Comparison of downsizing strategy (HANGZHOU Solution) and standard annulus sizing strategy in type 0 bicuspid aortic stenosis patients undergoing transcatheter aortic valve replacement: Rationale and design of a randomized clinical trial.

Background: There has not been a consensus on the prothesis sizing strategy in type 0 bicuspid aortic stenosis (AS) patients undergoing transcatheter aortic valve replacement (TAVR). Modifications to standard annular sizing strategies might be required due to the distinct anatomical characteristics. We have devised a downsizing strategy for TAVR using a self-expanding valve specifically for patients with type 0 bicuspid AS.

Technical Success after Transcatheter Aortic Valve Replacement for Bicuspid versus Tricuspid Aortic Stenosis.

Background: Comparative data of the Valve Academic Research Consortium (VARC-3)-defined technical success between bicuspid versus tricuspid aortic stenosis (AS) remain lacking. Aims: We sought to compare the technical success and other clinical outcomes between patients with bicuspid and tricuspid AS receiving transcatheter aortic valve replacement. Methods: A registration-based analysis was performed for 402 patients (211 and 191 cases of bicuspid and tricuspid AS, respectively).

Gut Microbiome Signatures in the Progression of Hepatitis B Virus-Induced Liver Disease.

The gut microbiome is associated with hepatitis B virus (HBV)-induced liver disease, which progresses from chronic hepatitis B, to liver cirrhosis, and eventually to hepatocellular carcinoma. Studies have analyzed the gut microbiome at each stage of HBV-induced liver diseases, but a consensus has not been reached on the microbial signatures across these stages. Here, we conducted by a systematic meta-analysis of 486 fecal samples from publicly available 16S rRNA gene datasets across all disease stages, and validated the results by a gut microbiome characterization on an independent cohort of 15 controls, 23 chronic hepatitis B, 20 liver cirrhosis, and 22 hepatocellular carcinoma patients.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.

Background: Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.

Methods: Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database.