100 research outputs found

    Author Correction: Federated learning enables big data for rare cancer boundary detection.

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    10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

    Polygenic risk modeling for prediction of epithelial ovarian cancer risk

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    10.1038/s41431-021-00987-7EUROPEAN JOURNAL OF HUMAN GENETICS303349-36

    Cross-talk between GABAergic postsynapse and microglia regulate synapse loss after brain ischemia

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    Microglia interact with neurons to facilitate synapse plasticity; however, signal(s) contributing to microglia activation for synapse elimination in pathology are not fully understood. Here, using in vitro organotypic hippocampal slice cultures and transient middle cerebral artery occlusion (MCAO) in genetically engineered mice in vivo, we report that at 24 hours after ischemia, microglia release brain-derived neurotrophic factor (BDNF) to downregulate glutamatergic and GABAergic synapses within the peri-infarct area. Analysis of the cornu ammonis 1 (CA1) in vitro shows that proBDNF and mBDNF downregulate glutamatergic dendritic spines and gephyrin scaffold stability through p75 neurotrophin receptor (p75NTR) and tropomyosin receptor kinase B (TrkB) receptors, respectively. After MCAO, we report that in the peri-infarct area and in the corresponding contralateral hemisphere, similar neuroplasticity occurs through microglia activation and gephyrin phosphorylation at serine-268 and serine-270 in vivo. Targeted deletion of the Bdnf gene in microglia or GphnS268A/S270A (phospho-null) point mutations protects against ischemic brain damage, neuroinflammation, and synapse downregulation after MCAO.ISSN:2375-254

    Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

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    10.1038/s41431-022-01085-yEUROPEAN JOURNAL OF HUMAN GENETICS305630-63

    Federated learning enables big data for rare cancer boundary detection.

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    Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

    Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

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    Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

    Polygenic risk modeling for prediction of epithelial ovarian cancer risk

    Get PDF
    Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

    Cross-talk between GABAergic postsynapse and microglia regulate synapse loss after brain ischemia

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    Microglia interact with neurons to facilitate synapse plasticity; however, signal(s) contributing to microglia activation for synapse elimination in pathology are not fully understood. Here, using in vitro organotypic hippocampal slice cultures and transient middle cerebral artery occlusion (MCAO) in genetically engineered mice in vivo, we report that at 24 hours after ischemia, microglia release brain-derived neurotrophic factor (BDNF) to downregulate glutamatergic and GABAergic synapses within the peri-infarct area. Analysis of the cornu ammonis 1 (CA1) in vitro shows that proBDNF and mBDNF downregulate glutamatergic dendritic spines and gephyrin scaffold stability through p75 neurotrophin receptor (p75NTR) and tropomyosin receptor kinase B (TrkB) receptors, respectively. After MCAO, we report that in the peri-infarct area and in the corresponding contralateral hemisphere, similar neuroplasticity occurs through microglia activation and gephyrin phosphorylation at serine-268 and serine-270 in vivo. Targeted deletion of the Bdnf gene in microglia or GphnS268A/S270A (phospho-null) point mutations protects against ischemic brain damage, neuroinflammation, and synapse downregulation after MCAO

    Polygenic risk modeling for prediction of epithelial ovarian cancer risk

    No full text
    Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, select and shrink for summary statistics (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs
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