Search Results (3,430)

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background: Magnesium (Mg) is an essential mineral that plays a vital role in various physiological processes, including enzyme regulation, neuromuscular function, and cardiovascular health. Dysmagnesemia has been associated with arrhythmias, neuromuscular dysfunction, and poor outcomes in intensive care unit (ICU) settings, representing diagnostic and therapeutic challenges. However, the relationship between dysmagnesemia and health outcomes in the ICU remains inadequately defined. Aim/Objective: This study aimed to assess the prevalence of dysmagnesemia and evaluate the correlation between total (tMg) and ionized magnesium (iMg) levels in a cohort of ICU and high dependency unit (HDU) patients. It also sought to evaluate patient characteristics and relevant health outcomes by comparing both concentrations of iMg and tMg. Methods: This prospective study was conducted among adult patients admitted to the ICU and the high dependency unit (HDU). Results: Among the 134 included patients, the median age was 63.5 years (IQR: 52.0–77.0). The majority, 91.0%, required mechanical ventilation. Additionally, 50.0% were diagnosed with diabetes, 28.4% had chronic kidney disease, and proton pump inhibitors (PPIs) were administered to 67.2% of the patients. The prevalence of hypomagnesemia, as measured by iMg, was 6.7%, while hypermagnesemia was at 39.6%. When measured by tMg, hypomagnesemia and hypermagnesemia were observed at rates of 14.9% and 22.4%, respectively. The iMg measurements showed an association between the incidence of atrial fibrillation and hypomagnesemia (p = 0.015), whereas tMg measurements linked hypomagnesemia with longer hospital stays. Notably, only a few patients identified with iMg-measured hypomagnesemia received magnesium replacement during their ICU stay. Conclusions: Dysmagnesemia is prevalent among critically ill patients, with discordance between iMg and tMg measurements. iMg appears more sensitive in detecting arrhythmia risk, while tMg correlates with length of stay. These findings support the need for larger studies and suggest considering iMg in magnesium monitoring and replacement strategies. Full article

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article

We present a novel method to estimate blood cortisol concentration from sweat cortisol measurements, incorporating a kinetic model to simulate cortisol transport dynamics. Cortisol dysregulation is observed in conditions like Cushing’s syndrome, characterized by excessive cortisol production, and stress-related disorders, which can lead to metabolic disturbances, anxiety, and impaired overall health. Sweat-sensing technology offers a non-invasive and continuous alternative to blood sampling. However, the limited research exploring the sweat–blood cortisol relationship in patients shows a moderate correlation ($R<0.6$), hindering its clinical application for long-term monitoring. In this paper, we propose a novel kinetic model describing cortisol transport from blood to sweat. The model was validated using data from 44 patients before and after cardiac surgery. A high Pearson correlation coefficient of 0.95 (95% CI: 0.92–0.97) was observed between our model’s estimated and experimental blood cortisol concentrations. Moreover, the method enables personalized estimation of physiological parameters, accurately reflecting patients’ status under varying clinical conditions. The method paves the way for the clinical application of long-term, non-invasive monitoring of cortisol using sweat-sensing technology. Enabling the personalized estimation of physiological parameters could potentially support clinical decision-making, helping doctors diagnose and monitor patients with health conditions involving cortisol dysregulation. Full article

Background/Objectives: This study investigates the utility of multiparametric PET/MRI in delineating changes in physiologically distinct intratumoral habitats during trastuzumab-induced alterations in a preclinical HER2+ breast cancer model. Methods: By integrating diffusion-weighted MRI, dynamic contrast-enhanced MRI, [¹⁸F]Fluorodeoxyglucose- and [¹⁸F]Fluorothymidine-PET, voxel-wise parametric maps were generated capturing cellular density, vascularity, metabolism, and proliferation. BT-474 tumor-bearing mice have high expression of HER2 and, in response to trastuzumab, an anti-HER2 antibody, effectively show changes in proliferation and tumor microenvironment alterations that result in decreases in tumor volume through time. Results: Single imaging metrics and changes in metrics were incapable of identifying treatment-induced alterations early in the course of therapy (day 4) prior to changes in tumor volume. Hierarchical clustering identified five distinct tumor habitats, which enabled longitudinal assessment of early treatment response. Tumor habitats were defined based on imaging metrics related to biology and categorized as highly vascular (HV), hypoxic responding (HRSP), transitional zone (TZ), active tumor (ATMR) and responding (RSP). The HRSP cluster volume significantly decreased in trastuzumab-treated tumors compared to controls by day 4 (p = 0.015). The volume of ATMR cluster was significantly different at baseline between cohorts (p = 0.03). The TZ cluster, indicative of regions transitioning more to necrosis, significantly decreased in treated tumors (p = 0.031), suggesting regions had already transitioned. Multiparametric image clustering showed a significant positive linear correlation with histological multiparametric mapping, with R² values of 0.56 (HRSP, p = 0.013, 0.64 (ATMR, p = 0.0055), and 0.49 (responding cluster, p = 0.024), confirming the biological relevance of imaging-derived clusters. Conclusions: These findings highlight the potential utility of multiparametric PET/MRI to capture biological alterations prior to any single imaging metric which has potential for better understanding longitudinal changes in biology, stratifying tumors based on those changes, optimizing therapeutic monitoring and advancing precision oncology. Full article

Smart glasses are promising alternatives for the continuous, unobtrusive monitoring of heart rate (HR). This study validates HR estimates obtained with the “Essilor Connected Glasses” (SmartEW) during sedentary activities. Thirty participants wore the SmartEW, equipped with an IMU sensor for HR estimation, a commercial smartwatch (Garmin Venu 3), and an ECG device (Movesense Flash). The protocol included six static tasks performed under controlled laboratory conditions. The SmartEW algorithm analyzed 22.5 s signal windows using spectral analysis to estimate HR and provide a quality index (QI). Statistical analyses assessed agreement with ECG and the impact of QI on HR accuracy. SmartEW showed high agreement with ECG, especially with QI threshold equal to 70, as a trade-off between accuracy, low error, and acceptable data coverage (80%). Correlation for QI ≥ 70 was high across all the experimental phases (r² up to 0.96), and the accuracy within ±5 bpm reached 95%. QI ≥ 70 also allowed biases to decrease (e.g., from −1.83 to −0.19 bpm while standing), with narrower limits of agreement, compared to ECG. SmartEW showed promising HR accuracy across sedentary activities, yielding high correlation and strong agreement with ECG and Garmin. SmartEW appears suitable for HR monitoring in static conditions, particularly when data quality is ensured. Full article

Basketball referees play a crucial role in game management, yet the physical and physiological demands placed on them during a game remain understudied. This study analyzed the workload of 35 group 1 referees during a U-18 Spanish championship, examining the effects of refereeing technique (two referees vs. three referees) and competition sex (male vs. female) across game quarters. Physical and physiological demands were measured using inertial devices and heart rate monitors during 37 matches (18 men’s and 19 women’s). The results revealed that 2-referee teams experienced significantly greater physical demands, covering approximately 25% more total distance and demonstrating higher values in high-intensity running compared to 3-referee teams. Female competition elicited higher demands in specific variables, particularly in the distance covered above 16 km/h and average speed. Analysis across quarters showed distinct temporal patterns, with the first and third quarters presenting the highest demands, especially for 2-referee teams. These findings suggest that basketball referees’ physical preparation should be tailored to the officiating technique and competition sex, with a particular emphasis on developing specific conditioning programs for the 2-referee technique and implementing targeted recovery strategies between quarters to maintain optimal performance throughout the game. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

Abies nebrodensis L. is a critically endangered conifer endemic to Sicily (Italy). Its residual population is confined to the Madonie mountain range under challenging climatological conditions. Despite the good adaptation shown by the relict population to the environmental conditions occurring in its habitat, Abies nebrodensis is subject to a series of threats, including climate change. Effective conservation strategies require reliable and versatile methods for monitoring its health status. Combining high-resolution remote sensing data with reanalysis of climatological datasets, this study aimed to identify correlations between vegetation indices (NDVI, GreenDVI, and EVI) and key climatological variables (temperature and precipitation) using advanced machine learning techniques. High-resolution RGB (Red, Green, Blue) and IrRG (infrared, Red, Green) maps were used to delineate tree crowns and extract statistics related to the selected vegetation indices. The results of phytosanitary inspections and multispectral analyses showed that the microclimatic conditions at the site level influence both the impact of crown disorders and tree physiology in terms of water content and photosynthetic activity. Hence, the correlation between the phytosanitary inspection results and vegetation indices suggests that multispectral techniques with drones can provide reliable indications of the health status of Abies nebrodensis trees. The findings of this study provide significant insights into the influence of environmental stress on Abies nebrodensis and offer a basis for developing new monitoring procedures that could assist in managing conservation measures. Full article

Hydrogen sulfide (H₂S), once regarded solely as a highly toxic gas, is now recognized as a crucial signaling molecule in plants, bacteria, and mammals. In humans, H₂S signaling plays a role in numerous physiological and pathological processes, including vasodilation, neuromodulation, and cytoprotection. To exploit its biological functions and therapeutic potential, a wide range of H₂S-releasing compounds, known as H₂S donors, have been developed. These donors are designed to release H₂S under physiological conditions in a controlled manner. Among them, self-reporting H₂S donors are seen as a particularly innovative class, combining therapeutic delivery with real-time fluorescence-based detection. This dual functionality enables spatiotemporal monitoring of H₂S release in biological environments, eliminating the need for additional sensors or probes that could disrupt cellular homeostasis. This review summarizes recent advancements in self-reporting H₂S donor systems, organizing them based on their activation triggers, such as specific bioanalytes, enzymes, or external stimuli like light. The discussion covers their design strategies, performance in biological applications, and therapeutic potential. Key challenges are also highlighted, including the need for precise control of H₂S release kinetics, accurate signal quantification, and improved biocompatibility. With continued refinement, self-reporting H₂S donors offer great promise for creating multifunctional platforms that seamlessly integrate diagnostic imaging with therapeutic H₂S delivery. Full article

Accurate sleep staging and sleep-disordered breathing (SDB) severity prediction are critical for the early diagnosis and management of sleep disorders. However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. We evaluate the proposed method across three public datasets (APPLES, SHHS, and HMC) treated as independent clients. For sleep staging, the model achieves accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with Cohen’s Kappa scores exceeding 0.71. For SDB severity classification, it obtains macro-F1 scores of 77.6%, 76.4%, and 79.1% on APPLES, SHHS_rest, and HMC, respectively. These results demonstrate that our unified FMTL framework effectively leverages multimodal PSG signals and federated training to deliver accurate and scalable sleep disorder assessment, paving the way for the development of a privacy-preserving, generalizable, and clinically applicable digital sleep monitoring system. Full article

Microwave energy is ideal for wearable devices due to its stable wireless power transfer capabilities. However, rigid receiving antennas in conventional RF energy harvesters compromise wearability. This study presents a wearable system using a flexible dual-band antenna (915 MHz/2.45 GHz) fabricated via conformal 3D printing on arm-mimicking curvatures, minimizing bending-induced performance loss. A hybrid microstrip–lumped element rectifier circuit enhances energy conversion efficiency. Tested with commercial 915 MHz transmitters and Wi-Fi routers, the system consistently delivers 3.27–3.31 V within an operational range, enabling continuous power supply for real-time physiological monitoring (e.g., pulse detection) and data transmission. This work demonstrates a practical solution for sustainable energy harvesting in flexible wearables. Full article

Despite the known impacts of weaning on animal health, the underlying molecular mechanisms remain unclear, particularly how psychological and nutritional stress differentially affect gut health and immune function over time. This study hypothesized that early weaning exerts distinct short- and long-term effects on lamb stress physiology, immunity, and gut health, mediated by specific molecular pathways. Twelve pairs of full-sibling male Hu sheep lambs were assigned to control (CON) or early-weaned (EW) groups. Plasma stress/immune markers were dynamically monitored, and intestinal morphology, antioxidant capacity, apoptosis, and transcriptomic profiles were analyzed at 5 and 28 days post-weaning. Early weaning triggered transient psychological stress, elevating hypothalamic–pituitary–adrenal (HPA) axis hormones (cortisol, catecholamines) and inflammatory cytokines (TNF-α) within 1 day (p < 0.05); however, stress responses were transient and recovered by 7 days post-weaning. Sustained intestinal remodeling was observed in EW lambs, featuring reduced ileal villus height, increased crypt depth (p < 0.05), and oxidative damage (MDA levels doubled vs. CON; p < 0.01). Compensatory epithelial adaptation included increased crypt depth but paradoxically reduced villus tip apoptosis. The transcriptome analysis revealed significant changes in gene expression related to immune function, fat digestion, and metabolism. Key DEGs included APOA4, linked to lipid transport adaptation; NOS2, associated with nitric oxide-mediated immune–metabolic crosstalk; and mitochondrial gene COX1, reflecting energy metabolism dysregulation. Protein–protein interaction analysis revealed NOS2 as a hub gene interacting with IDO1 and CXCL11, connecting oxidative stress to immune cell recruitment. Early weaning exerts minimal lasting psychological stress but drives persistent gut dysfunction through transcriptome-mediated changes in metabolic and immune pathways, highlighting key genes such as APOA4, NOS2, and COX1 as potential regulators of these effects. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Photoplethysmographic (PPG) sensors are small and cheap wearable sensors which open the possibility of monitoring physiological parameters such as heart rate during normal daily routines, ultimately providing valuable information on health status. Despite their potential and distribution within wearable devices, their accuracy is affected by several influencing parameters, such as contact pressure and physical activity. In this study, the effect of contact pressure (i.e., at 20, 60, and 75 mmHg) and intensity of physical activity (i.e., at 3, 6, and 8 km/h) were evaluated on a sample of 25 subjects using both a reference device (i.e., an electrocardiography-based device) and a PPG sensor applied to the skin with controlled contact pressure values. Results showed differing accuracy and precision when measuring the heart rate at different pressure levels, achieving the best performance at a contact pressure of 60 mmHg, with a mean absolute percentage error of between 3.36% and 6.83% depending on the physical activity levels, and a Pearson’s correlation coefficient of between 0.81 and 0.95. Plus, considering the individual optimal contact pressure, measurement uncertainty significantly decreases at any contact pressure, for instance, decreasing from 15 bpm (at 60 mmHg) to 8 bpm when running at a speed of 6 km/h (coverage factor k = 2). These results may constitute useful information for both users and manufacturers to improve the metrological performance of PPG sensors and expand their use in a clinical context. Full article