Co3O4-RuO2/Ti3C2Tx MXene Electrocatalysts for Oxygen Evolution Reaction in Acidic and Alkaline Media.

MXene 2D materials and non-noble transition metal oxide nanoparticles have been proposed as novel pH-universal platforms for oxygen evolution reaction (OER), owing to the enhancement of active site exposures and conductivity. Herein, Co3O4-RuO2 /Ti3C2Tx/carbon cloths (CRMC) were assembled in a facile way as an efficient OER platform through a hydrothermal process. The Co3O4-RuO2/Ti3C2Tx demonstrated prominent OER catalytic performance under acidic and alkaline conditions, which showed overpotential values of 195 and 247 mV at 10 mA cm-2 with Tafel slopes of 93 and 97 mVdec-1, respectively.

Top-down attention and Alzheimer's pathology impact cortical selectivity during learning, influencing episodic memory in older adults.

Human aging affects the ability to remember new experiences, in part, because of altered neural function during memory formation. One potential contributor to age-related memory decline is diminished neural selectivity -- i.e.

Robotic Versus Laparoscopic Liver Resection: A Nationwide Propensity Score Matched Analysis.

Objective: To compare nationwide outcomes of robotic liver resection (RLR) with laparoscopic liver resection (LLR).

Background: Minimally invasive liver resection is increasingly performed using the robotic approach as this could help overcome inherent technical limitations of laparoscopy. It is unknown if this translates to improved patient outcomes.

The Family Anesthesia Experience (FAX): Implementing a Social Support-Focused Wellness Programme Across Multiple Residency Programmes.

Introduction: Lack of social support negatively impacts medical trainees' wellness. Programmes to educate medical trainees and their support persons (SPs) have been developed to improve resident wellness, but implementation of these programmes at other institutions remains unclear. We aimed to demonstrate the feasibility of implementing the Family Anesthesia Experience (FAX) programme across multiple institutions and to assess the programme's utility.

Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.

Background: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to screen participants with high risk of PCa and, specifically, investigated whether participants with metabolic syndrome (MetS) exhibited an elevated PCa risk.

Methods: A prospective cohort study was performed with 41,837 participants in South Korea.