Search Results (2,272)

(1) Background: The naked mole-rat (Heterocephalus glaber) survives hypoxia–reoxygenation stresses by utilizing metabolic rate depression, achieved in part by downregulating nonessential genes and processes to conserve endogenous cellular resources and prevent buildup of toxic waste byproducts. Tight molecular control of protein degradation (specifically the ubiquitin–proteasome system) is a potent regulatory tool for maintaining muscle integrity during hypoxia, but how this system is regulated in the heart of hypoxia-tolerant species is poorly understood. (2) Methods: The protein expression levels of cullin-RING E3 ligases (specifically CRL4 architecture), deubiquitinating enzymes, and proteasomal activity were assayed in cardiac tissues from H. glaber exposed to 24 h of normoxia or hypoxia in vivo. (3) Results: Overall, the protein expression of E3 ligases decreased, whereas expression of deubiquitinating enzymes increased during hypoxia, all of which play roles in themes of oxidative stress, heightened DNA damage repair, and the HIF-1-VHL-NFκB axis. Proteasomal activity was elevated during hypoxia, which conceivably links to the oxidative stress theory of aging and longevity of H. glaber. (4) Conclusions: Taken together, our results expand current research into protein degradation and extreme environmental stress responses, with a specific focus on cardiac mechanisms related to oxidative stress resistance along the hypoxia-longevity axis. Full article

Microplastics (MPs) and antibiotics have emerged as contaminants of global concern, posing potential threats to ecosystem security and organismal health. To investigate the individual and combined toxicity of microplastics (PS-MPs) and sulfamethoxazole (SMX), we conducted a 120 h acute exposure experiment using embryo–larval zebrafish as a toxicological model. Our findings demonstrate that both PS-MPs and SMX can induce neurodevelopmental toxicity in embryo–larval zebrafish during embryonic development. Notably, PS-MPs and SMX exerted a significant synergistic effect. PS-MPs 1 µm in diameter were restricted to the chorion surface of pre-hatching zebrafish, whereas post-hatching, PS-MPs accumulated mainly in the gut and gills, with accumulation levels increasing progressively with exposure duration. Individual exposure to PS-MPs or SMX reduced spontaneous locomotion, decreased heart rate, and shortened body length in embryo–larval zebrafish. In addition to exacerbating these effects, coexposure further increased the incidence of malformations such as pericardial effusion and spinal curvature. PS-MPs and SMX significantly decreased the levels of dopamine (DA), serotonin (5-HT), and γ-aminobutyric acid (GABA) in zebrafish while also suppressing acetylcholinesterase (AChE) activity and increasing acetylcholine (ACh) levels. Moreover, upon coexposure at high concentrations, PS-MPs and SMX acted synergistically to reduce the levels of DA and GABA. The downregulation of key neurodevelopmental genes (elavl3, gap43, and syn2a) and related neurotransmitter pathway genes indicates that PS-MPs and SMX impaired structural development and functional regulation of the nervous system. An integrated biomarker response (IBR) index confirmed that PS-MPs and SMX significantly enhanced developmental neurotoxicity during early neurodevelopment in embryo–larval zebrafish through synergistic effects. Our study provides critical toxicological evidence for the scientific assessment of the ecological risks posed by microplastic–antibiotic cocontamination. Full article

Continuous heart rate monitoring is crucial for early cardiovascular disease detection. To overcome the discomfort and limitations of ECG in home settings, we propose a multimodal temporal fusion network (MM-TFNet) that integrates ballistocardiography (BCG) and photoplethysmography (PPG) signals. The network extracts temporal features from BCG and PPG signals through temporal convolutional networks (TCNs) and bidirectional long short-term memory networks (BiLSTMs), respectively, achieving cross-modal dynamic fusion at the feature level. First, bimodal features are projected into a unified dimensional space through fully connected layers. Subsequently, a cross-modal attention weight matrix is constructed for adaptive learning of the complementary correlation between BCG mechanical vibration and PPG volumetric flow features. Combined with dynamic focusing on key heartbeat waveforms through multi-head self-attention (MHSA), the model’s robustness under dynamic activity states is significantly enhanced. Experimental validation using a publicly available BCG-PPG-ECG simultaneous acquisition dataset comprising 40 subjects demonstrates that the model achieves excellent performance with a mean absolute error (MAE) of 0.88 BPM in heart rate prediction tasks, outperforming current mainstream deep learning methods. This study provides theoretical foundations and engineering guidance for developing contactless, low-power, edge-deployable home health monitoring systems, demonstrating the broad application potential of multimodal fusion methods in complex physiological signal analysis. Full article

Positive affect comprises distinct affective states that differ in arousal level, such as high-arousal positive affect (HAPA) and low-arousal positive affect (LAPA), which have been shown to be associated with different effects and effective contexts. In studies of positive affect, it is therefore important not only to assess overall positivity but also to distinguish between different types of positive affect. Existing assessments rely mainly on self-reports, which may be unreliable for individuals with limited self-report abilities. The aim of this study was to examine whether physiological indices can discriminate between HAPA and LAPA. Participants were presented with eight video stimuli designed to elicit either HAPA or LAPA, and self-report measures were used as manipulation checks to define the affective conditions, while heart rate variability (HRV) and electroencephalography (EEG) were recorded. HRV indices did not show significant differences between the two affective conditions. In contrast, analyses of EEG relative power revealed significant differences between the HAPA and LAPA conditions. These findings demonstrate that, under the present experimental conditions, physiological differences between low- and high-arousal positive affect can be captured in EEG signals using relative power, a simple and reproducible analytical index, whereas no such differences were observed in HRV indices. Full article

Background: Sepsis-associated liver injury (SALI) is a serious complication of sepsis that increases the risk of organ dysfunction and mortality; however, early identification of high-risk patients remains difficult due to nonspecific clinical features and complex pathophysiology. This study aimed to develop machine learning (ML) models to predict 28-day mortality in SALI patients within the first 24 h of intensive care unit (ICU) admission. Methods: A total of 1157 patients were included, comprising 826 from the MIMIC-IV (v2.2) database, 225 from MIMIC-III (v1.4), and 106 from eICU (v2.0). Data from MIMIC-IV were split into training and internal validation sets (7:3), while MIMIC-III and eICU served as external validation cohorts. Thirty clinically relevant features were selected. Eight ML models were evaluated using AUROC, accuracy, precision, recall, F1-score, and specificity. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) enhanced interpretability. Results: XGBoost model achieved the best performance, with an AUROC of 0.8556 (95% CI: 0.807–0.898), accuracy of 0.7702, recall of 0.8469, and specificity of 0.7200. SHAP identified lactate, blood urea nitrogen, heart rate, hemoglobin, and diastolic blood pressure as key predictors, while LIME provided patient-level interpretability. Conclusions: The XGBoost-based model may facilitate early mortality risk stratification and support clinical decision-making for SALI patients in ICU settings. Full article

Background: Patients with dental phobia frequently require intravenous sedation (IVS) to undergo dental treatment; however, some can gradually discontinue IVS through repeated clinical experiences. The physiological and psychological factors influencing successful IVS withdrawal remain unclear. This study aimed to compare physiological (sAA, HR) and subjective (VAS) measures between patients who discontinued IVS and those who remained dependent on IVS. Methods: This prospective cohort study included 51 patients with dental phobia treated under IVS. Participants were classified into a Non-Sedation Group (NSG; n = 25) and a Sedation-Dependent Group (SDG; n = 26) based on their ability to discontinue IVS during the course of treatment. Salivary alpha-amylase (sAA), heart rate (HR), and visual analog scale (VAS) scores for fear, tension, and anxiety were assessed at predefined time points from the waiting room to venous cannulation. Treatment satisfaction and expectations for future treatment were also evaluated. Results: sAA activity was significantly higher in the SDG than in the NSG at T0 and T1 (p < 0.05), indicating higher levels of selected physiological measures during anticipatory phases; however, the difference at T2 was not significant. HR differed significantly only in the waiting room, whereas no between-group differences were observed in self-reported VAS scores for fear, tension, or anxiety at any time point, indicating a dissociation between physiological and subjective stress measures. Treatment satisfaction and expectations for future treatment were significantly higher in the SDG. Conclusions: Patients who remained dependent on IVS showed higher levels in selected physiological measures at the group level during anticipatory stages, whereas no corresponding differences were observed in self-reported subjective measures. These findings are exploratory and descriptive in nature and do not imply predictive or causal relationships. Full article

Anxiety is a common psychological challenge among athletes, particularly in response to intense training sessions. This randomized crossover study investigated the immediate effects of a single session of focused attention meditation on anxiety, autonomic responses, and performance during high-intensity intermittent training (HIIT) in twenty-six university-level speed skaters. Participants completed three pre-exercise interventions (focused attention meditation, controlled breathing, and random thinking) on separate occasions in a randomized order. Following each intervention, participants performed a leg cycling-based HIIT protocol consisting of 20 s of maximal effort work followed by 10 s of passive rest, repeated for 8 sets using a cycling ergometer. State anxiety was assessed using the State–Trait Anxiety Inventory, and mood disturbance was evaluated using the Profile of Mood States. Autonomic and physiological responses were assessed via heart rate variability (coefficient of variation), oxygen uptake, and power output, measured before and after the intervention and the HIIT bout. Focused attention meditation significantly reduced state anxiety compared with random thinking (ΔSTAI: −5.0 [6.0] vs. −1.0 [4.3]; p < 0.05, effect size = 0.527), whereas controlled breathing primarily influenced heart rate variability (CV: 0.10 [0.11] vs. 0.07 [0.03]; p = 0.041, effect size = 0.736). No significant differences were observed among conditions in mean power output or fatigue index during HIIT. These findings suggest that single-session focused attention meditation may serve as a practical pre-exercise strategy for an immediate reduction in state anxiety, without compromising subsequent high-intensity exercise performance. Full article

This research explores the use of physiological signals derived from heart activity to assess mental effort during flight-related tasks. Data were collected through wearable sensors during simulations with varying cognitive demands. Specific indicators related to heart rate variability (HRV) were extracted and tested in different combinations to identify those most relevant for distinguishing levels of mental workload (WL). A Random Forest (RF) ensemble method is applied to classify two conditions, and its performance is examined under various settings, including model complexity and data partitioning strategies. Results showed that certain feature pairs significantly enhanced classification accuracy. The best features settings obtained from the RF are then used to train the other two decision trees-based classifiers, namely the AdaBoost and the XGBoost. Moreover, the decision trees models output is compared with predictions from a Kriging spatial interpolation technique, showing superior results in terms of reliability and consistency. This study highlights the potential of using heart-based physiological data and advanced classification techniques for developing intelligent support systems in aviation. Full article

Diabetes mellitus (DM) is a chronic disease characterised by chronic hyperglycaemia due to a defect in the production of or cell insensitivity to insulin. If left untreated, it might result in severe side effects such retinal, nephropathy, neuropathy, and cardiovascular disease. Extensive research has been made to develop more effective and less expensive alternatives to existing treatment regimes. This review aims to evaluate research done thus far to test the effect of Saccharomyces boulardii (S. boulardii or Sb) in treating DM and its complications. Searches were conducted using Scopus, Web of Science, PubMed and Google Scholar on 26 July 2025. Overall, 227 articles were identified, and 5 fulfilled the inclusion criteria. Results extracted were from two models of diabetes (type 1 and 2) and two strains of Sb. In type 1 diabetes models, a significant reduction in glycaemia was observed, while in type 2 diabetes models, a non-significant effect was noted, depending on the strain used. Furthermore, an improvement in cardiac function was observed through reduced heart rate variability, a decrease in blood pressure, an increase in C-peptide and hepatic glycogen stores, enhanced liver healing, a nephroprotective effect, as well as a reduction in oxidative stress, blood triglyceride levels, and the inflammatory response. Administration of Sb induced positive modulation of the intestinal microbiota, with a decrease in pathobionts in the stools. Overall, the few studies evaluated indicate that the use of Sb appears to be a promising approach to improve the management of diabetes and its associated metabolic and related complications. The protocol of this review is registered in PROSPERO under ID CRD420251012919. Full article

This pilot study investigated multimodal physiological monitoring using minimally invasive and wearable sensors across two experimental settings. Experiment 1 involved five healthy adults (1 female) who simultaneously wore an interstitial fluid glucose (ISFG) sensor and a ring-type wearable device during sleep (00:00–06:00). Time-series analyses revealed that ISFG levels decreased during sleep in four of the five participants. ISFG values were significantly lower in the latter half of the sleep period compared with the first half (0–3 h vs. 3–6 h, p = 0.01, d = 2.056). Four participants also exhibited a mild reduction in SpO₂ between 03:00–04:00. These results suggest that nocturnal ISFG decline may be associated with subtle oxygen-saturation dynamics. Experiment 2 examined whether wearable sensors can detect physiological changes across meal-related phases. Nine male participants were monitored for heart rate (HR) and skin temperature during three periods: pre-meal (Phase 1: 09:00–09:30), during meal consumption (Phase 2: 12:30–13:00), and post-meal (Phase 3: 13:00–13:30). A paired comparison demonstrated a significant difference in median HR between Phase 1 and Phase 2 (p = 0.029, d = 0.812), indicating a large effect size. In contrast, HR–temperature correlation was weak and not statistically significant (Pearson r = 0.067, p = 0.298). Together, these findings demonstrate that multimodal wearable sensing can capture both nocturnal glucose fluctuations and meal-induced cardiovascular changes. This integrative approach may support real-time physiological risk assessment and future development of remote healthcare applications. Full article

Background: This study aimed to provide reference values for estimating training intensities in long-distance runners based on progressive incremental tests, considering differences related to sex and performance level. Methods: A total of 1411 endurance-trained runners (819 men and 592 women) completed a standardized treadmill protocol with gas exchange analysis to determine ventilatory thresholds and peak oxygen consumption (VO_2peak). Heart rate (HR) and running speed at each threshold were expressed relative to their peak values. Results: HR at second ventilatory threshold (VT2) occurred at 93.5 ± 2.5% of HR peak, and HR at first ventilatory threshold at 85.1 ± 4.6%. The relative running speeds at VT2 and VT1 corresponded to 87.6 ± 3.9% and 73.9 ± 5.5% of the speed at VO_2peak, respectively. In men, beginners exhibited higher relative HR and VO₂ values at the ventilatory thresholds than elite runners. In contrast, women displayed higher and more stable relative values across performance levels. Conclusions: These findings establish precise, evidence-based reference ranges derived from a large cohort of runners and highlight the need to consider sex and performance level when estimating exercise intensities. Individualized physiological assessment remains essential for accurate training prescription and performance optimization. Full article

During exercise, increased oxygen consumption results in elevated production of reactive oxygen species (ROS). If the antioxidant system is unable to counteract this surge in ROS, oxidative stress occurs. Physical activity modulates both the generation and clearance of ROS through dynamic interactions between metabolic and antioxidant systems, and also influences the oxidative burst activity of phagocytes, a key component of the innate immune response. To investigate the acute physiological responses to high-intensity interval training (HIIT), we assessed the effects of a single HIIT session on oxidative stress markers and the oxidative burst activity of phagocytes in young professional athletes and non-athlete individuals. Blood samples were collected before and after a HIIT session from eleven male athletes (mean age: 22.1 ± 4.5 years) and ten male non-athlete university students (mean age: 21.6 ± 2.3 years). Participants performed a single treadmill HIIT session of ten 45-s intervals at 75–85% of heart rate reserve, separated by 45-s low-intensity recovery periods, with target intensities individualized using the Karvonen formula. Total antioxidant capacity, activities of catalase, superoxide dismutase and glutathione peroxidase enzymes, total serum nitrite/nitrate levels, lipid peroxidation products, and oxidative burst activity of phagocytes were evaluated before and after exercise. In athletes, a significant increase was observed in the activity of superoxide dismutase (from a median of 2.09 to 2.21 U/mL; p = 0.037) and catalase (from a median of 32.94 to 45.45 nmol/min/mL; p = 0.034) after exercise, whereas no significant changes were found in the control group. Total serum nitrite/nitrate levels significantly increased in both groups after exercise (athletes: from a median of 8.70 to 9.95 µM; p = 0.029; controls: from a median of 10.20 to 11.50 µM; p = 0.016). Oxidative burst capacity of peripheral blood phagocytes was significantly higher in athletes both before (median: 10,422 vs. 6766; p = 0.029) and after (median: 9365 vs. 7370; p = 0.047) the HIIT session compared to controls. Our findings demonstrate that training status markedly influences oxidative stress responses, with athletes exhibiting more effective long-term antioxidant adaptations. These results emphasize the necessity of tailoring exercise regimens to baseline fitness levels in order to optimize oxidative stress management across different populations. Full article

Vehicular Ad Hoc Networks (VANETs) enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for enhancing road safety. However, reliable driver stress assessment remains challenging due to noisy sensing, inter-driver variability, and context dynamics. This paper proposes a Fuzzy-based Driver Stress Detection System (FDSDS) that employs an Interval Type-2 Fuzzy Logic System (IT2FLS) to model uncertainty. The FDSDS considers four complementary inputs—Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Steering Angle Variation (SAV), and Traffic Density (TD)—to estimate Driver Stress Level (DSL). Extensive simulations (14,641 test points) show monotonic associations between DSL and the inputs, which reveal that physiological indicators dominate average influence (finite-difference sensitivity: GSR 0.357, SAV 0.239, TD 0.239, HRV 0.235). Under severe physiological conditions (HRV = 0.1, GSR = 0.9), the system consistently outputs high stress (mean DSL = 0.813; range 0.622–0.958), while favorable physiological conditions (HRV = 0.9, GSR = 0.1) yield low stress even in challenging traffic (range 0.044–0.512). The IT2FLS uncertainty bands widen for intermediate conditions, aligning with the inherent ambiguity of moderate stress states. These results indicate that combining physiological, behavioral, and environmental factors with IT2FLS yields interpreted, uncertainty-aware stress estimates suitable for real-time VANET applications. Full article

The REPRIEVE Trial recently reported high rates of sudden cardiac death (SCD) middle-aged people living with HIV-1 infection (PWH) using the WHO/NIH-recommended two nucleoside reverse transcriptase inhibitors (NRTIs)/one integrase strand inhibitor (INSTI) regimen to manage HIV-1 viremia. To date, clinically relevant animal models to delineate underlying causes for this remain limited. Here, we assessed if HIV-1-infected NOD.Cg-Prkdc^scidIl2rg^tm1Wjl/SzJ humanized mice (Hu-mice) treated with the WHO/NIH-recommended antiretroviral regimen, dolutegravir (DTG, INSTI)/tenofovir disoproxil fumarate (TDF, NRTIs)/emtricitabine (FTC, NRTIs), can recapitulate abnormalities in the ECG and subclinical structural heart disease that serve as harbingers of SCD in middle-aged PWH. HIV-1-infected and uninfected Hu-mice served as controls. After one month of infection (HIV-1_ADA), ECG intervals/segments were significantly altered. ECG changes progressively worsened as the duration of untreated infection increased. Treating HIV-1-infected animals with the DTG/TDF/FTC for eight weeks, starting four weeks after infection, prevented worsening, but did not restore ECG intervals/segments to those before infection. In hearts from DTG/TDF/FTC-treated animals, steady-state levels of the sarco-(endo) plasmic reticulum Ca²⁺ ATPase (SERCA2) were reduced by 35%. Steady-state levels of type 2 ryanodine receptor (RyR2) did not change, but its phosphorylation status at Ser2808 was 2-fold higher than that of uninfected controls, indicative of a gain-of-function. The density of perfused micro vessels and fibrosis in hearts of DTG/TDF/FTC-treated animals was not significantly different from that of HIV-1-infected and uninfected Hu-mice. These data show for the first time that HIV-1 infection is triggering abnormalities in the ECG of Hu-mice, and changes in ECG persisted with DTG/TDF/FTC treatment, independent of ischemia and/or fibrosis. They also indicate that chronic DTG/TDF/FTC treatment did not worsen ECG changes, including the QT interval. Since phosphorylation of RyR2 at Ser2808 occurs via β-adrenergic activation of protein kinase A, these new data also suggest that chronic hyperadrenergic activity may be increasing the risk of SCD via Ca²⁺ leak through RyR2. Full article

Fatigue is a multifactorial phenomenon affecting both physical and psychological performance, particularly in high-stress occupations. Although wearable sensors enable continuous monitoring, conventional machine-learning (ML) models can produce unstable, weakly calibrated, and opaque predictions in real-world settings. To improve reliability and interpretability, we developed a selective Retrieval-Augmented Generation (RAG)–enhanced hybrid ML–LLM framework that integrates the efficiency of ML with the reasoning capability of large language models (LLMs). Using wearable and ecological momentary assessment data from 297 emergency responders (9543 seven-day windows), logistic regression, XGBoost, and LSTM models were trained to classify fatigue levels dichotomized by the median of daily tiredness scores. The LLM was selectively activated only for borderline ML outputs (0.45 ≤ p ≤ 0.55), using symbolic rules and retrieved analog examples. In the uncertainty region, performance improved from 0.556/0.684/0.635/0.659 to 0.617/0.703/0.748/0.725 (accuracy/precision/recall/F1). On the full test set, performance similarly improved from 0.707/0.739/0.918/0.819 to 0.718/0.741/0.937/0.827, with gains confirmed by McNemar’s paired comparison test (p < 0.05). SHAP-based ML interpretation and LLM reasoning analyses independently identified short-term sleep duration and heart-rate variability as dominant predictors, providing transparent explanations for model behavior. This framework enhances classification robustness, interpretability, and efficiency, offering a scalable solution for real-world fatigue monitoring. Full article