Efficacy and safety of tirofiban in acute ischemic stroke patients with ideal reperfusion: A cohort study of LAA and CE subgroups.

Background And Objectives: Despite achieving ideal reperfusion (eTICI = 3) through endovascular treatment (EVT), some acute ischemic stroke (AIS) patients still experience poor outcomes. This study aims to evaluate the efficacy and safety of tirofiban in AIS patients with ideal reperfusion, focusing on its effects in large artery atherosclerosis (LAA) and cardioembolic (CE) stroke.

Methods: A total of 474 AIS patients from the RESCUE-BT database were included.

Energy-Saving Electrochemical Hydrogen Production via Amorphous CoMoP/NF Bifunctional Catalyst Coupled with HMF Oxidation Reactions in Alkaline Electrolyte.

To improve water splitting efficiency and enhance energy utilization, it is crucial to develop catalysts with excellent activity, long-term stability, and low cost. In this study, we synthesized a three-dimensional nanostructured amorphous CoMoP/NF bifunctional catalyst for both the hydrogen evolution reaction (HER) and the 5-hydroxymethylfurfural oxidation reaction (HMFOR), using a sacrificial template method. Benefiting from element doping regulation and morphology control, CoMoP/NF exhibited outstanding catalytic activity.

Primary pulmonary meningioma: a case report and literature review.

Introduction: Primary pulmonary meningioma is a rare disease. There have been only a little over 50 cases of primary pulmonary meningioma (PPM) reported in previous literature. The pathogenesis of PPM is still unclear.

Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.