Evolocumab for early reduction of LDL-C levels in patients with acute ischemic stroke: a randomized controlled trial.

Background: Low-density lipoprotein cholesterol (LDL-C) has been determined as an established risk factor for acute ischemic stroke (AIS). Despite the recommendation for in-hospital initiation of high-intensity statin therapy in AIS patients, achieving the desired target LDL-C levels remains challenging. Evolocumab, a highly effective and quickly acting agent for reducing LDL-C levels, has yet to undergo extensively exploration in the acute phase of AIS.

Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy.

Background: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Infiltration and alterations in non-cardiomyocytes of the human heart involve crucially in the occurrence of DCM and associated immunotherapeutic approaches.

Methods: We constructed a single-cell transcriptional atlas of DCM and normal patients.

Novel compound heterozygous mutations in the LARS2 gene in a Chinese family with hearing loss.

Background: Mutations in the LARS2 gene are correlated with Perrault syndrome, a rare autosomal recessive genetic disorder, that is typically characterized by sensorineural hearing loss and ovarian insufficiency.

Methods: Whole-exome sequencing and mutational analysis were employed to identify hearing loss-causing genes in a Chinese family from the Guangxi Zhuang Autonomous Region. Clinical phenotypes, audiological data, and color Doppler ultrasound of the family were collected, and a series of computer software were used to analyze the impact of genetic variations on protein structure and function.

Burden of coronary artery calcification in ANCA-associated vasculitis.

Background: Cardiovascular disease (CVD) is a leading cause of death in ANCA-associated vasculitis (AAV). Screening and primary cardiovascular prevention may improve outcomes.

Methods: We identified patients in the 2002-2019 Mass General Brigham AAV cohort with thoracic CT scans obtained for other clinical purposes.

Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels.

Objective: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning.