Establishing a Novel Animal Model of Tricuspid Regurgitation by Self-expanding Stent.

Aim: To establish a simple, safe, and reproducible animal model of tricuspid regurgitation (TR).

Methods: A self-expanding stent made of nickel-titanium shape memory metal alloy was developed. Ten white pigs were randomized into an experimental group (n = 7) and a control group (n = 3).

No Association of Polycystic Ovary Syndrome with Pancreatic Cancer: A Mendelian Randomization Study.

Unlabelled: Recently, there has been a debate regarding the association between polycystic ovary syndrome (PCOS) and pancreatic cancer (PC). In order to examine the causal relationship between PCOS and PC, we conducted a Mendelian randomization study, which utilized 12 single nucleotide polymorphisms (SNPs) identified from a genome-wide association study (GWAS) meta-analysis that included 10,074 PCOS cases and 103,164 controls of European ancestry as instrumental variables (IVs). The outcome data were obtained from the FinnGen database (including 605 cases and 218,187 controls).

Weakly supervised learning in thymoma histopathology classification: an interpretable approach.

Introduction: Thymoma classification is challenging due to its diverse morphology. Accurate classification is crucial for diagnosis, but current methods often struggle with complex tumor subtypes. This study presents an AI-assisted diagnostic model that combines weakly supervised learning with a divide-and-conquer multi-instance learning (MIL) approach to improve classification accuracy and interpretability.

Case Report: A review of two children with deep sternal wound infections after precordial surgery treated with a simple negative pressure closed drainage technique.

Deep sternal wound infection (DSWI) is a rare but potentially devastating complication of median sternotomy performed in cardiac surgery. This report summarizes the nursing management of two pediatric cases with a DSWI treated using Do It Yourself (DIY) negative pressure suction (DIY-NPS) after surgery. The technique maintains a continuous suction pressure of 75 mmHg and intermittently flushes small volumes of fluid to stimulate granulation tissue formation and control systemic infection.

Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning.

Experimental and theoretical studies on the compositional changes of new particle formation in the nucleation and initial growth stages of acid-base systems (2 and 5 nm) are extremely challenging. This study proposes a machine learning method for predicting the composition change of the sulfuric acid-dimethylamine system in the transformation from monomer to nanoparticle by learning the structure and composition information on small-sized sulfuric acid (SA)-dimethylamine (DMA) molecular clusters. Based on this method and changes in components, we found that the sulfuric acid-dimethylamine growth was mainly through the alternate adsorption of (SA)(DMA), (SA)(DMA), and (SA) clusters at the early stage of nucleation, which accounted for about 70, 20, and 10%, respectively.