Frontiers in Physiology最新文献

Objectives: This meta-analysis aimed to synthesize evidence on the effects of low-volume high-intensity interval training (LV-HIIT) on body composition and cardiometabolic health in overweight and obese children and adolescents. Specifically, we sought to: (1) quantify the effects of LV-HIIT versus non-exercise controls; (2) directly compare LV-HIIT with moderate-intensity continuous training (MICT); (3) identify participant- and program-level moderators prespecified subgroup and meta-regression analyses, to inform time-efficient pediatric exercise prescriptions.

Methods: Six databases (PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, and EBSCO) were systematically searched. Randomized controlled trials comparing LV-HIIT with either MICT or inactive controls in participants aged 7-16 years with overweight or obesity were included. Using random-effects models to calculate standardized mean differences with 95% confidence intervals Subgroup and meta-regression analyses were conducted to identify potential moderators.

Results: Twelve trials involving 609 participants were included. Compared with non-exercise controls, LV-HIIT reduced BMI (SMD = -1.15, 95% CI [-1.68, -0.61]), body fat percentage (SMD = -0.84, 95% CI [-1.09, -0.59]), waist circumference (SMD = -0.62, 95% CI [-0.93, -0.32]), systolic blood pressure (SMD = -0.80, 95% CI [-1.11, -0.49]), diastolic blood pressure (SMD = -0.47, 95% CI [-0.77, -0.17]), while increasing VO₂max (SMD = 2.10, 95% CI [1.32, 2.87]). Compared with MICT, LV-HIIT showed greater improvements in BMI (SMD = -0.27, 95% CI [-0.49, -0.04]), systolic blood pressure (SMD = -0.25, 95% CI [-0.55, -0.05]), and VO₂max (SMD = 0.76, 95% CI [0.39, 1.13]), while showing comparable effectiveness in reducing body fat percentage (SMD = -0.06, 95% CI [-0.35, 0.22]) and waist circumference (SMD = -0.37, 95% CI [-0.75, 0.01]). Subgroup analyses revealed greater BMI reductions with LV-HIIT among participants who were overweight at baseline and among males. Meta-regression with baseline adiposity as a continuous moderator indicated participants with lower baseline BMI may experience greater BMI reductions after LV-HIIT.

Conclusion: LV-HIIT significantly improves body composition, cardiometabolic health, and cardiorespiratory fitness in overweight and obese children and adolescents, offering comparable or superior benefits to traditional MICT in approximately half the time.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420250655540.

Background: Countermovement jump performance is the gold standard for assessing lower limb power, and even minor improvements can significantly enhance performance in sports such as basketball and volleyball. Post-activation potentiation (PAP) and post-activation performance enhancement (PAPE) are key mechanisms for acute performance enhancement, but the relative efficacy of different resistance training protocols (such as squats, deadlifts, flywheel training, and leg presses) remains unclear.

Objective: To quantify and rank the acute potentiating effects of four resistance training protocols (conventional squats, flywheel squats, deadlifts, and leg presses) on countermovement jump performance through a network meta-analysis, and to explore the optimal load intensity and rest interval for the best resistance training modality.

Methods: Six major databases were searched (up to May 2025), and 51 randomized controlled trials (involving 886 athletes) were included. A network meta-analysis within a frequentist framework was conducted, with standardized mean differences (SMD) and surface under the cumulative ranking curve (SUCRA) used to assess the efficacy ranking. Subgroup analyses were performed based on load intensity (≥85% 1RM for high intensity, <85% 1RM for moderate-low intensity) and rest interval (short: 0-4 min; medium: 5-7 min; long: ≥8 min).

Results: A total of 51 studies were included. Flywheel training had the highest SUCRA value (95.8%), with a significant improvement in countermovement jump performance (SMD = 0.67, 95% CI: 0.22-1.12). This was followed by deadlifts (SUCRA = 62.4%, SMD = 0.28, 95% CI: 0.22%-0.78%) and back squats (SUCRA = 57.6%, SMD = 0.23, 95% CI: 0.03%-0.48%). Leg presses may have a negative impact on countermovement jump performance (SUCRA = 9.4%, SMD = -0.36, 95% CI: 1.18%-0.45%). For flywheel training, the best results were observed with moderate intensity (SMD = 0.92, 95% CI: 0.05%-1.80%) and medium rest intervals (SMD = 0.96, 95% CI: 0.04%-1.87%).

Conclusion: Based on evidence of high quality level, Flywheel training is the best way to enhance acute countermovement jump performance. Regarding training parameters, while subgroup analyses point towards moderate intensity and 5-7 min of rest, these should be viewed as preliminary indicators due to wide confidence intervals and residual heterogeneity. While the conclusions for deadlifts and squats are based on less conclusive evidence, they are recommended as alternative options when a flywheel device is not available. If conditions do not permit, deadlifts can be considered as the next best option. However, the current evidence is insufficient to support the positive role of leg press in enhancing acute jumping ability.

Diabetic cardiomyopathy (DCM) is a serious complication of end-stage diabetes that manifests as cardiac hypertrophy and heart failure. The present study performed a bioinformatics analysis to predict possible targets for aerobic exercise to improve DCM, and animal experiments were conducted to detect the relevant mechanisms. Oxidative stress (OS)-DCM-trained differentially expressed genes (DEGs) were retrieved from the GeneCards database and a Gene Expression Omnibus microarray dataset. Subsequently, a protein-protein interaction network was constructed to screen the hub genes of the OS-DCM-trained DEGs. In addition, a model of type 2 diabetes was established using streptozotocin and a high-fat diet. Rats were divided into the control, DCM and DCM plus exercise (DCME) groups. The DCME group underwent 8 weeks of moderate-intensity treadmill training. Assessment of cardiac function, myocardial enzymes and OS-related indicators in each group. Compared with the control group, the levels of BNP, CK-MB, c-TnT, LDH, MDA, LVEF, LVIDd, and LVIDs in the DCM group were significantly increased (P < 0.05), while SOD, GSH, and LVFS were significantly decreased (P < 0.05); The above indicators were significantly improved in DCME group rats (P < 0.05). In addition, the expression levels of target genes predicted to be associated with the aerobic exercise-induced improvement of DCM were detected and western blotting was used to determine the relevant signaling pathways. Bioinformatics analysis identified nine hub genes, which, according to Kyoto Encyclopedia of Genes and Genomes enrichment analysis, were mainly involved in "IL-17 signaling pathway," "TNF signaling pathway," "apoptosis" and "necroptosis." Aerobic exercise improved the heart function and myocardial enzymes of the rats in the DCM group, reduced myocardial damage, and inhibited fibrosis and OS. Detection of the nine core genes revealed that only protein C (PROC) met the predicted trend; PROC expression was lower in the DCM group than that in the control group and was higher in the DCME group than that in the DCM group (P < 0.05). Further confirmation using western blotting suggested that aerobic exercise may improve DCM by activating the PROC/proteinase-activated receptor 1 (PAR1)/nuclear factor (erythroid-derived 2)-like 2 (Nrf2)/heme oxygenase-1 (HO-1) signaling pathway. In conclusion, aerobic exercise may mitigate DCM by activating the PROC/PAR1/Nrf2/HO-1 signaling pathway. These findings could pave the way for further investigations into how exercise might regulate OS and influences DCM progression, providing novel insights into its diagnosis and prognosis.

Pulmonary embolism (PE) is a common and potentially fatal obstructive disease of the pulmonary arteries; early diagnosis and continuous monitoring are particularly critical in critically ill patients. Electrical impedance tomography (EIT), a noninvasive and radiation-free imaging modality that enables real-time bedside monitoring, offers a promising approach for adjunctive diagnosis and perfusion assessment of PE, especially in patients who cannot undergo computed tomography pulmonary angiography (CTPA) due to instability or other contraindications. Building upon an overview of EIT imaging principles and recent advances in pulmonary perfusion monitoring, this review concentrates on the bedside application of EIT and the clinical value of EIT in bedside assessment of PE. Unlike prior research, this study proposes an EIT perfusion imaging strategy using a hypertonic saline bolus for the diagnosis of PE and compares it with bedside monitoring based on cardiac impedance signals. Additionally, we assess the current clinical evidence according to GRADE standards and identify its existing limitations. Finally, we further discuss the key challenges hindering clinical translation of EIT and outline future directions. This review aims to provide clinicians and researchers with a reference to facilitate broader adoption of EIT in the bedside monitoring of PE.

Introduction: Bipolar disorder (BD) is a severe psychiatric disorder characterized by shifting of mood patterns from manic to depressive episodes. The molecular mechanisms underlying BD have not been fully elucidated, and research into biomarkers is important for prevention and early intervention. The Na⁺, K⁺-ATPase is a metalloprotein that interacts with many chemical elements. It was demonstrated that the interactions of Na⁺, K⁺-ATPase with endogenous cardiac steroids is involved in BD. It was hypothesized that these interactions are mimicked by chemical elements which may participate in BD etiology. We have recently demonstrated that the concentration of Aluminum (Al), Boron (B), Cupper (Cu), Potassium (K), Magnesium (Mg) and Vanadium (V) were significantly lower in the pre-frontal cortex of individuals with BD compared with controls. We hypothesized that differences in the levels of chemical elements between BD and healthy controls would also be reflected in scalp hair.

Methods: To test this hypothesis, the levels of 25 chemical elements were determined by Inductively coupled plasma mass spectrometry (ICP-MS) in the scalp hair of 30 individuals with BD and 30 sex- and age-matched controls.

Results: We found that the levels of Al, Cu, Nickel (Ni) and Thallium (Tl) are elevated in the hair of BD patients compared to controls. In addition, the concentrations of Ni levels in hair samples were correlated with the severity of the mental illness as quantified by the Global Assessment of Functioning Scale.

Conclusion: Although interpretations are tentative due to the limited sample size, our results suggest that changes in chemical elements may be involved either in the etiology of BD or altered due to the disease progression, which needs to be clarified further in larger independent samples.

Scanpath analysis provides a powerful window into visual behavior by jointly capturing the spatial organization and temporal dynamics of gaze. By linking perception, cognition, and oculomotor control, scanpaths offer rich insights into how individuals explore visual scenes and accomplish task goals. Despite decades of research, however, the field remains methodologically fragmented, with a wide diversity of representations and comparison metrics that complicate interpretation and methodological choice. This article reviews computational approaches for the characterization and comparison of scanpaths, with an explicit focus on their underlying assumptions, interpretability, and practical implications. We first survey representations and metrics designed to describe individual scanpaths, ranging from geometric descriptors and spatial density representations to more advanced approaches such as attention maps, recurrence quantification analysis, and symbolic string encodings that capture temporal regularities and structural patterns. We then review methods for comparing scanpaths across observers, stimuli, or tasks, including point-mapping metrics, elastic alignment techniques, string-edit distances, saliency-based measures, and hybrid approaches integrating spatial and temporal information. Across these methods, we highlight their respective strengths, limitations, and sensitivities to design choices such as discretization, spatial resolution, and temporal weighting. Rather than promoting a single optimal metric, this review emphasizes scanpath analysis as a family of complementary tools whose relevance depends on the research question and experimental context. Overall, this work aims to provide a unified conceptual framework to guide methodological selection, foster reproducibility, and support the meaningful interpretation of gaze dynamics across disciplines.

Background: Plyometric training may enhance upper-limb explosive performance and stroke velocity in youth badminton players while contributing to mitigate injury risk, yet the influence of biological maturation on these adaptations remains unclear.

Purpose: To compare the effects of upper-limb plyometric training (PLYOgen), technical plyometric training integrating badminton stroke mechanics (PLYObad), and regular training on upper-limb strength, plyometric performance, and smash speed while accounting for maturity offset.

Methods: Sixty-two male players (12-14 years) were randomized to PLYOgen, PLYObad, or control. All groups continued their usual badminton practice (2-3 sessions/week; 75-90 min/session). Over 6 weeks, only the intervention groups completed an additional supervised plyometric session once per week (25-30 min; 72 explosive actions), whereas the control group performed no additional plyometric/strength sessions. Repeated-measures ANCOVA with maturity offset as covariate assessed pre-post changes in overhead medicine ball throw, seated chest pass, plyometric push-up height, and smash speed.

Results: Significant time effects were observed for all outcomes (p < 0.001; η²p = 0.737-0.954). Time × maturity-offset interactions were significant for overhead throw (p < 0.001; η²p = 0.571), chest pass (p < 0.001; η²p = 0.482), push-up height (p = 0.006; η²p = 0.122), and smash speed (p < 0.001; η²p = 0.360), indicating that players with higher maturity offset (closer to or beyond PHV) tended to show larger pre-post improvements. Time × group interactions were also significant for overhead throw (p < 0.001; η²p = 0.918), chest pass (p < 0.001; η²p = 0.840), push-up height (p < 0.001; η²p = 0.718), and smash speed (p < 0.001; η²p = 0.950). Post-hoc analysis showed PLYOgen and PLYObad improved overhead throw and smash speed more than control, with PLYOgen also presenting greater values than control in push-up height.

Conclusion: Both plyometric approaches enhanced upper-limb explosive performance, with biological maturation significantly moderating training responsiveness. This should be considered when modifying youth training programs to manage injury risk factors and to ensure that training practices are appropriately aligned with the players' developmental level.

Accurate prediction of asymptomatic small abdominal aortic aneurysm (AAA) growth is crucial for risk stratification and personalized surveillance. This study developed an end-to-end deep learning framework to predict rapid expansion (≥0.5 cm/6 months) using computed tomography angiography (CTA) images from 81 asymptomatic patients with small AAA (30 rapid-growth and 51 stable patients). The pipeline integrated three core components: a ResNet50 classifier for identifying aortic images (99.86% accuracy, 99.91% F1-score), a YOLOv11 detector for localizing aneurysms (precision-recall: 0.902), and a MedMamba-based feature fusion model that combined imaging features with clinical metadata via multi-head self-attention. Model robustness was ensured through stratified 5-fold cross-validation and comprehensive data augmentation. The fusion model achieved a predictive accuracy of 98.75% and an F1-score of 97.78, outperforming seven classical deep learning backbones. Furthermore, explainability analyses confirmed the model's reliance on established clinical risk factors and highlighted biologically plausible imaging regions for prediction. The proposed ResNet50-YOLOv11-MedMamba framework demonstrates the feasibility of automating AAA growth prediction directly from CTA and shows promising potential to enhance clinical decision-making.