Correction: Exercise preconditioning alleviates photothrombotic ischemic stroke in mice by orchestrating neutrophils.

[This corrects the article DOI: 10.3389/fphys.2025.

Exercise preconditioning alleviates photothrombotic ischemic stroke in mice by orchestrating neutrophils.

Background: In ischemic stroke (IS) pathology, neutrophils are rapidly recruited to the infarcted brain and exacerbate tissue damage by releasing the amount of neutrophil extracellular traps (NETs). Previous studies have demonstrated that alleviating IS is associated with reduced accumulated neutrophils and NET levels. Notably, exercise preconditioning (EP) has the potential to modulate neutrophil function, thereby contributing to the amelioration of IS.

Identification of stroke biomarkers using proteomic profiling of extracellular vesicles derived from human blood: a preliminary study.

Introduction: Stroke is a devastating brain disease that causes extensive neurological impairment and high mortality. Rapid diagnosis and intervention of stroke are necessary to minimize neurological damage and improve recovery. Extracellular vesicles (EVs) have been identified as potential biomarkers for stroke, suggesting promising avenues for rapid diagnosis and prognostic assessments.

Refining the Martin-Hopkins method for estimating low-density lipoprotein cholesterol levels: Median versus optimal TG/VLDL-C ratio.

Background: Low-density lipoprotein cholesterol (LDL-C), a major modifiable risk factor for cardiovascular diseases, is typically calculated using the Friedewald formula when triglyceride (TG) levels are below 400 mg/dL. Recent studies have demonstrated the superior accuracy of the Martin-Hopkins method across diverse populations. While this method estimates very-low-density lipoprotein cholesterol (VLDL-C) using strata-specific median TG/VLDL-C ratios, its reliance on median statistics raises questions about whether these ratios are truly optimal.

Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation.

Accurate classification of biomedical signals is crucial for advancing non-invasive diagnostic methods, particularly for identifying gastrointestinal and related medical conditions where conventional techniques often fall short. An ensemble learning framework was developed by integrating Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to classify spectrogram images generated from percussion and palpation signals. The framework employs Short-Time Fourier Transform (STFT) to extract spectral and temporal information, enabling accurate signal processing and classification into distinct anatomical regions.

Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study.

Backgrounds And Objectives: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions.

Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach.

Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable device using single-lead electrocardiography (ECG) and impedance pneumography (IPG) for multi-stage sleep classification.

Towards early detection of chronic kidney disease based on gait patterns: IMU-based approach using neural networks.

The aging population has led to an increased prevalence of chronic kidney disease (CKD), associated with a higher incidence of gait disturbances and rise in fall rates. It is important that early detection and continuous monitoring of CKD to improve patient prognosis. Our study explores a non-clinical approach for detecting CKD by analyzing gait characteristics using inertial movement unit (IMU) sensors.

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) signals. We explore the application of ECG for not only providing cardiophysiological information but also for more extensive patient surveillance, including emergency recognition. Utilizing an end-to-end one-dimensional convolutional neural network (1D CNN), we analyzed wireless ECG data from 40 participants performing five common daily activities.

Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Objective: Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult.

Methods: We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types.

GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.

Background: Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development.

Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.

Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightweight nature and simplicity. Recently, HAR techniques leveraging wearable biometric signals, such as electrocardiography (ECG) and photoplethysmography (PPG), have been proposed using publicly available datasets.

The effect of voluntary exercise on light cycle stress-induced metabolic resistance.

Purpose: Disruption of circadian genes affects metabolic homeostasis. Regular exercise programs prevent metabolic dysfunction and alter circadian gene expression In this study, we investigated whether exercise affects light stress-induced circadian rhythm derangement and metabolic resistance.

Methods: A circadian rhythm derangement mouse model was designed by extending the light exposure by two hours (14 L/10 D) for three weeks.

Distinct effect of exercise modes on mood-related behavior in mice.

Exercise can afford several benefits to combat mood disorders in both rodents and humans. Engagement in various physical activities upregulates levels of neurotrophic factors in several brain regions and improves mental health. However, the type of exercise that regulates mood and the underlying mechanisms in the brain remain elusive.

A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia.

One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders.

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform.

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs.

Physical Telerehabilitation Improves Quality of Life in Patients with Multiple Sclerosis.

The purpose of this study was to investigate the effect of physical telerehabilitation on the quality of life (QOL) in patients with multiple sclerosis (PwMS) in a randomized controlled trial. PwMS in both groups received home-based individualized exercise plan based on their physical therapy exam. PwMS in the intervention group were guided by a telerehabilitation system in following their exercise program on a daily basis whereas PwMS in the control group received periodic newsletters.

Usability of Remote Assessment of Exercise Capacity for Pulmonary Telerehabilitation Program.

Pulmonary rehabilitation [PR] has been successfully carried out via telemedicine however initial patient assessment has been traditionally conducted in PR centers. The first step in PR is assessment of patient's exercise capacity which allows individualized prescription of safe and effective exercise program. With COVID-19 pandemics assessment of patients in PR centers has been limited resulting in significant reduction of patients undergoing life-saving PR.

Association Between System Usage Pattern and Impact of Web-Based Telerehabilitation in Patients with Multiple Sclerosis.

Patients with multiple sclerosis (PwMS) increasingly use online services for managing their healthcare. The objective of this study was to investigate web log data (weblogs) generated by PwMS in the process of web-based telerehabilitation and correlate them with rehabilitation progress. The weblogs from 17 patients (female: 15, male 2; mean age: 60.

Towards a Highly Usable, Mobile Electronic Platform for Patient Recruitment and Consent Management.

This study seeks to assess usability and acceptance of E-Consent on mobile devices such as tablet computers for collecting universal biobank consents. Usability inspection occurred via cognitive walkthroughs and heuristics evaluations, supplemented by surveys to capture health literacy, patient engagement, and other metrics. 17 patients of varied ages, backgrounds, and occupations participated in the study.

Relationship Between Exercise Duration in Multimodal Telerehabilitation and Quality of Sleep in Patients with Multiple Sclerosis.

The purpose of this study was to investigate the effect of a telerehabilitation system on the quality of sleep in patients with multiple sclerosis (PwMS). Fifteen females and two males (60.1 ± 11.

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit.

Importance: Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed.

Implant Failure Prediction Using Discriminant Analysis.

Electronic dental records (EDR) provide access to a vast repository of clinical information which may be used for analyzing dental care delivery. The goal of this study was identification of determinants of implant survival and development of implant failure prediction model using large data set of intact and failed implant parameters extracted from EDR. A retrospective analysis of 19 variables reflecting patient, surgeon and dental treatment characteristics of 800 dental implants was performed using discriminant analysis to develop a predictive model identifying potential implant failure based on characteristics routinely available in a clinical care setting.

Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor.

Wearable sweat sensors have enabled real-time monitoring of sweat profiles (sweat concentration versus time) and could enable monitoring of electrolyte loss during exercise or for individuals working in extreme environments. To assess the feasibility of using a wearable sweat chloride sensor for real-time monitoring of individuals during exercise, we recorded and analyzed the sweat profiles of 50 healthy subjects while spinning at 75 Watts for 1 hour. The measured sweat chloride concentrations were in the range from 2.