Formulation of Quality Improvement Methodology for Risk Mitigation in Congenital Cardiac Catheterization.

The C3PO collaborative, with a history of successful quality improvement (QI) initiatives, leveraged registry participants to develop a multi-center QI initiative to reduce adverse events (AEs) in congenital cardiac catheterization. A 32-person, interdisciplinary working group analyzed audited data for all congenital cardiac catheterization cases from 2014-2017. The primary outcome was the occurrence of any high-severity (level 3/4/5) AE.

Dual-Driven Activation of High-Valence States in Prussian Blue Analogues Via Graphene-Quantum Dots and Ozone-Induced Surface Restructuring for Superior Hydrogen Evolution Electrocatalyst.

Electrochemical water splitting is a pivotal process for sustainable hydrogen energy production, relying on efficient hydrogen evolution reaction (HER) catalysts, particularly in acidic environments, where both high activity and durability are crucial. Despite the favorable kinetics of platinum (Pt)-based materials, their performance is hindered under harsh conditions, driving the search for alternatives. Due to their unique structural characteristic, Prussian blue analogs (PBAs) emerge as attractive candidates for designing efficient HER electrocatalysts.

Safety of transarterial chemoembolization on renal function in combined hepatocellular carcinoma and chronic kidney disease patients.

This study was to investigate the safety of transarterial chemoembolization (TACE) which required injection of contrast medium on renal function in combined hepatocellular carcinoma and chronic kidney disease (CKD) patients. A total of 265 patients admitted for the first session of TACE were included for analysis. CKD was defined as Cockcroft-Gault glomerular filtration rate (CG-GFR) < 60 mL/min/1.

A machine learning approach for identification of vascular access patency in hemodialysis patients using photoplethysmography: A pilot study.

Introduction: Vascular access (VA) is essential for patients with hemodialysis, and its dysfunction is a major complication that can reduce quality of life or even threaten life. VA patency is not only difficult to predict on an individual basis, but also challenging to predict in real-time. To overcome this challenge, this study aimed to develop a machine learning approach to predict 6-month primary patency (PP) using photoplethysmography (PPG) signals acquired from the tips of both index fingers.