Dynamic borax-crosslinked transparent and antifreezing tragacanth gum-glycerol hydrogel for biomedical use.

A novel ECG QRS complex detection algorithm based on dynamic Bayesian network.

Enhanced-performance flexible pressure sensors enabled by synergistic effect of hierarchical porous structures for motion sensing and deep learning-assisted speech recognition.

Delayed Sleep-Wake Phase Disorder (DSWPD) is a circadian rhythm disorder marked by a consistent and distressing delay in sleep timing relative to societal norms. While traditionally viewed as a circadian phase disorder, growing evidence shows psychological, behavioural, and physical health factors interact with circadian biology to influence onset, maintenance, and outcomes. of review: This review synthesises recent literature on DSWPD's multifactorial nature, focusing on aetiology, nosology, comorbidities, and treatment. It highlights emerging evidence supporting a multidimensional diagnostic approach and personalised, multimodal management. Some individuals with DSWPD exhibit a significantly delayed circadian phase, while others show normal circadian timing but persistently delayed sleep behaviour. A spectrum approach or subtyping into circadian and behavioural variants has been proposed. Comorbidities with psychiatric conditions including depression, anxiety, ADHD and autism are common and may affect treatment response. Chronobiotic treatments remain core, but cognitive-behavioural and psychotherapeutic interventions are increasingly essential, especially in non-circadian or comorbid cases. Advances in wearable technology and circadian modelling offer promising tools for diagnosis, monitoring, intervention. DSWPD is heterogeneous and requires an integrative, individualised approach considering circadian biology, behaviour and psychiatric comorbidities. A multidimensional diagnostic and treatment model could improve outcomes and functioning.

Reactive oxygen and nitrogen species are essential to several biological functions, from maintaining cellular homeostasis to driving disease progression and aging. As such, the precise modulation of cellular redox states has emerged as a promising medical strategy. In parallel, microneedle technology has rapidly advanced over the past two decades, offering minimally invasive, targeted, and painless delivery of therapeutics. This review presents a comprehensive analysis of the emerging field of redox-active microneedles (RAMs), a novel class of wearable medical devices with broad potential for treating infected wounds, cancer, psoriasis, diabetes, arthritis, hair loss, skin aging, and potentially many other conditions. By integrating redox biology with advanced microneedle-based drug delivery systems, RAMs are poised to transform personalized and precision medicine, an evolution supported by a growing number of patents, clinical trials, and rapid advancements in catalytic, photodynamic, and nanomedicine research.

Multifunctional conductive hydrogel based on carboxymethyl cellulose/oxidized sodium alginate for machine learning-guided sports training.

Towards wearable electronic devices: A high linearity bionic flexible stretchable sensor based on MXene/GO nanocomposites and gradient stiffness strategy.

Sustainable consumption of wearable healthcare devices and its relevance to the net-zero agenda: Evidence from senior citizens

Multifunctional NR/MXene/SiO₂ film with core-shell structure for all-weather thermal management and EM shielding.

A flexible miniatured wearable device for monitoring lactic acid and pH in sweat

Tonic motor activation (TOMAC) is a non-pharmacological treatment for moderate-to-severe medication-refractory Restless Legs Syndrome (RLS). This bilateral wearable device applies high-frequency electrical stimulation to the peroneal nerve, engaging the therapeutic mechanism while minimizing sleep discomfort. A recent meta-analysis evaluated TOMAC in RLS using aggregate data, which precluded subgroup analyses. The aim of our systematic review and meta-analysis was to extract individual participant data to enable the evaluation of TOMAC as adjunctive treatment and monotherapy in RLS. This study was registered on PROSPERO (CRD420251005571). Web of Science, Scopus, and PubMed were searched, from inception to March 31, 2025, to identify studies evaluating TOMAC for RLS. Risk of bias (Cochrane Risk of Bias Tool and Downs and Black checklist) and quality of evidence (Oxford Centre for Evidence-Based Medicine 2011 guidelines) of eligible studies were assessed. Primary outcomes were changes in International RLS Study Group Rating Scale (IRLS) score for efficacy and in Medical Outcomes Study Sleep Problem Index II (MOS-II) score for sleep improvement. Main safety outcome was the incidence of device-related adverse events. Subgroup analyses evaluated TOMAC as adjunctive therapy and as monotherapy, as well as by age, RLS age-of-onset, sex, RLS severity, and stimulation amplitude. Five studies from the United States were extracted including three randomized-controlled-trials with 252 participants for analyses (69 monotherapy/183 adjunctive TOMAC therapy). Relative to sham, TOMAC significantly reduced IRLS score both as adjunctive therapy (MD: 3.39, p=0.0001) and monotherapy (mean difference [MD]: 3.80, p=0.0047), and significantly reduced MOS-II score both as adjunctive therapy (MD: 8.23, p=0.0006) and monotherapy (MD: 9.65, p=0.0236). There were no significant differences in IRLS MD based on age, age of RLS onset, sex, RLS severity, and stimulation amplitude. Mild discomfort was the only adverse event with higher prevalence for TOMAC than sham. These results suggest that TOMAC is a tolerable non-pharmacological treatment that reduces RLS symptoms and improves sleep, both as adjunctive therapy and as monotherapy.

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea.

In situ self-layering bilayer alginate-gelatin hydrogels enabling synergistic adhesion and sensing for pressure distribution recognition.

Effective diabetes management is increasingly shifting toward minimally invasive technologies that enable frequent and reliable assessment of glucose levels in interstitial fluid (ISF), enabling more informed and patient-centered monitoring. However, current approaches to glucose detection rely heavily on invasive blood-based glucometers or complex wearable devices for continuous monitoring. This study introduces a complete biosensing system using a novel swellable biocompatible microneedle (MN) array combined with chemically modified screen-printed electrodes (SPEs). Optimization of ISF collection was achieved using a customized applicator with variable vibration, which resulted in ISF uptake of 6.55±0.47μL (0Hz) and 7.06±0.44μL (100Hz) within 5min of application. The Prussian Blue/chitosan-SWCNT/GOx/Nafion-modified SPE shows excellent sensitivity of 12.26μAmM-1 cm-2, a detection limit of 0.08mM, and high selectivity against common ISF interferents. In vitro and ex vivo validation across clinically relevant glucose concentrations showed strong linearity (R2=0.989 and 0.978, respectively), with recovery exceeding 70% in vitro and 50% ex vivo compared with a commercial glucometer. This minimally invasive MN SPE platform enables reliable glucose quantification from microliter ISF volumes and shows strong potential for future multi-biomarker, point-of-care monitoring.

Interindividual differences in digital phenotypes of major depressive disorder: A passive sensing study using smartphone and wearable sensor data.

High-performance hydrogel sensors with dual-network structure for wearable devices: Integration of self-healing, antimicrobial, extreme environmental tolerance, and long-term sensing stability

Toward non-invasive diagnostics through AuNSs@Nano-MIP biosensor for sensitive lactoferrin detection in sweat.

Hyperuricemia (HUA), a complex metabolic disorder, has shown a continuous increase in global prevalence and a trend toward younger age at onset. Its management strategies now face the key challenge of transitioning from a “one-size-fits-all” approach to precision care tailored to individual needs. This review systematically summarizes the epidemiology, pathogenesis, and limitations of current dietary guidelines for HUA, highlighting that genetic background, gut microbiota, and dietary patterns collectively generate marked inter-individual variability. Medicinal food homologous substances, which regulate uric acid metabolism through multiple targets—by inhibiting xanthine oxidase, modulating uric acid transporters, alleviating inflammation, and reshaping the gut microbiota—have become promising resources for precision intervention. However, their complex composition and heterogeneous responses hinder clinical translation. Artificial intelligence (AI), with its powerful multi-modal data integration and pattern recognition capabilities, offers a novel avenue for constructing a precision intervention system for HUA. We elaborate on AI-enabled risk prediction (polygenic risk scores and machine-learning models), dynamic monitoring (wearable devices coupled with LSTM networks), intelligent TCM (Traditional Chinese Medicine)-syndrome identification, and the development of personalized food recommendation systems (PFRS). By integrating genomics, clinical data, behavioral information, and TCM phenotyping, AI can build an individual “metabolic profile”, realizing a closed loop of full-cycle management from early warning and syndrome discrimination to dynamic generation of personalized diets and medicinal-food recommendations. This paradigm shift from “one-size-fits-all” to “one person, one policy” lays the theoretical foundation for an intelligent, whole-cycle HUA health-management system. Future efforts should focus on multi-center data integration, model validation, and clinical translation.

Hospital triage requires rapid and accurate assessment of vital signs, supported by well-trained personnel within a robust emergency medical system. However, in remote regions of Colombia, access to skilled staff and monitoring equipment is limited. This study proposes a machine learning framework to estimate blood pressure using photoplethysmography (PPG) signals, demographic data, and comorbidity information within an automated IoT-based triage system. As no prior machine learning–based solutions have addressed patient health status prediction in isolated Colombian regions, this framework aims to provide a complementary triage system in areas lacking expert support. The system integrates a forearm-worn wearable device with a kiosk to collect data, generating 20 input features encompassing demographic/comorbidity information, summary vital signs, and PPG morphology and variability descriptors. Three regression models - feedforward neural network, XGBoost, and Random Forest - are trained and compared for simultaneous estimation of systolic and diastolic blood pressure. Training uses a public short-record PPG dataset comprising 657 signal segments from 219 subjects with subject-wise cross-validation; external validation is performed on the PhysioNet Pulse Transit Time-PPG database. Tree-based ensemble models outperform the neural network on the main dataset, with XGBoost achieving the best performance for both systolic and diastolic blood pressure. These findings highlight ensemble models as competitive and interpretable alternatives for PPG-based blood pressure estimation, supporting their integration into IoT-enabled triage systems to improve evidence-based patient prioritization, especially in underserved regions.

A smart, shielding, and self-sterilizing E-skin with wavelet-enhanced signal fidelity for next-generation wearable healthcare.