Life Sciences

185 items

arXiv Sep 26, 2026
The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.
The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.
PRAXIS-VirtualCell proposes a modular framework that organizes biological data, models, perturbations, and validation evidence, using biological contracts and evidence-aware execution to separate supported predictions from extrapolation and abstention across E. coli, S. cerevisiae, and human K562 cells.
The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.
The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.
arXiv Sep 26, 2026
This survey by Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin and Yu Zhang synthesises brain-to-language decoding, which translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs, across invasive and non-invasive measurements; it connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, examines model development, public resources and the evolution of evaluation, compares published performance and communication costs within their reported protocols, identifies phonetic, acoustic and semantic targets as preserving different aspects of a message, shared representations
This survey by Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin and Yu Zhang synthesises brain-to-language decoding, which translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs, across invasive and non-invasive measurements; it connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, examines model development, public resources and the evolution of evaluation, compares published performance and communication costs within their reported protocols, identifies phonetic, acoustic and semantic targets as preserving different aspects of a message, shared representations
Yang and six co-authors survey brain-to-language decoding, tracing a field that moved from constrained recognition and acoustic reconstruction to text generation, streaming personalised speech and facial animation
This survey by Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin and Yu Zhang synthesises brain-to-language decoding, which translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs, across invasive and non-invasive measurements; it connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, examines model development, public resources and the evolution of evaluation, compares published performance and communication costs within their reported protocols, identifies phonetic, acoustic and semantic targets as preserving different aspects of a message, shared representations
This survey by Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin and Yu Zhang synthesises brain-to-language decoding, which translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs, across invasive and non-invasive measurements; it connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, examines model development, public resources and the evolution of evaluation, compares published performance and communication costs within their reported protocols, identifies phonetic, acoustic and semantic targets as preserving different aspects of a message, shared representations
arXiv Sep 26, 2026
The study builds a reaction-diffusion predator-prey model with additional food and predator intraspecific competition, locates the Hopf bifurcation of the coexistence state exactly in the well-mixed setting and shows the resulting cycle is stable, derives the diffusion-driven Turing threshold in the spatial setting, and finds that with prey mobility and competition strength as control parameters the pattern-forming and oscillatory instabilities meet at a single point, with simulations confirming that weak competition gives a whole-field oscillation, stronger competition with faster prey spread gives fixed patterns, and near the crossover the two combine into patterns that pulse in time.
The study builds a reaction-diffusion predator-prey model with additional food and predator intraspecific competition, locates the Hopf bifurcation of the coexistence state exactly in the well-mixed setting and shows the resulting cycle is stable, derives the diffusion-driven Turing threshold in the spatial setting, and finds that with prey mobility and competition strength as control parameters the pattern-forming and oscillatory instabilities meet at a single point, with simulations confirming that weak competition gives a whole-field oscillation, stronger competition with faster prey spread gives fixed patterns, and near the crossover the two combine into patterns that pulse in time.
Predator self-competition and prey mobility jointly set spatial structure in an additional-food predator-prey system: weak competition gives whole-field oscillations, stronger competition gives fixed patches, and near the crossover the two combine into pulsing patterns
The study builds a reaction-diffusion predator-prey model with additional food and predator intraspecific competition, locates the Hopf bifurcation of the coexistence state exactly in the well-mixed setting and shows the resulting cycle is stable, derives the diffusion-driven Turing threshold in the spatial setting, and finds that with prey mobility and competition strength as control parameters the pattern-forming and oscillatory instabilities meet at a single point, with simulations confirming that weak competition gives a whole-field oscillation, stronger competition with faster prey spread gives fixed patterns, and near the crossover the two combine into patterns that pulse in time.
The study builds a reaction-diffusion predator-prey model with additional food and predator intraspecific competition, locates the Hopf bifurcation of the coexistence state exactly in the well-mixed setting and shows the resulting cycle is stable, derives the diffusion-driven Turing threshold in the spatial setting, and finds that with prey mobility and competition strength as control parameters the pattern-forming and oscillatory instabilities meet at a single point, with simulations confirming that weak competition gives a whole-field oscillation, stronger competition with faster prey spread gives fixed patterns, and near the crossover the two combine into patterns that pulse in time.
arXiv Sep 26, 2026
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet and, using Euclidean EEGNet as a baseline, compares it against standard federated learning and centralised training on three motor-imagery datasets spanning diverse channel, subject and class regimes; it observes that personalised SPDNet reaches higher accuracy than both standard federated and centralised training while converging in fewer rounds and communicating fewer parameters than standard federated learning, and that it outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet and, using Euclidean EEGNet as a baseline, compares it against standard federated learning and centralised training on three motor-imagery datasets spanning diverse channel, subject and class regimes; it observes that personalised SPDNet reaches higher accuracy than both standard federated and centralised training while converging in fewer rounds and communicating fewer parameters than standard federated learning, and that it outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
Personalised federated learning lets SPDNet EEG decoding beat standard federated and centralised training on three motor-imagery datasets and outperform every EEGNet configuration on two of them
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet and, using Euclidean EEGNet as a baseline, compares it against standard federated learning and centralised training on three motor-imagery datasets spanning diverse channel, subject and class regimes; it observes that personalised SPDNet reaches higher accuracy than both standard federated and centralised training while converging in fewer rounds and communicating fewer parameters than standard federated learning, and that it outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet and, using Euclidean EEGNet as a baseline, compares it against standard federated learning and centralised training on three motor-imagery datasets spanning diverse channel, subject and class regimes; it observes that personalised SPDNet reaches higher accuracy than both standard federated and centralised training while converging in fewer rounds and communicating fewer parameters than standard federated learning, and that it outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
arXiv Sep 26, 2026
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet, and compares it against standard federated learning and centralised training with the Euclidean EEGNet as a baseline; across three motor-imagery datasets spanning diverse channel, subject and class regimes, personalised SPDNet reaches higher accuracy than both standard federated and centralised training, converges in fewer rounds and communicates fewer parameters than standard federated learning, and outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet, and compares it against standard federated learning and centralised training with the Euclidean EEGNet as a baseline; across three motor-imagery datasets spanning diverse channel, subject and class regimes, personalised SPDNet reaches higher accuracy than both standard federated and centralised training, converges in fewer rounds and communicates fewer parameters than standard federated learning, and outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
Personalised federated learning lets SPDNet EEG decoding beat standard federated and centralised training on three motor-imagery datasets and outperform every EEGNet configuration on two of them
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet, and compares it against standard federated learning and centralised training with the Euclidean EEGNet as a baseline; across three motor-imagery datasets spanning diverse channel, subject and class regimes, personalised SPDNet reaches higher accuracy than both standard federated and centralised training, converges in fewer rounds and communicates fewer parameters than standard federated learning, and outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
The work adapts personalised federated learning, in which all subjects share a trunk while each subject keeps its own head, to the Riemannian SPDNet, and compares it against standard federated learning and centralised training with the Euclidean EEGNet as a baseline; across three motor-imagery datasets spanning diverse channel, subject and class regimes, personalised SPDNet reaches higher accuracy than both standard federated and centralised training, converges in fewer rounds and communicates fewer parameters than standard federated learning, and outperforms every EEGNet configuration on two of the three datasets, although centralised EEGNet outperforms centralised SPDNet.
Arabian Journal of Chemistry Sep 26, 2026
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
Review maps indole derivatives targeting mycolic acid enzymes (MmpL3, InhA, KasA/B), their SAR and synthetic routes, and notes most series still lack enzymatic and genetic target validation
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
发表出处待核验 Sep 26, 2026
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
Review traces forensic identification from RFLP and STR to mtDNA, Y-chromosome markers and next-generation sequencing, flagging data complexity, ethics and global standardization as key concerns
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
Lecture notes in computer science Sep 26, 2026
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
CALHippo builds a three-class, CA1–CA4-wide cell annotation library from 1 µm/px BigBrain sections and trains a UNet density model to infer cell density across the CA complex
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful
bioRxiv Sep 26, 2026
The work presents Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type and cell-state composition without reconstructing gene expression; the authors find that even models accurately predicting perturbation-induced changes in average gene expression perform poorly at forecasting compositional shifts, while in the primary T-cell benchmark Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines and better preserved the diversity of observed cell states; the authors further extend it to intact tissue, predicting perturbation-induced cell-type proportion changes in local cellular neighborhoods and using attention patterns to generate hypotheses about context-dependent cellular
The work presents Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type and cell-state composition without reconstructing gene expression; the authors find that even models accurately predicting perturbation-induced changes in average gene expression perform poorly at forecasting compositional shifts, while in the primary T-cell benchmark Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines and better preserved the diversity of observed cell states; the authors further extend it to intact tissue, predicting perturbation-induced cell-type proportion changes in local cellular neighborhoods and using attention patterns to generate hypotheses about context-dependent cellular
Pop-Corn directly predicts perturbation-driven compositional shifts, outperforming expression-mediated pipelines on held-out perturbations
The work presents Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type and cell-state composition without reconstructing gene expression; the authors find that even models accurately predicting perturbation-induced changes in average gene expression perform poorly at forecasting compositional shifts, while in the primary T-cell benchmark Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines and better preserved the diversity of observed cell states; the authors further extend it to intact tissue, predicting perturbation-induced cell-type proportion changes in local cellular neighborhoods and using attention patterns to generate hypotheses about context-dependent cellular
The work presents Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type and cell-state composition without reconstructing gene expression; the authors find that even models accurately predicting perturbation-induced changes in average gene expression perform poorly at forecasting compositional shifts, while in the primary T-cell benchmark Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines and better preserved the diversity of observed cell states; the authors further extend it to intact tissue, predicting perturbation-induced cell-type proportion changes in local cellular neighborhoods and using attention patterns to generate hypotheses about context-dependent cellular
bioRxiv Sep 26, 2026
Using 63 organoid samples from 22 colorectal cancer patients, external validation in TCGA-COAD/READ (n=624) and GSE39582 (n=536) totaling 1,160 cases, public cell line panels (GDSC2, DepMap), and 65 lines from an independent patient-derived CRC organoid biobank, the study tested whether usage of the dual MDM2 promoters (P1/P2) acts as a molecular switch separating a chromosomal-instability type from an environment-adaptive type (microsatellite instability/serrated pathway with gastric metaplasia), finding deep-learning morphological classification at 98.5% test accuracy (64/65), morphology corresponding to P1/P2 isoform usage (median Type1 fraction 0.826 versus 0.444 in P1-dominant samples; non-Type1 cystic mucinous morphology in P2-dominant samples, AUC 0.
Using 63 organoid samples from 22 colorectal cancer patients, external validation in TCGA-COAD/READ (n=624) and GSE39582 (n=536) totaling 1,160 cases, public cell line panels (GDSC2, DepMap), and 65 lines from an independent patient-derived CRC organoid biobank, the study tested whether usage of the dual MDM2 promoters (P1/P2) acts as a molecular switch separating a chromosomal-instability type from an environment-adaptive type (microsatellite instability/serrated pathway with gastric metaplasia), finding deep-learning morphological classification at 98.5% test accuracy (64/65), morphology corresponding to P1/P2 isoform usage (median Type1 fraction 0.826 versus 0.444 in P1-dominant samples; non-Type1 cystic mucinous morphology in P2-dominant samples, AUC 0.
MDM2 promoter P1/P2 switching and colorectal cancer lineage plasticity: deep-learning morphology classification at 98.5% accuracy, with higher P2 index in TP53 wild-type tumors and greater Nutlin-3a sensitivity
Using 63 organoid samples from 22 colorectal cancer patients, external validation in TCGA-COAD/READ (n=624) and GSE39582 (n=536) totaling 1,160 cases, public cell line panels (GDSC2, DepMap), and 65 lines from an independent patient-derived CRC organoid biobank, the study tested whether usage of the dual MDM2 promoters (P1/P2) acts as a molecular switch separating a chromosomal-instability type from an environment-adaptive type (microsatellite instability/serrated pathway with gastric metaplasia), finding deep-learning morphological classification at 98.5% test accuracy (64/65), morphology corresponding to P1/P2 isoform usage (median Type1 fraction 0.826 versus 0.444 in P1-dominant samples; non-Type1 cystic mucinous morphology in P2-dominant samples, AUC 0.
Using 63 organoid samples from 22 colorectal cancer patients, external validation in TCGA-COAD/READ (n=624) and GSE39582 (n=536) totaling 1,160 cases, public cell line panels (GDSC2, DepMap), and 65 lines from an independent patient-derived CRC organoid biobank, the study tested whether usage of the dual MDM2 promoters (P1/P2) acts as a molecular switch separating a chromosomal-instability type from an environment-adaptive type (microsatellite instability/serrated pathway with gastric metaplasia), finding deep-learning morphological classification at 98.5% test accuracy (64/65), morphology corresponding to P1/P2 isoform usage (median Type1 fraction 0.826 versus 0.444 in P1-dominant samples; non-Type1 cystic mucinous morphology in P2-dominant samples, AUC 0.

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