Pulmonary Embolism Response Teams-Evidence of Benefits? A Systematic Review and Meta-Analysis.

Venous thromboembolisms constitute a major cause of morbidity and mortality with 60,000 to 100,000 deaths attributed to pulmonary embolism in the US annually. Both clinical presentations and treatment strategies can vary greatly, and the selection of an appropriate therapeutic strategy is often provider specific. A pulmonary embolism response team (PERT) offers a multidisciplinary approach to clinical decision making and the management of high-risk pulmonary emboli.

Metoprolol vs diltiazem for atrial fibrillation with rapid ventricular rate: Systematic review and meta-analysis of adverse events.

Background: Intravenous (IV) diltiazem and metoprolol are commonly used to achieve rate control for atrial fibrillation with RVR (Afib with RVR), and are both recommended as first-line by current guidelines. While prior studies investigated the efficacy of these medications, there is little evidence available regarding the risk of adverse events (AEs) with their use.

Methods: We identified randomized controlled trials (RCT) and observational studies reporting rates of AEs following administration of IV diltiazem and metoprolol for Afib with RVR by searching PubMed, SCOPUS, EMBASE, and Cochrane Library.

Comparison of antioxidant and antimicrobial activities of submerged culture mycelium and basidioma extracts of .

Unlabelled: This research aimed to assess the biological characteristics of both submerged culture mycelium and artificial basidioma of NTH-PL4. The extraction yield from the basidioma surpassed that of the mycelium. The use of hot water extract resulted in the highest total carbohydrate content, predominantly found in the basidioma.

Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms.

Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability.

Enhancing lesion detection in liver and kidney CT scans via lesion mask selection from two models: A main model and a model focused on small lesions.

Automated segmentation and detection of tumors in CT scans of the liver and kidney have a significant potential in assisting clinicians with cancer diagnosis and treatment planning. However, current approaches, including state-of-the-art deep learning ones, still face many challenges. Many tumors are not detected by these approaches when tested on public datasets for tumor detection and segmentation such as the Kidney Tumor Segmentation Challenge (KiTS) and the Liver tumor segmentation challenge (LiTS).