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https://www.selleckchem.com/products/ly2880070.html https//clinicaltrials.gov. Unique identifier NCT02446587. Prediction of infarct extent among patients with acute ischemic stroke using computed tomography perfusion is defined by predefined discrete computed tomography perfusion thresholds. Our objective is to develop a threshold-free computed tomography perfusion-based machine learning (ML) model to predict follow-up infarct in patients with acute ischemic stroke. Sixty-eight patients from the PRoveIT study (Measuring Collaterals With Multi-Phase CT Angiography in Patients With Ischemic Stroke) were used to derive a ML model using random forest to predict follow-up infarction voxel by voxel, and 137 patients from the HERMES study (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) were used to test the derived ML model. Average map, T , cerebral blood flow, cerebral blood volume, and time variables including stroke onset-to-imaging and imaging-to-reperfusion time, were used as features to train the ML model. Spatial and volumetric agreement between the ML model predicted follow-up i patients with acute ischemic stroke better than current methods. The causes of recurrent ischemic stroke despite anticoagulation for atrial fibrillation are uncertain but might include small vessel occlusion. We investigated whether magnetic resonance imaging markers of cerebral small vessel disease (SVD) are associated with ischemic stroke risk during follow-up in patients anticoagulated for atrial fibrillation after recent ischemic stroke or transient ischemic attack. We analyzed data from a prospective multicenter inception cohort study of ischemic stroke or transient ischemic attack anticoagulated for atrial fibrillation (CROMIS-2 [Clinical Relevance of Microbleeds in Stroke Study]). We rated markers of SVD on baseline brain magnetic resonance imaging basal ganglia perivascular spaces (number ≥11); cerebral microbleeds (number ≥1); lacunes (number ≥1); a

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