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Background Oligodendrocyte precursor cells (OPCs) regulate neuronal, glial, and vascular systems in diverse ways and display phenotypic heterogeneity beyond their established role as a reservoir for mature oligodendrocytes. However, the detailed phenotypic changes of OPCs after cerebral ischemia remain largely unknown. Here, we aimed to investigate the roles of reactive OPCs in the ischemic brain. Methods and Results The behavior of OPCs was evaluated in a mouse model of ischemic stroke produced by transient middle cerebral artery occlusion in vivo. For in vitro experiments, the phenotypic change of OPCs after oxygen glucose derivation was examined using a primary rat OPC culture. Furthermore, the therapeutic potential of hypoxic OPCs was evaluated in a mouse model of middle cerebral artery occlusion in vivo. Perivascular OPCs in the cerebral cortex were increased alongside poststroke angiogenesis in a mouse model of middle cerebral artery occlusion. In vitro RNA‐seq analysis revealed that primary cultured OPCs increased the gene expression of numerous pro‐angiogenic factors after oxygen glucose derivation. Hypoxic OPCs secreted a greater amount of pro‐angiogenic factors, such as vascular endothelial growth factor and angiopoietin‐1, compared with normoxic OPCs. Hypoxic OPC‐derived conditioned media increased the viability and tube formation of endothelial cells. In vivo studies also demonstrated that 5 consecutive daily treatments with hypoxic OPC‐conditioned media, beginning 2 days after middle cerebral artery occlusion, facilitated poststroke angiogenesis, alleviated infarct volume, and improved functional disabilities. Conclusions Following cerebral ischemia, the phenotype of OPCs in the cerebral cortex shifts from the parenchymal subtype to the perivascular subtype, which can promote angiogenesis. The optimal use of hypoxic OPCs secretome would provide a novel therapeutic option for stroke.

The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66–0.79); CHADS2, 0.61 (95%CI, 0.53–0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54–0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF. [ABSTRACT FROM AUTHOR]