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Background: Atrial infarction is a complication of myocardial infarction with ventricular infarction; however, isolated atrial infarction (IAI) has rarely been reported. Herein, we report a case of IAI associated with sick sinus syndrome and atrial fibrillation (AF).

Case Summary: An 83-year-old woman was brought to the emergency department with a complaint of general malaise.

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Introduction/objective: Atrial fibrillation (AF) could present with slow ventricular-response; bradycardia could facilitate the emergence of AF. The conviction that one "does not succumb" from bradycardia as an escape rhythm will emerge unless one sustains a fatal injury following syncope is in stark difference with ventricular tachyarrhythmia (VA), which may promptly cause cardiac arrest. However, this is not always the case, as a life-threatening situation may emerge during the bradycardic episode, i.

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[The characteristics of gastric electric rhythm in obese patients].

Zhonghua Yi Xue Za Zhi

December 2024

Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing100730, China.

To investigate the characteristics of gastric electric rhythm in patients with obesity. Obese patients who were scheduled to undergo weight reduction surgery in Peking Union Medical College Hospital from January 2018 to February 2024 were prospectively included. According to body mass index (BMI), the patients were divided into mild-to-moderate obesity group (28 kg/m≤BMI<40 kg/m) and severe obesity group (BMI≥40 kg/m).

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Smartphone-based technology is increasingly used in veterinary medicine as a complementary tool for cardiac auscultation and ECG recording. This prospective observational study evaluates a digital stethoscope (DS) that consists of simultaneous phonocardiographic and one-lead ECG recording (dECG) in adult horses, comparing it with auscultation using a conventional stethoscope (CS) and reference base-apex ECG (rECG). A total of 104 horses underwent cardiac auscultation using CSs and DSs to detect arrhythmias and murmurs.

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Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability.

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