiv Sep 16, 2026
Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, this study compared how well traditional transcriptomic cell type classification, representations from the foundation model scGPT pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes predict electrophysiological features, finding that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings, with the best results obtained by combining the outputs of separate cell type and scGPT-based models.
Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, this study compared how well traditional transcriptomic cell type classification, representations from the foundation model scGPT pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes predict electrophysiological features, finding that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings, with the best results obtained by combining the outputs of separate cell type and scGPT-based models.
The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features
Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, this study compared how well traditional transcriptomic cell type classification, representations from the foundation model scGPT pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes predict electrophysiological features, finding that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings, with the best results obtained by combining the outputs of separate cell type and scGPT-based models.
Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, this study compared how well traditional transcriptomic cell type classification, representations from the foundation model scGPT pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes predict electrophysiological features, finding that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings, with the best results obtained by combining the outputs of separate cell type and scGPT-based models.
Pharmacology & therapeutics Sep 16, 2026
This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.
This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.
Drug Resistance in Cancer: A Conceptual Shift from Single-Gene Mechanisms to Systemic Adaptive Resistance
This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.
This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.
medRxiv Sep 16, 2026
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Genomic foundation model-derived disruption profiling links somatic mutations to cancer biology and clinical outcomes
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Biologia futura Sep 16, 2026
This review states that microbial diversity underpins human health, agricultural productivity, ecological balance, and ecosystem functioning, and it surveys the role of the gut microbial community in immune regulation, metabolism, and disease prevention, the contributions of interactions among plants, fungi, bacteria, and other soil microorganisms to carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in terrestrial ecosystems, and emerging microbiome-based therapies such as precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine, while noting that multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence enable emerging uses in agriculture, envi
This review states that microbial diversity underpins human health, agricultural productivity, ecological balance, and ecosystem functioning, and it surveys the role of the gut microbial community in immune regulation, metabolism, and disease prevention, the contributions of interactions among plants, fungi, bacteria, and other soil microorganisms to carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in terrestrial ecosystems, and emerging microbiome-based therapies such as precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine, while noting that multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence enable emerging uses in agriculture, envi
Microbial diversity: the essential foundation for life on our planet
This review states that microbial diversity underpins human health, agricultural productivity, ecological balance, and ecosystem functioning, and it surveys the role of the gut microbial community in immune regulation, metabolism, and disease prevention, the contributions of interactions among plants, fungi, bacteria, and other soil microorganisms to carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in terrestrial ecosystems, and emerging microbiome-based therapies such as precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine, while noting that multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence enable emerging uses in agriculture, envi
This review states that microbial diversity underpins human health, agricultural productivity, ecological balance, and ecosystem functioning, and it surveys the role of the gut microbial community in immune regulation, metabolism, and disease prevention, the contributions of interactions among plants, fungi, bacteria, and other soil microorganisms to carbon sequestration, nutrient cycling, stress resilience, and sustainable agricultural productivity in terrestrial ecosystems, and emerging microbiome-based therapies such as precision probiotics, postbiotics, faecal microbiota transplantation, and personalized microbiome medicine, while noting that multi-omic techniques, synthetic microbial genomes, microbiome engineering, and artificial intelligence enable emerging uses in agriculture, envi
Trends in biotechnology Sep 16, 2026
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
Liquid biopsy in glioblastoma: emerging technologies and translational opportunities
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
Trends in pharmacological sciences Sep 16, 2026
The article notes that while artificial intelligence accelerates drug discovery it also creates regulatory gaps, with more than 100 AI-assisted pipelines in trials and frameworks lacking enforceable standards; it therefore proposes a risk-tiered framework that distinguishes discovery AI from evidence-generating AI, mandates impact assessments and Investigational New Drug disclosure when AI influences decisions, and transforms guidelines into binding, risk-proportionate regulation.
The article notes that while artificial intelligence accelerates drug discovery it also creates regulatory gaps, with more than 100 AI-assisted pipelines in trials and frameworks lacking enforceable standards; it therefore proposes a risk-tiered framework that distinguishes discovery AI from evidence-generating AI, mandates impact assessments and Investigational New Drug disclosure when AI influences decisions, and transforms guidelines into binding, risk-proportionate regulation.
Closing AI drug-regulatory gaps through harmonized oversight
The article notes that while artificial intelligence accelerates drug discovery it also creates regulatory gaps, with more than 100 AI-assisted pipelines in trials and frameworks lacking enforceable standards; it therefore proposes a risk-tiered framework that distinguishes discovery AI from evidence-generating AI, mandates impact assessments and Investigational New Drug disclosure when AI influences decisions, and transforms guidelines into binding, risk-proportionate regulation.
The article notes that while artificial intelligence accelerates drug discovery it also creates regulatory gaps, with more than 100 AI-assisted pipelines in trials and frameworks lacking enforceable standards; it therefore proposes a risk-tiered framework that distinguishes discovery AI from evidence-generating AI, mandates impact assessments and Investigational New Drug disclosure when AI influences decisions, and transforms guidelines into binding, risk-proportionate regulation.
RNA Sep 16, 2026
This work develops a continuum-based reaction-diffusion model of XIST-mediated gene silencing spread on chromosomes, finding that XIST spread can be tuned by known negative feedback loops regulating its synthesis and degradation, while silencing spread is controlled by a wave-pinning mechanism driven by global regulation of the silencing complex together with local epigenetic regulators, and uses a 3D chromosome structure inferred from experimental data to show spatiotemporal regulation of silencing spread.
This work develops a continuum-based reaction-diffusion model of XIST-mediated gene silencing spread on chromosomes, finding that XIST spread can be tuned by known negative feedback loops regulating its synthesis and degradation, while silencing spread is controlled by a wave-pinning mechanism driven by global regulation of the silencing complex together with local epigenetic regulators, and uses a 3D chromosome structure inferred from experimental data to show spatiotemporal regulation of silencing spread.
A Continuum-Based Reaction-Diffusion Model Reveals Spatial Spread of Gene Silencing in Chromosomal Inactivation
This work develops a continuum-based reaction-diffusion model of XIST-mediated gene silencing spread on chromosomes, finding that XIST spread can be tuned by known negative feedback loops regulating its synthesis and degradation, while silencing spread is controlled by a wave-pinning mechanism driven by global regulation of the silencing complex together with local epigenetic regulators, and uses a 3D chromosome structure inferred from experimental data to show spatiotemporal regulation of silencing spread.
This work develops a continuum-based reaction-diffusion model of XIST-mediated gene silencing spread on chromosomes, finding that XIST spread can be tuned by known negative feedback loops regulating its synthesis and degradation, while silencing spread is controlled by a wave-pinning mechanism driven by global regulation of the silencing complex together with local epigenetic regulators, and uses a 3D chromosome structure inferred from experimental data to show spatiotemporal regulation of silencing spread.
Bioscience Reports Sep 16, 2026
This review systematically examines how DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA networks drive chemoresistance and recurrence in ovarian cancer, summarizes clinical trial results of epigenetic agents (DNMT, HDAC, and EZH2 inhibitors) combined with PARP inhibitors or immunotherapy, and reviews biomarker advances based on circulating cfDNA methylation, circulating miRNAs, and AI-based liquid biopsy platforms, proposing a precision oncology framework for patient stratification and real-time monitoring of chemoresistance.
This review systematically examines how DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA networks drive chemoresistance and recurrence in ovarian cancer, summarizes clinical trial results of epigenetic agents (DNMT, HDAC, and EZH2 inhibitors) combined with PARP inhibitors or immunotherapy, and reviews biomarker advances based on circulating cfDNA methylation, circulating miRNAs, and AI-based liquid biopsy platforms, proposing a precision oncology framework for patient stratification and real-time monitoring of chemoresistance.
Epigenetic Mechanisms and Clinical Translation in Ovarian Cancer: From Molecular Pathways to Precision Therapy
This review systematically examines how DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA networks drive chemoresistance and recurrence in ovarian cancer, summarizes clinical trial results of epigenetic agents (DNMT, HDAC, and EZH2 inhibitors) combined with PARP inhibitors or immunotherapy, and reviews biomarker advances based on circulating cfDNA methylation, circulating miRNAs, and AI-based liquid biopsy platforms, proposing a precision oncology framework for patient stratification and real-time monitoring of chemoresistance.
This review systematically examines how DNA methylation, histone modifications, chromatin remodeling, and non-coding RNA networks drive chemoresistance and recurrence in ovarian cancer, summarizes clinical trial results of epigenetic agents (DNMT, HDAC, and EZH2 inhibitors) combined with PARP inhibitors or immunotherapy, and reviews biomarker advances based on circulating cfDNA methylation, circulating miRNAs, and AI-based liquid biopsy platforms, proposing a precision oncology framework for patient stratification and real-time monitoring of chemoresistance.
arXiv Sep 16, 2026
This work proposes a new self-supervised pretraining task for 3D graph neural networks: pretrained on 542k SwissProt protein structures from the AlphaFold Database, the model predicts the Euclidean distance between the geometric centroid of each protein subgraph (2-hop ego networks centered on 10% of amino acids) and the global geometric centroid of the whole protein, discretized into 10 equal bins and trained with cross-entropy; across ProNet, SchNet, and GCN backbones and ca_base, ca_angles, and ca_bb featurizations, it improves Fold, Superfamily, Family, and React classification by up to about 6% over no-pretraining and edge-distance pretraining baselines, without multiple views, augmentations, or masking strategies.
This work proposes a new self-supervised pretraining task for 3D graph neural networks: pretrained on 542k SwissProt protein structures from the AlphaFold Database, the model predicts the Euclidean distance between the geometric centroid of each protein subgraph (2-hop ego networks centered on 10% of amino acids) and the global geometric centroid of the whole protein, discretized into 10 equal bins and trained with cross-entropy; across ProNet, SchNet, and GCN backbones and ca_base, ca_angles, and ca_bb featurizations, it improves Fold, Superfamily, Family, and React classification by up to about 6% over no-pretraining and edge-distance pretraining baselines, without multiple views, augmentations, or masking strategies.
Geometric Self-supervised Pretraining on 3D Protein Structures Using Subgraphs
This work proposes a new self-supervised pretraining task for 3D graph neural networks: pretrained on 542k SwissProt protein structures from the AlphaFold Database, the model predicts the Euclidean distance between the geometric centroid of each protein subgraph (2-hop ego networks centered on 10% of amino acids) and the global geometric centroid of the whole protein, discretized into 10 equal bins and trained with cross-entropy; across ProNet, SchNet, and GCN backbones and ca_base, ca_angles, and ca_bb featurizations, it improves Fold, Superfamily, Family, and React classification by up to about 6% over no-pretraining and edge-distance pretraining baselines, without multiple views, augmentations, or masking strategies.
This work proposes a new self-supervised pretraining task for 3D graph neural networks: pretrained on 542k SwissProt protein structures from the AlphaFold Database, the model predicts the Euclidean distance between the geometric centroid of each protein subgraph (2-hop ego networks centered on 10% of amino acids) and the global geometric centroid of the whole protein, discretized into 10 equal bins and trained with cross-entropy; across ProNet, SchNet, and GCN backbones and ca_base, ca_angles, and ca_bb featurizations, it improves Fold, Superfamily, Family, and React classification by up to about 6% over no-pretraining and edge-distance pretraining baselines, without multiple views, augmentations, or masking strategies.
Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 16, 2026
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
AI-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.
This study applied AI-guided phenotypic drug repurposing to drug-resistant Streptococcus pneumoniae, using ensembles of transformer, graph, and tree models trained on 1849 actives and 34 503 inactives to prospectively examine 6747 drugs, selecting 11 candidate antibiotics of which nine strongly reduced in vitro growth of S. pneumoniae R6 (IC50 ≤ 0.4 µg/mL), with the most potent drugs thiostrepton and ceftiofur showing IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively, and thiostrepton remaining highly potent against multidrug-resistant strains.

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