Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital.

Increased atrial fibrillation risk in Parkinson's disease: A nationwide population-based study.

Objective: Parkinson's disease (PD) is the second most common neurodegenerative disorder associated with various morbidities. Although the relationship between cardiovascular disease and PD has been studied, a paucity of information on PD and atrial fibrillation (AF) association exists. Thus, we aimed to investigate whether patients with PD have an increased risk of AF.

Radiosensitization of tumor cells by modulation of ATM kinase.

Purpose: To elucidate the relationship between the radiation-induced activation of ataxia telangiectasia mutated (ATM) kinase, G2 arrest and the caffeine-induced radiosensitization.

Method: RKO cells (human colorectal cancer cells) and ATM kinase over-expressing RKO/ATM cells were used. The cellular radiosensitivity was determined with clonogenic survival assay and the cell cycle progression, including G2 arrest, was studied with flow cytometry.