Frontiers in Physiology

BackgroundThoracic spine mobilization (TSM) has been proposed to influence autonomic nervous system (ANS) activity, yet evidence remains inconsistent and feasibility of standardised protocols is unclear. This study aimed to evaluate whether a randomized TSM protocol can be implemented successfully in healthy participants and to provide preliminary estimates of its effects on heart rate variability (HRV) and heart rate (HR). MethodsA randomized feasibility trial was conducted with healthy young ...

Abdominal aortic aneurysms (AAA) affect more than 1% of adults over 50 and carry significant mortality risk. Current surveillance relies on intermittent imaging (ultrasound or MRI) at 6-24 month intervals, which may miss rapid growth acceleration between visits. We investigate the feasibility of continuous aneurysm diameter tracking using photoplethysmography (PPG) signals. Using a one-dimensional hemodynamic model that simulates pulse wave propagation from the heart to the digital artery, we de...

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component ...

Societies are aging rapidly in parallel with the increasingly earlier onset of serious diseases in younger populations. These and other factors are creating a substantial disparity between healthspan, the period of life where an individual is free from serious chronic disease or disability, and lifespan -- expanding the morbidity span. Extending healthspan has thus become a major priority. To pursue an integrated strategy toward healthspan support, we launched DELTA, a prospective, open-label, i...

Background: Pressure volume (PV) loop analysis remains the gold standard for assessing the intrinsic global diastolic properties of the left ventricle (LV). Traditional fitting techniques rely on local, phase-constrained fittings and are limited due to their sensitivity to noise, landmark selection, violation of assumptions, and non-convergence. Objective: To develop and validate DIAPINN, a physics-informed neural network (PINN) framework capable of calculating intrinsic diastolic properties of ...

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration...

Current clinical risk stratification for thoracic aortic aneurysms (TAA) relies primarily on maximum diameter, which is a poor predictor of rupture. Recent fluid-structure interaction studies have identified a dimensionless "flutter instability parameter" (N{omega} ) that accurately classifies abnormal aortic growth. However, this parameter currently serves as a static diagnostic snapshot. In this work, we propose a proof-of-concept computational framework that links flutter instability to micro...

ObjectiveThis study aimed to characterize the activation of lower urinary tract (LUT) targets in response to pudendal nerve stimulation (PNS) in awake human participants. Materials and MethodsIn this single center study, recruited participants had an implanted pudendal neurostimulator for treatment of their symptoms including overactive bladder, incontinence, urinary retention, and/or pelvic pain. Participants came in for a modified urodynamic study where a multichannel manometry catheter was p...

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables non-invasive characterization of carotid atherosclerotic plaque. PurposeTo evaluate the performance and reproducibility of a simplified DCE-MRI quantification method for carotid plaque assessment. MethodsT1-weighted black-blood DCE-MRI of the carotid arteries at 3T was performed at baseline and after six months in patients with mild-to-moderate atherosclerotic lesions in a pilot placebo-controlled randomized trial...

BackgroundIn critically ill patients admitted to the intensive care unit (ICU), rapid skeletal muscle atrophy frequently develops in the acute phase. This ICU-acquired weakness can significantly impair long-term physical function. Although the biceps brachii cross-sectional area (CSA) is commonly used to assess muscle atrophy, its ultrasound imaging can be technically challenging, and the flexor carpi ulnaris may offer a more accessible alternative. Therefore, this study aimed to investigate whe...

BackgroundNormative pediatric electrocardiographic (ECG) parameters are standardized, but lack temporal resolution for neonates and infants. These values are clinically important, as they support the diagnosis, risk stratification, and management of cardiovascular diseases (CVD). MethodsFive ECG parameters (heart rate (HR), QRS, PR, QT, QTc intervals) were retrospectively analyzed from 7,346 recordings from 6,967 patients at a large pediatric hospital. Patients were only included if their ECG w...

We present a deep learning model that predicts left atrial (LA) volume from standard 12-lead ECG recordings and basic patient data. This approach offers a low-cost, scalable alternative to MRI-based LA volume measurement, which remains the clinical gold standard but is often inaccessible. Our model performs regression directly on LA volume targets and leverages Shapley values to provide interpretable feature importance. Results highlight the predictive value of ECG signals and demonstrate that p...

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and ti...

AimsCatheter ablation using radiofrequency (RF) or pulsed field (PF) energy is an effective treatment method for ventricular arrhythmia (VA). PF offers advantages in lesion formation in anatomically challenging regions. However, its acute effects on ventricular contractility during substrate modification require further elucidation. This study aimed to compare real-time hemodynamic changes associated with PF versus radiofrequency ablation in the left ventricle using stroke volume (SV) as a surro...

This research presents multimodal deep learning for structural heart disease prediction. We evaluated multiple deep learning architectures, including TCN, Simple CNN, ResNet1d18, Light transformer and Hybrid model. The models were examined across the three seeds to ensure robustness, and bootstrap confidence interval is used to measure performance differences. TCN consistently outperforms other competing architectures, achieving statistically significant improvements with stable performance acro...

AimsWe aimed to develop and evaluate fully automated artificial intelligence (AI) system. for detection of mitral valve prolapse (MVP) and mitral regurgitation (MR) from echocardiographic studies. Methods and ResultsWe used a dataset of 24,869 echocardiographic studies from the University of California San Francisco (UCSF) to train a multi-view deep neural network (DNN) to detect MVP using apical 4-chamber, 2-chamber, and parasternal long-axis views. A separate dataset of 27,906 studies from UC...

Background and AimSedentary lifestyle and obesity are considered to be significant risk factors that create a pathway for the appearance of the sedentary cardiac phenotype consisting of cardiac atrophy, myocardial stiffening, and altered haemodynamics. Although exercise training has the potential to reverse this detrimental process, the literature data on the magnitude of improvements and the certainty of evidence are inconsistent. This systematic review and meta-analysis aimed to evaluate the ...

ObjectivesArtificial intelligence (AI)-enabled digital stethoscopes combine phonocardiography and electrocardiography to support detection of cardiac rhythm and structural abnormalities. This study evaluated the feasibility and exploratory diagnostic performance of AI-guided cardiac auscultation during routine general practice consultations and home visits. MethodsIn this prospective feasibility study, 50 consecutive patients aged [≥]65 years underwent AI-assisted auscultation using the Eko ...

BACKGROUNDDilated cardiomyopathy (DCM) presents a highly heterogeneous spectrum, including a familial subset with elevated arrhythmic risk. Traditional demographic and imaging markers, such as late gadolinium enhancement, have been inadequate for identifying high-risk patients before arrhythmic events. Remodelling of the interventricular septum--central to ventricular mechanics and conduction--may offer improved risk stratification. OBJECTIVESTo identify differences in left ventricular (LV) mor...

Quantifying muscle health at scale has been limited by the difficulty of segmenting individual muscles on MRI. We developed an automated 3D deep-learning framework that segments 20 bilateral hip and thigh muscles from Dixon MRI, enabling muscle level quantification of volume and relative fat fraction (rFF). Applied to 10,840 baseline and 2,766 longitudinal UK Biobank scans, this framework supports population-scale phenotyping across demographic, metabolic and treatment exposures. Segmentation ac...