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Ventricular Depolarization Abnormalities and Their Role in Cardiac Risk Stratification - A Narrative Review.
Rev Cardiovasc Med
January 2025
Department of Cardiovasculair Sciences, KU Leuven, 3000 Leuven, Belgium.
Ventricular depolarization refers to the electrical activation and subsequent contraction of the ventricles, visible as the QRS complex on a 12-lead electrocardiogram (ECG). A well-organized and efficient depolarization is critical for cardiac function. Abnormalities in ventricular depolarization may indicate various pathologies and can be present in all leads if the condition is general, or in a subgroup of anatomically contiguous leads if the condition is limited to the corresponding anatomic location of the heart.
View Article and Find Full Text PDFIntranasal oxytocin for apathy in people with frontotemporal dementia (FOXY): a multicentre, randomised, double-blind, placebo-controlled, adaptive, crossover, phase 2a/2b superiority trial.
Lancet Neurol
February 2025
Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada; Department of Cognitive Neurology, St Joseph's Health Care London, London, ON, Canada. Electronic address:
Background: No treatments exist for apathy in people with frontotemporal dementia. Previously, in a randomised double-blind, placebo-controlled, dose-finding study, intranasal oxytocin administration in people with frontotemporal dementia improved apathy ratings on the Neuropsychiatric Inventory over 1 week and, in a randomised, double-blind, placebo-controlled, crossover study, a single dose of 72 IU oxytocin increased blood-oxygen-level-dependent signal in limbic brain regions. We aimed to determine whether longer treatment with oxytocin improves apathy in people with frontotemporal dementia.
View Article and Find Full Text PDFA Deep Learning Approach for Mental Fatigue State Assessment.
Sensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFCardiovascular Disease Screening in Primary School Children.
Children (Basel)
December 2024
School of Medicine, University of Crete, 71 003 Heraklion, Crete, Greece.
Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations.
Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children.
Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review.
Children (Basel)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field.
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