Numerous exocrine glands play key physiological roles in the body that include tearing, salivation, and lactation, as well as the control of body temperature via sweating. Malfunction of sweat glands can be deeply problematic or-in the case of anhidrosis-life-threatening. The prevalence of sweating disorders is high, affecting millions. The few available therapies are generally of limited effectiveness. Several lines of evidence point to regulation of sweating by the cannabinoid signaling system, an arrangement that would mirror cannabinoid regulation of tearing and salivation. Mice sweat in their paws via glands that closely resemble human eccrine sweat glands, including regulation by muscarinic signaling and by temperature. We applied a galvanic skin response-based assay to investigate cannabinoid regulation of sweating in awake, unanesthetized mice. The muscarinic agonist pilocarpine increased conductance while the antagonist glycopyrrolate reduced conductance, validating the model as a measure of sweating. The cannabinoid receptor agonist CP55940 substantially reduced conductance in wild-type and CB2 but not CB1 receptor knockout mice. The phytocannabinoid tetrahydrocannabinol (THC) also reduced conductance, while the non-psychoactive cannabidiol (CBD) did not. Using immunohistochemistry, we detected CB1 receptors in periglandular cholinergic axons, the anandamide-synthesizing enzyme NAPE-PLD in myoepithelial cells, and the anandamide metabolizing enzyme FAAH in acinar cells. This indicates that a local CB1/anandamide-based circuit is present in mouse walking pads. In summary, we employed a novel galvanic skin response-based assay to determine that cannabinoid CB1 receptors reduce sweating in a mouse model. This may point to a previously unappreciated effect on sweating in cannabis users.
Wearable biosensing technologies are advancing sports performance monitoring by enabling the continuous and real-time measurement of physiological and biochemical parameters. Among non-invasive biofluids, sweat has become the most widely studied medium due to its easy accessibility during physical activity and its presence of multiple relevant biomarkers. This review critically examines the recent developments in sweat-based wearable biosensing technologies and identifies the key challenges that hinder their transition from laboratory prototypes to practical sports-monitoring systems. The discussion includes a brief introduction to sweat generation, the important biomarkers present in sweat, and their significance in sports health monitoring. Various electrochemical sensing platforms designed for sweat analysis are reviewed, with an emphasis on their structural designs and operational mechanisms. Major application areas, including lactate monitoring for fatigue detection, electrolyte sensing for hydration assessment, and cortisol measurement for stress evaluation, are discussed. This review also highlights the important challenges, including sensor calibration, motion-related artifacts, variability in sweat composition among individuals, and long-term operational stability. Emerging approaches, including multimodal sensing, machine-learning-assisted data interpretation, nanomaterial-enabled sensors, and closed-loop feedback systems, are also discussed as potential solutions to improve the reliability and real-world applicability of sweat-based wearable biosensors for sports performance monitoring.
Effective management of prenatal nutrient concentrations, such as those associated with folate, is critical for the health of the prospective mother and child, but quantifying them currently requires frequent blood tests and specialized laboratories. Human sweat is a non-invasive alternative to blood that is well suited for point-of-care biosensing. Here we present a skin-interfaced microcapsule that enables collection and storage of pristine, microlitre volumes of sweat and supports an efficient interface to a portable lab-on-a-disc platform for folate quantification. This platform automates an entire enzyme-linked immunoassay sequence for measuring folate in sweat, including incubation, washing, mixing and detection, and facilitates wireless data transmission. A series of tests in human participants reveal a dose-response relationship between oral intake of folate supplements and sweat folate levels, with a strong correlation in levels between sweat and serum. In addition, daily tracking of sweat folate concentrations shows clear differences between control periods without supplementation and daily intake periods. This technology creates possibilities for the routine use of sweat for precise point-of-care assessment of prenatal nutrient bioavailability.
To evaluate continuous and non-invasive sweat-inferred blood lactate monitoring for determining the exercise intensity at the second metabolic threshold (MT2) and maximal lactate steady state (MLSS), compared to capillary blood lactate and gas exchange analysis. 17 physically active individuals (11 males and 6 females) and 19 endurance athletes (14 males and 5 females) completed a maximal graded exercise test (GXT) to quantify oxygen uptake (VO2), percentage of peak oxygen uptake (%VO2peak) and power output (watts) at MT2, using sweat-inferred blood lactate and capillary blood lactate for the second lactate threshold (LT2), and gas exchange analysis for the second ventilatory threshold (VT2). Participants also completed a MLSS test to quantify the power output (watts) at MLSS. Between-methods agreement was assessed using linear mixed models with mean differences (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), Lin's concordance correlation coefficient (CCC) and Bland-Altman analysis. Compared with VT2, LT2 derived from sweat-inferred blood lactate showed no significant differences when using the Log-Exp-Mod-Dmax method, yielding the strongest agreement across VO2 (MD = 0.3 mL·min -1·kg -1, p = 0.999; MAE = 2.1 mL·min -1·kg -1, MAPE = 5.2%), %VO2peak (MD = 0.4%, p = 0.999; MAE = 4.1%, MAPE = 5.2%), and power output (MD = 1.9W, p = 0.999; MAE = 9.1W, MAPE = 5.0%). Direct comparison of capillary blood and sweat-inferred blood lactate using the Log-Exp-Mod-Dmax method showed high agreement for VO2 (MD = 0.2 mL·min -1·kg -1, p = 0.999; MAE = 2.4 mL·min -1·kg -1, MAPE = 6.4%), %VO2peak (MD = -0.1%, p = 0.999; MAE = 4.9%, MAPE = 6.4%), and power output (MD = 0.0W, p = 0.999; MAE = 10.6W, MAPE = 6.1%). Capillary blood and sweat-inferred blood lactate also showed excellent agreement for MLSS-associated power output (MD = -2.0 W, p = 0.089; MAE = 3.1 W, MAPE = 2.5%). Continuous sweat-inferred blood lactate can be used to determine MT2 and MLSS intensities, providing an alternative to capillary blood lactate.
Leucine (Leu) is a promising biomarker for metabolic health and muscle growth, offering significant potential for assessing physical well-being. Sweat sensors for Leu detection eliminate the reliance on invasive blood analysis and the sophisticated, large-scale instrumentation. Current sweat sensors, however, are complex to fabricate, exhibit low sensitivity toward nonelectroactive Leu, require intense exercise or thermal/chemical stimulation to generate sweat, and lack reusability. This work reports a flexible, highly sensitive, and reusable sweat sensor based on molecularly imprinted polymers, Prussian blue nanoparticles, and laser-induced graphene. When integrated with a highly permeable porous Poly(vinyl alcohol) hydrogel for convenient and rapid collection of instantaneous sweat secreted from the fingertip, the sensor can continuously detect Leu with high sensitivity (7641 nA mm-2 per decade), low detection limit (10 nM), and excellent repeatability. This flexible biosensing patch offers a promising strategy for noninvasive sweat Leu analysis and wearable health monitoring, showing potential for assessing health status related to obesity, type 2 diabetes (T2DM), and muscle loss.
We present herein a unique analytical methodology based on a one-step, three-dimensional printed sensing device for measuring sweat rate noninvasively, which allows for the assessment of hydration levels. It comprises a multilayered construction, and diverse designs are investigated, with the printing components consisting of conductive (CB-PLA) and nonconductive polylactic acid (PLA) printable materials. A prominent aspect is the integration of what is termed here as "floating electrodes", which advantageously modify the overall impedance of the device during sweat flow measurements. To rationalize the mechanism behind this, multiple sensor configurations, including two-, four-, six-, eight-, or multilayer structures, were thoroughly examined. Although multilayer configurations demonstrated the potential to enhance the overall capacity and sensing area, multiplying the number of electrodes in the device effectively resulted in a higher frequency of impedance variations while providing additional sweat rate data points from a single device. In off-body testing, the optimal system (based on four electrodes) exhibits a calibration range of 1-10 μL min-1 with a total capacity of 37 μL and a correlation coefficient of r = 0.927 between the sensor and timer-based flow validation. With variations oscillating between 0 and 15% as well as a strong correlation (r = 0.79-0.93) with the cotton patch gravimetric method, on-body validation utilizing iontophoresis and cycling to generate sweat in the tested subjects demonstrated strong correlation with standard methods. Our findings illustrate the potential of the "floating electrode" concept for scaling up to larger and higher-capacity sweat sensing devices, as it functions effectively in both single- and double-layer designs. In addition, 3D printing technology will allow for on-demand customization of the corresponding analytical devices.
Sweat is a rich biofluid whose composition depends heavily on physiology and varies systematically across a range of systemic and dermatological conditions, making it an attractive medium for non-invasive diagnostics. However, existing diagnostic tools, which rely primarily on electrochemical ion-selective electrodes and optical microfluidic systems, require complex instrumentation and have significant limitations in ease of application and deployment. This poses a need for a low-cost, simple sensing approach using sweat as a sample for disease detection. Here we demonstrate a novel bubble sensing methodology that exploits the relationship between bubble film stability and electrolyte concentration in a reagent-free setup requiring no electrochemical transduction. A controlled-volume bubble was made using a sodium dodecyl sulphate-glycerol solution, which was then tested by adding potassium chloride (KCl) solutions at concentrations of 0.01-0.15 mol L-1, simulating sweat at variable ionic strengths. Two characteristic timescales were identified: the time to burst (tb), measured by the naked eye on a seconds timescale, and the film retraction time (τ), resolved at 100 000 frames per second using a high-speed camera. The time to burst exhibited a strong exponential decay with increasing KCl concentration (R2 = 0.934), with greatest sensitivity in the healthy resting sweat range (0.01-0.1 mol L-1) and a plateau at pathological concentrations above 0.1 mol L-1. High-speed imaging revealed distinct changes in rupture initiation location and film retraction behaviour upon analyte addition, with retraction time increasing from 250 µs in control bubbles to ∼1.5 ms. The observed trend was quantitatively reproduced using a coupled DLVO-Kramers nucleation model, identifying electrostatic double-layer screening as the primary mechanism driving faster rupture at higher ionic strength. This work establishes the proof of concept for bubble rupture dynamics as a functional sensing mechanism and provides the basis for further development of surfactant bubble-based biosensors.
The colonization of textiles by axillary skin bacteria produces an unpleasant odour due to the rapid growth of a selective community of bacteria. Such colonized textiles subsequently act as vectors for transmitting nosocomial infections among healthcare workers and patients. An in-depth understanding of bacterial behaviour on soft surfaces like fabrics is necessary to mitigate the transmission of infections. This study examined the effect of artificial human sweat on biofilm formation by Staphylococcus aureus, Escherichia coli, Enterococcus faecalis, and Pseudomonas aeruginosa, on three fabrics, viz. polyester, cotton, and polyester-cotton (70:30) blend. Artificial sweat was constituted to replicate the natural human sweat on textiles. Using atomic force microscopy, the three-dimensional topography of the biofilm was determined, and scanning electron microscopy was employed to visualise the biofilm that had developed on the fabrics. All bacterial strains showed maximum growth on polyester fabric in the presence of sweat. P. aeruginosa and S. aureus were found to be strong biofilm producers, whereas E. coli and E. faecalis were moderate producers. The ability of the four bacterial strains to form biofilm was related to their production of extracellular polymeric substances (EPS). P. aeruginosa produced viscous EPS in contrast to the EPS produced by other bacterial strains. In conclusion, this study corroborates that sweat plays a major role in the colonization of textiles by bacteria. Regular practice of fabric hygiene, and the development of modified fabrics with anti-pathogen properties, could potentially reduce the prevalence of nosocomial infections in healthcare settings. The online version contains supplementary material available at 10.1007/s12088-024-01409-0.
Wearable technologies have emerged as transformative tools in modern societies, enabling non-invasive and continuous monitoring of physiological parameters outside traditional clinical settings. Practical deployment of biosensors into compact wearable systems requires overcoming key challenges, like achieving sufficient signal stability, specificity, miniaturization, portability, and, most critically, long-term operational reliability and manufacturing scalability. Here, we address some of these challenges by presenting a hybrid electrochemical biosensor for lactate detection in human sweat, integrating silk fibroin as a bioactive immobilization matrix, ionic liquids (ILs) as ionic conductivity enhancers, and a Prussian Blue-based transducer. Silk fibroin significantly enhanced lactate oxidase stability and catalytic activity, while the incorporation of ILs improved ionic transport, thereby favoring electron transfer and increasing sensor sensitivity. To overcome the intrinsically narrow linear range of enzymatic lactate sensors, a diffusion-modulating top layer was engineered, enabling controlled mass transport and extending the measurable range to the full physiological lactate window in sweat (0.5-70 mM). The resulting biosensor exhibited high sensitivity, robust linearity, and stable performance. Validation with artificial and real sweat samples demonstrated accurate quantification and >90% recovery, with strong agreement with ion chromatography. The SF/IL architecture provides a versatile scaffold that could be extended to simultaneous multi-analyte detection, enabling comprehensive, real-time metabolic profiling from a single sweat patch, enabling non-invasive, continuous, and personalized health monitoring.
The determination of soluble elemental contaminants in tattoo inks is challenged by the lack of standardized extraction procedures, limiting the comparability of analytical results and the assessment of exposure-relevant fractions under the European REACH framework. In this study, artificial sweat extraction was applied as a mild and physiologically relevant approach to evaluate elements potentially released from tattoo inks under sweat-simulated skin-contact conditions. Seventy-eight commercial tattoo inks of different colors were extracted with artificial sweat at 37 °C for 1 h and analyzed by inductively coupled plasma mass spectrometry. Optimization of collision/reaction cell conditions, dilution strategy, and internal standard correction effectively reduced matrix-related interferences caused by the high salt and chloride content of artificial sweat, ensuring reliable quantification. Matrix-matched calibration was required due to significant signal suppression for several analytes. Method accuracy and precision, assessed using NIST 1643f and spiked samples, were generally satisfactory. Elemental release showed marked color-dependent trends, particularly for Cu, Zn, Ba, Al, Ga, Si, Sr, and Zr, reflecting differences in pigment composition and formulation. Soluble Ba, Cu, and Zn remained below EU regulatory limits. While total digestion remains essential for complete characterization, the proposed methodology provides a simple and transferable tool for exposure-oriented assessment of potentially bioaccessible elements in tattoo inks.
Efficient localization of analytes near plasmonic hotspots remains a major challenge for surface-enhanced Raman scattering (SERS) detection in complex liquid-phase systems. Herein, an interlaced cellulose-based porous membrane composed of dissolving pulp fibers (DPFs), mechanically ground nanofibers (MGNFs), sodium alginate (SA), and Ag nanoparticles was developed as a molecular enrichment-assisted SERS platform for sweat biomarker determination. The hierarchical porous architecture provided interconnected transport channels and abundant interfacial adsorption sites, facilitating analyte retention and localized enrichment within the plasmonic region. Benefiting from the synergistic effects of porous confinement and uniformly distributed Ag nanoparticles, the optimized substrate exhibited sensitive and reproducible SERS performance with an enhancement factor of 4.36 × 10⁷, a relative standard deviation of 8.04%, and a detection limit down to 1.0 × 10⁻⁸ mol/L for Rhodamine 6G (R6G). Systematic adsorption experiments using molecules with different charge properties further demonstrated the broad molecular enrichment capability of the structured cellulose network. The developed platform enabled quantitative detection of lactate and urea within physiologically relevant sweat concentration ranges and showed satisfactory analytical performance in spiked sweat samples. More importantly, this work demonstrates a structure-engineered porous membrane strategy for integrating molecular enrichment with plasmonic sensing, providing new insight into cellulose-based SERS platforms for complex bioanalytical applications.
Surface-enhanced Raman scattering (SERS) is a promising tool for sweat analysis, but signal noise, nonlinear responses, and spectral overlap limit its quantitative accuracy in complex samples. In this work, an intelligent SERS sensing strategy was developed by combining an Ag NP fractal substrate with a Gradient Kernel Size Convolutional Neural Network (GKS-CNN). The self-assembled Ag NP substrate provided abundant electromagnetic hotspots, enabling sensitive detection of lactate (LA) and glucose (Glu) from 10-4 to 101 mM, with limits of detection of 108 nM and 224 nM, respectively. PCA results showed that the preprocessed SERS spectra contained distinguishable concentration-related information. Compared with linear regression, traditional machine learning methods, and standard CNNs, GKS-CNN achieved better prediction performance by extracting multi-scale features from nonlinear and overlapping spectra. For pure-component samples, the R2 values of LA and Glu reached 0.99 and 0.98, respectively; for the joint dataset containing pure and mixed samples, the R2 values were 0.98 and 0.97, respectively. In real sweat analysis, sample-level splitting was used to reduce data leakage, and the model classified four exercise-related states with an independent test accuracy of 95.00%. The average accuracies of 20 repeated random splits and 5-fold cross-validation were 94.50% and 93.50%, respectively. These results suggest that the proposed SERS-GKS-CNN strategy has potential for non-invasive sweat metabolite analysis and preliminary physiological-state recognition.
Electronic skin (e-skin), a stretchable, conformable, and multimodal sensing platform, is rapidly advancing in healthcare, robotics, and human-machine interaction (HMI). However, prolonged wear and sweat accumulation at the e-skin-skin interface can introduce signal artifacts, cause discomfort, and eventually lead to interfacial failure. Inspired by the unidirectional liquid transport of the Nepenthes peristome, we constructed an intelligent breathing e-skin (SPTL) featuring a Janus bilayer structure that creates a "liquid diode" effect for active sweat transport, achieving a remarkable cumulative one-way transport index of 956.36 while maintaining a dry interface even under profuse perspiration. This device exhibits a high tensile strain of 627%, a breathability of 20.02 mm s-1, and a pressure sensitivity of 7.39 kPa-1. Furthermore, the SPTL demonstrates versatile multimodal sensing capabilities, including real-time Morse code and non-contact capacitive sensing, while sustaining stable performance over 10,000 pressing cycles (capacitance decay < 3%). As a bio-integrated electrode, SPTL enables high-fidelity acquisition of electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG) signals, significantly outperforming commercial Ag/AgCl electrodes. Integrated with machine learning algorithms and EMG signals captured by the SPTL, it facilitates precise teleoperation of a quadruped robot and handwritten letter recognition with over 95% accuracy. This bionic strategy provides a versatile solution for intelligent, breathable, and multimodal bio-integrated interfaces.
Skin contact is an important route for human exposure to atmospheric pollutants. Many atmospheric pollutants possess photochemical activity, and skin sweat provides a natural environment for the photogeneration of reactive species (RSs) from pollutants. However, the photochemical transformation of atmospheric pollutants in skin sweat and their toxicological effects on the skin remain poorly understood. Herein, 1-nitronaphthalene (1-NN), a common atmospheric pollutant, was used as a model compound to investigate the influence of key components (i.e., Organic acids, Fe3 +) of skin sweat on the degradation and transformation mechanisms of 1-NN, as well as the skin toxicity of its transformation products. Our results show that lactic acid (LA) facilitated the photodegradation of 1-NN. The excited triplet state 1-NN (31-NN*) can oxidize LA to generate carbon-centered radicals (·CH(CH3)OH) that was confirmed by EPR spin trapping and high-resolution mass spectrometry. Histidine (His) further accelerates this process by consuming dissolved oxygen (3O2), thereby inhibiting the quenching of 31-NN* as well as carbon-centered radicals by 3O2. Additionally, the presence of Fe3+ can trigger the generation of carboxyl radicals (CO2•-) via carboxylic acid ligand-to-metal charge transfer, further enhancing the photodegradation of 1-NN. Toxicity assays indicated that the photochemical transformation process of 1-NN can lead to higher cytotoxicity. These findings provide a new strategy for understanding the transformation processes and toxicity evolution of nitrated polycyclic aromatic hydrocarbons on the skin surface.
Elevated methylglyoxal (MG) levels contribute to diabetes-related complications through accelerated formation of advanced glycation end products, highlighting the need for sensitive, portable, and non-invasive monitoring strategies. Here, we report a gold-carbon nanohybrid electrochemical MG sensor integrated into a three-dimensional printed microfluidic platform for point-of-care analysis. The sensing interface is engineered by sequential electrodeposition of carboxyl-functionalized multi-walled carbon nanotubes (fMWCNT) and gold nanoparticles (AuNPs) onto a screen-printed carbon electrode (SPCE). The fMWCNT provide a high-surface-area scaffold for MG adsorption, while AuNPs enhance electroactive surface area and electron transfer. The AuNPs/fMWCNT/SPCE sensor exhibits quasi-reversible electrochemical behavior suitable for sensitive MG detection. The optimized sensor achieves reliable MG detection across a wide dynamic range (50 nM-100 µM), with limits of detection of 40 nM in aqueous media and 400 nM in artificial sweat. Accurate MG quantification in human saliva and sweat, with recoveries exceeding 97%, demonstrates robustness in complex biological matrices. This work demonstrates the potential of this electrochemical sensing system for future wearable biosensors and personalized diabetes monitoring.
Wearable biochemical sensing is shifting sports physiology from intermittent laboratory sampling toward continuous, in situ readouts during training and competition. This review analyzes skin conformal systems that integrate capillary driven fluid routing, flexible sensor arrays, and wireless readout to capture dynamic changes in eccrine secretions relevant to performance management. We compare microchannel layouts that enable controlled filling, time resolved sampling, and evaporation suppression, and we discuss how material selection governs comfort and analytical fidelity, highlighting tradeoffs among polydimethylsiloxane, polyurethane, and polyethylene terephthalate substrates and their compatibility with scalable fabrication. Manufacturing pathways are assessed from soft lithography to laser cutting, three dimensional printing, and roll to roll processing for high throughput multilayer assembly. For transduction, we summarize enzymatic amperometric schemes based on lactate oxidase with hydrogen peroxide detection, including sensitivity, response time, and oxygen dependence, and we contrast these with emerging non enzymatic catalysts. We then detail potentiometric ion selective electrodes for sodium and potassium, focusing on ion selective membrane chemistry, solid state reference electrodes, Nernstian response, and dominant error sources such as drift and biofouling. System integration challenges, including chemical and electrical cross talk in multiplexed layouts, are linked to microfluidic isolation strategies and multiplexed electronics. Finally, we appraise validation practice, emphasizing the debated sweat to blood relationship, the need for synchronized comparative protocols, and the role of data analytics and machine learning for personalization, drift compensation, and prediction of thermoregulatory strain. Remaining barriers include long term stability, adhesion under motion, manufacturability, and regulatory evidence requirements in real world settings. Across these topics, the review emphasizes technology-specific limitations, engineering translation metrics, and realistic breakthrough directions rather than treating wearable sweat patches as a mature plug-and-play platform.
Reliable, noninvasive monitoring of metabolic biomarkers using wearable biosensors remains a significant challenge, owing to the low and variable concentration of analytes in sweat. Herein, we report a hierarchical V2CTx-MXene@f-MWCNTs/Laser-Induced Graphene (LIG) hybrid architecture for the multiplexed and dynamically calibrated sweat sensing of glucose and β-hydroxybutyrate (HB). The synergistic integration of 2D MXene and 1D f-MWCNTs prevents MXene restacking, increases electron flow, and enhances the electroactive surface area, while the porous 3D LIG matrix provides a flexible, and conductive platform for wearable bioelectronics. The microfluidic-integrated V2CTx@f-MWCNTs nanocomposite-based patch sequentially detects glucose and HB while providing real-time pH and temperature calibration. The fabricated patch biosensor exhibits high sensitivities of 131.41 μA mM-1 cm-2 and 53.81 μA mM-1 cm-2 (glucose and HB), with low detection limits of 2 μM and 10 μM, respectively. Moreover, the HB sensor exhibits stable performance for more than 171 min of continuous operation. This multifunctional platform demonstrates excellent electrochemical performance, stability, and reproducibility and reliable on-body applicability, providing accurate metabolic health monitoring and advancing next-generation noninvasive bioelectronic platforms.
Scientific exercise monitoring is significant for injury risk prevention and training outcome promotion. Wearable biosensing technologies have emerged as transformative tools for real-time, in-situ physiological state profiling through dynamic biomarker detection during exercise activities. However, current studies remain suboptimal for practical exercise management due to inherent constraints including single-analyte detection paradigms, limited permeability and breathability, and inadequate thermoregulatory performance. Here, we present a novel wearable composite fabric system engineered for multiplex sweat biomarker monitoring while delivering unprecedented wear comfort. The hierarchical architecture was achieved through strategic integration of interwoven fiber-based sensor arrays with conventional textiles, augmented by bilateral deposition of microbead-enhanced polyvinylidene difluoride (PVDF) and polyacrylonitrile (PAN) electrospun nanofiber membranes. The porous matrix, Janus hierarchical gradient, and microbead-mediated interfacial engineering render this fabric system with excellent breathability of 13 mm/s, water vapor transmission rate of 468.9 g/m2/h, and solar reflectance of 96.2%. Systematic validation revealed the system's capabilities in multiplex biomarker tracking during diverse exercise scenarios, machine learning-powered fatigue assessment, and scientific exercise regimen evaluation. This work establishes a universal framework for developing wearable platforms with reliable sensing functionality and wear comfort, facilitating effective personalized exercise healthcare management.
The accurate and non-invasive detection of dopamine (DA) in sweat is crucial for the early diagnosis of neurological diseases, but the selective and sensitive detection of DA in complex biological matrices still remains a challenge. This study presents a novel screen-printed electrode based on a ternary nanocomposite of gold nanobipyramid@copper selenide@MXene nanosheets (Au NBP@Cu2-xSe@MXene) for the highly sensitive and selective detection of DA. For the first time, Au NBPs with localized surface plasmon resonance (LSPR) in the near-infrared second window (NIR-II) are employed to enable a one-step, pretreatment-free detection strategy by leveraging the negligible background absorption of biological matrices. Under NIR-II irradiation, the Au NBP core generates an LSPR-induced photothermal effect, which can be harnessed by the Cu2-xSe shell to generate a thermoelectric field that significantly accelerates the interfacial electrocatalytic oxidation of DA. Besides, Au NBPs can generate LSPR hot carriers, which are injected into Cu2-xSe to participate in the redox process. Furthermore, the MXene substrate ensures efficient charge transport and structural stability. As a result, this ternary nanocomposite-based sensor exhibits a wide linear detection range for DA from 0.1 to 1000 μM. It achieves a detection limit of 0.107 μM under standard conditions, which is further reduced to 0.068 μM under NIR-II laser irradiation, demonstrating the effective signal amplification via photothermal-thermoelectric coupling. This work provides a robust and innovative material platform that integrates photothermal, thermoelectric, and electrochemical mechanisms, paving the way for the development of next-generation, high-performance wearable sensors for non-invasive health monitoring.
Introduction Hyperhidrosis is a condition characterized by excessive sweating that can negatively affect quality of life and psychosocial well-being. This study aimed to determine the self-reported prevalence of hyperhidrosis symptoms and the level of awareness among medical students at Gulf Medical University, Ajman, UAE. Methods A cross-sectional study was conducted over six months among students aged ≥18 years across six academic programs. Ethical approval was obtained, and a pilot study was conducted before data collection. A structured self-administered questionnaire developed from a literature review and reviewed by specialists was used to assess sociodemographic characteristics, self-reported excessive sweating symptoms suggestive of hyperhidrosis, awareness, and associated factors. Data were analyzed using descriptive and inferential statistics. Results Out of 600 eligible participants, 480 completed the questionnaire (response rate: 80%). The majority were female (75.4%) and aged 18-21 years. The prevalence of self-reported excessive sweating symptoms suggestive of hyperhidrosis was 36.8% (171/465; 95% CI: 32.4%-41.2%). Among participants reporting excessive sweating symptoms who responded to the relevant items, the axilla was the most commonly affected site, followed by the face and hands. Afternoon was the most frequently reported time of excessive sweating. Awareness of hyperhidrosis was low, with only 31.7% of participants reporting prior knowledge, most commonly through the internet and academic sources. A significantly higher prevalence of self-reported excessive sweating symptoms was observed among males compared to females (p<0.01). Smoking was also significantly associated with self-reported excessive sweating symptoms (p<0.05). No significant associations were observed with age group, body mass index, or diabetes status. A proportion of participants reported a positive family history of similar symptoms. Conclusion Self-reported excessive sweating symptoms suggestive of hyperhidrosis were relatively common among medical students, but awareness remained low. While associations with gender and smoking were observed, the findings should be interpreted cautiously, given the use of self-reported data rather than clinically validated diagnostic criteria. Increased awareness and further studies using validated diagnostic instruments are recommended.