{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:48:51Z","timestamp":1782989331982,"version":"3.54.5"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":125,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Mutation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Reliable molecular biomarkers for poststroke cognitive impairment (PSCI) remain limited. Using publicly available bulk transcriptomic and single\u2010cell RNA\u2010seq datasets from GEO, we investigated lactate metabolism\u2013 and pyroptosis\u2010related signatures and developed a diagnostic model. Differential expression analysis, KEGG pathway enrichment, and weighted gene coexpression network analysis (WGCNA) were performed, followed by multialgorithm feature selection (LASSO, SVM\u2010RFE, and random forest). A logistic regression classifier was trained in the discovery cohort and externally validated in an independent cohort. Glycolysis\/lactate metabolism, HIF\u20101 signaling, and NOD\u2010like receptor\u2010related pathways were enriched in PSCI\u2010associated samples, and key coexpression modules were strongly correlated with ischemic injury traits. Cross\u2010model consensus identified LDHA, GSDMD, and CASP1 as hub genes, yielding an AUC of 0.912 (95% bootstrap CI: 0.841\u20130.983) in the training cohort and 0.885 (95% bootstrap CI: 0.798\u20130.972) in the validation cohort. Immune deconvolution and scRNA\u2010seq validation suggested increased proinflammatory microglia\u2010associated signals, with relatively higher LDHA expression in microglia than in neurons; cell\u2013cell communication analysis highlighted inflammatory interactions including IL1B\u2013IL1R1. Connectivity map (CMap) analysis nominated candidate compounds, and molecular docking predicted favorable binding between oxamate and LDHA (binding energy\u2009=\u2009\u22129.5\u2009kcal\/mol). Collectively, these findings propose a compact LDHA\/GSDMD\/CASP1 biomarker panel for PSCI diagnosis and provide hypothesis\u2010generating therapeutic leads that warrant further experimental validation.<\/jats:p>","DOI":"10.1155\/humu\/2963117","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:49:31Z","timestamp":1778060971000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of a Diagnostic Model and Drug Prediction for Postischemic Stroke Cognitive Impairment Based on Machine Learning Screening of Lactate Metabolism\u2013 and Pyroptosis\u2010Related 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