Randomised controlled trials of dual antiplatelet therapy versus aspirin in patients with stroke or transient ischaemic attack: an updated meta-analysis.

Introduction: We explored the efficacy and safety of dual antiplatelet therapy (DAPT) for individuals diagnosed with stroke or transient ischaemic attack (TIA), incorporating the latest insights from randomised controlled trials (RCTs). The emerging evidence surrounding DAPT in stroke and TIA plays a pivotal role in guiding clinical decisions.

Methods: Our study included five RCTs (INSPIRES, THALES, POINT, CHANCE, FASTER) on DAPT (aspirin + P2Y12 inhibitor) initiated within 72 hours of acute stroke or TIA, which evaluated DAPT efficacy and safety over 21-90 days, focusing on new strokes and major bleeding.

Machine learning based prediction model for bile leak following hepatectomy for liver cancer.

Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.

Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.

Enhancing Access to Mental Health Services for Antepartum and Postpartum Women Through Telemental Health Services at Wellbeing Centers in Selected Health Facilities in Bangladesh: Implementation Research.

Background: Globally, 10% of pregnant women and 13% of postpartum women experience mental disorders. In Bangladesh, nearly 50% of mothers face common mental disorders, but mental health services and trained professionals to serve their needs are scarce. To address this, the government of Bangladesh's Non-Communicable Disease Control program initiated "Wellbeing Centers," telemental health services in selected public hospitals.

Deep eutectic solvent-based green extraction of Strychnos potatorum seed phenolics: Process optimization via response surface methodology and artificial neural network.

The current research focused on extraction optimization of bioactive compounds from Strychnos potatorum seeds (SPs) using an eco-friendly glycerol-sodium acetate based deep eutectic solvent (DES). The optimization was accomplished using response surface methodology (RSM) and artificial neural networking (ANN). The independent variables included shaking time (A), temperature (B), and solvent-to-feed ratio (C), and the responses were the extraction yield, total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (DPPH), and antidiabetic activity (α-amylase inhibitory activity).

Enhancing the productivity and resilience of rice () under environmental stress conditions using clustered regularly interspaced short palindromic repeats (CRISPR) technology.

Rice (Oryza sativa ) is a crucial staple crop worldwide, providing nutrition to more than half of the global population. Nonetheless, the sustainability of grain production is increasingly jeopardized by both biotic and abiotic stressors exacerbated by climate change, which increases the crop's rvulnerability to pests and diseases. Genome-editing by clustered regularly interspaced short palindromic repeats and CRISPR-associated Protein 9 (CRISPR-Cas9) presents a potential solution for enhancing rice productivity and resilience under climatic stress.