Medicine & Health

455 items

Cancer Discovery Oct 1, 2026
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
MutationProjector, pretrained on 30,000+ tumor genomes, predicts immunotherapy response in bladder, lung and melanoma cancers and flags KMT2D and SMARCA4-STK11 as candidate biomarkers
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
发表出处待核验 Oct 1, 2026
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
A cross-field review of seven psychiatric research areas finds big data yields mechanistic insight and transdisciplinary collaboration, but sample size alone does not guarantee more precise estimates and a translational gap remains
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
Investigative Radiology Oct 1, 2026
The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.
The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.
AI-RADS let 5 radiologists grade 350 AI outputs, reaching interreader agreement of α=0.87 for image tasks and α=0.93 for generative tasks
The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.
The study developed and multireader-evaluated AI-RADS, a structured framework for case-level assessment of radiology AI output reliability, clinical utility, and recommended actions, in which 5 board-certified radiologists independently evaluated 350 cases processed by 7 representative AI applications, assigning each case one of 5 AI-RADS categories, applicable modifiers, and an independent correctness rating as a reference; substantial interreader agreement was observed for core categories in image-based tasks (Krippendorff's α=0.87; 95% CI: 0.83-0.91) and generative AI tasks (α=0.93; 95% CI: 0.91-0.
Journal of Clinical Gastroenterology Oct 1, 2026
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
AI-assisted digital cholangioscopy for indeterminate and malignant biliary strictures: 5 studies, 675 lesions, pooled sensitivity 95% and specificity 88%
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
arXiv Oct 1, 2026
The work shows that most of the reported gain from jointly decoding all words in a sentence (d'Ascoli et al., 2025) is reproducible on synthetic signals containing no brain information (22.0% versus 22.3% on real MEG), because neighbouring three-second word-aligned windows overlap and implicitly leak word duration; decoding words independently instead (SimpleB2T) makes aggregation across observations and an LLM prior effective, reaching 36.6% word error rate with five observations per word on a clinically motivated benchmark.
The work shows that most of the reported gain from jointly decoding all words in a sentence (d'Ascoli et al., 2025) is reproducible on synthetic signals containing no brain information (22.0% versus 22.3% on real MEG), because neighbouring three-second word-aligned windows overlap and implicitly leak word duration; decoding words independently instead (SimpleB2T) makes aggregation across observations and an LLM prior effective, reaching 36.6% word error rate with five observations per word on a clinically motivated benchmark.
Removing the overlapping-window timing shortcut lets non-invasive brain-to-text reach 36.6% word error rate with five observations per word
The work shows that most of the reported gain from jointly decoding all words in a sentence (d'Ascoli et al., 2025) is reproducible on synthetic signals containing no brain information (22.0% versus 22.3% on real MEG), because neighbouring three-second word-aligned windows overlap and implicitly leak word duration; decoding words independently instead (SimpleB2T) makes aggregation across observations and an LLM prior effective, reaching 36.6% word error rate with five observations per word on a clinically motivated benchmark.
The work shows that most of the reported gain from jointly decoding all words in a sentence (d'Ascoli et al., 2025) is reproducible on synthetic signals containing no brain information (22.0% versus 22.3% on real MEG), because neighbouring three-second word-aligned windows overlap and implicitly leak word duration; decoding words independently instead (SimpleB2T) makes aggregation across observations and an LLM prior effective, reaching 36.6% word error rate with five observations per word on a clinically motivated benchmark.
Nature News Oct 1, 2026
A Nature news story describes a mouse study, published in Advanced Healthcare Materials, in which researchers tweaked the antiparasitic drug niclosamide to target cells implicated in endometriosis, while an npj Women's Health analysis reports that among Denmark's top 100 grant-awarding foundations endometriosis research received EUR 173,958 compared with EUR 254,908,430 for diabetes and EUR 325,940 for inflammatory bowel disease, alongside a wide gap in media mentions.
A Nature news story describes a mouse study, published in Advanced Healthcare Materials, in which researchers tweaked the antiparasitic drug niclosamide to target cells implicated in endometriosis, while an npj Women's Health analysis reports that among Denmark's top 100 grant-awarding foundations endometriosis research received EUR 173,958 compared with EUR 254,908,430 for diabetes and EUR 325,940 for inflammatory bowel disease, alongside a wide gap in media mentions.
Danish foundation data show endometriosis research received EUR 173,958 versus EUR 254.9 million for diabetes, while a mouse study reports a tweaked niclosamide reversed lesion-induced macrophage changes
A Nature news story describes a mouse study, published in Advanced Healthcare Materials, in which researchers tweaked the antiparasitic drug niclosamide to target cells implicated in endometriosis, while an npj Women's Health analysis reports that among Denmark's top 100 grant-awarding foundations endometriosis research received EUR 173,958 compared with EUR 254,908,430 for diabetes and EUR 325,940 for inflammatory bowel disease, alongside a wide gap in media mentions.
A Nature news story describes a mouse study, published in Advanced Healthcare Materials, in which researchers tweaked the antiparasitic drug niclosamide to target cells implicated in endometriosis, while an npj Women's Health analysis reports that among Denmark's top 100 grant-awarding foundations endometriosis research received EUR 173,958 compared with EUR 254,908,430 for diabetes and EUR 325,940 for inflammatory bowel disease, alongside a wide gap in media mentions.
Journal of Applied Health Sciences and Medicine Sep 30, 2026
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
5-Fluorouracil forms molecular complexes with anionic SDS and cationic TBAB micelles: TBAB equilibrates faster and more stably while SDS shows relaxation processes
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
Using UV-Vis spectroscopy under physiological conditions (pH 7.4, 37 °C, 0.1 mM), this study tracked the interaction of the anticancer drug 5-fluorouracil (5-FU) with the anionic micelle SDS and the cationic micelle TBAB, finding that both form molecular complexes treatable as reversible first-order equilibria: TBAB gave k* = 17 × 10⁻³ min⁻¹, t1/2 = 40.76 min and Keq = 17.72, whereas SDS gave k* = 7.60 × 10⁻³ min⁻¹, t1/2 = 91.20 min and Keq = 12.38, with SDS involving relaxation equilibrium processes because both reactants carry negative charge, and both complexes showed negative ΔG⁰ (SDS −6486.62 J/mol, TBAB −7409.98 J/mol), indicating spontaneous binding driven by van der Waals forces or hydrogen bonding.
The FASEB Journal Sep 30, 2026
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating Metabolomics, Mendelian Randomization, and Machine Learning, a Study Flags Phenylalanine and Its Transporter SLC6A14 as Candidate Diagnostic and Therapeutic Targets in Pancreatic Cancer
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
Integrating plasma metabolomics, Mendelian randomization, and machine learning, this study identified phenylalanine as causally associated with pancreatic cancer among 55 plasma metabolites (IVW OR = 1.641, 95% CI 1.052–2.562, p = 0.029), derived eight related differentially expressed genes, built a random forest diagnostic model from 113 combinations of 12 algorithms (training AUC 0.994; validation AUCs 0.918, 0.983, 0.923), used SHAP to rank SLC6A14 as the top feature, and combined single-cell sequencing, simulated gene knockout, molecular docking, and molecular dynamics to suggest genistein binds SLC6A14 stably, with RT-qPCR confirming high expression of the five model genes in a BxPC-3 versus HPDE6-C7 cell pair.
The FASEB Journal Sep 30, 2026
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
Integrating machine learning with multilayer transcriptomics pins JAK2 and ANXA5 as key genes linking obstructive sleep apnea to oxidative stress, validated in patient adipose tissue, intermittent-hypoxia mice, and post-CPAP samples
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
medRxiv Sep 30, 2026
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
1,003 people with depression rated psychotherapy with different levels of AI involvement: they preferred human therapists, willing to pay 31.6% less for assistive or collaborative AI and 57.2% less for fully autonomous AI
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.
The study had 1,003 participants with depression read vignettes describing psychotherapy options with different levels of AI involvement (a human therapist without AI, assistive AI, collaborative AI, and fully autonomous AI) and rate them; participants consistently evaluated human therapists more favorably, reporting greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability, and compared with a human therapist they were willing to pay 31.6% less for therapists using assistive or collaborative AI and 57.

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