Search Results (1,108)

Exosomes and small extracellular vesicles are promising liquid-biopsy biomarkers because they carry molecular information from their cells of origin and can be accessed from minimally invasive biofluids. Reliable separation and detection are made more difficult by their small size, low abundance, diverse composition, and co-occurrence with lipoproteins, protein aggregates, and other extracellular particles. To improve exosome enrichment, capture, and sensing, nanomaterial-assisted techniques have become crucial. Using a mechanism-based approach that differentiates between non-gravimetric signal amplification and genuine physical mass loading, this study offers an organized comparison of nanomaterial-enabled exosome sensing techniques. This distinction is helpful because different transducers measure different physical quantities: while optical, electrochemical, fluorescent, catalytic, and nucleic acid-based platforms typically benefit from enhanced signal generation rather than increased mass, resonant and gravimetric sensors benefit from increased inertial or surface-bound mass. In terms of amplification mechanism, transducer compatibility, sample-matrix tolerance, workflow complexity, and translational maturity, the review contrasts metallic nanoparticles, magnetic systems, metal–organic frameworks, carbon and two-dimensional materials, quantum dots, upconversion nanomaterials, DNA nanostructures, and polymer-based platforms. The gap between analytical sensitivity and clinical utility, including separation purity, recovery, biological heterogeneity, pre-analytical variability, interference from complex biofluids, and the need for uniform validation, is given special focus. The review concludes that no single nanomaterial or amplification method is universally optimal; instead, platform-aware, application-specific integration of isolation, amplification, and validation techniques is necessary for clinically meaningful exosome sensing. Full article

This study aims to evaluate multiple feature sets composed of sensor-based biomarkers acquired during walking for the automated estimation of post-stroke motor impairment levels using Fugl-Meyer Lower Extremity Assessment (FMA-LE)-derived classes. Sensor-based walking data from the open-source ARRA dataset were combined with data collected at the Hospital of Braga. Data from 32 post-stroke individuals (FMA-LE motor score: 24 ± 3) were included. A decision tree classifier was evaluated using stratified six-fold cross-validation across different feature sets, including: correlated with motor impairment levels versus full feature sets; spatiotemporal versus surface electromyographic (sEMG) features; inclusion of demographic variables; and the use of data augmentation. The best performance was achieved using correlated sEMG features combined with age, paretic side, and body mass, along with noise-based data augmentation, yielding a validation Matthews Correlation Coefficient (MCC) of 0.85 ± 0.16 and a test MCC of 0.70. sEMG features provided improved classification performance compared to spatiotemporal features, and comparable results were obtained using a reduced subset of muscles. These results demonstrate the feasibility of using sEMG-based features acquired during walking to classify post-stroke motor impairment levels. Feature reduction and inclusion of demographic variables may support efficient model design, while data augmentation may enhance generalization. Further validation in larger and more diverse datasets is required to assess robustness and clinical applicability. Full article

This study reports the development of a portable multispectral optoelectronic system for automated thyroid cancer detection in immunohistochemically stained histological slides. The platform integrates a 14-band AS7343 multispectral sensor, a dual-fiber optical setup operating in transreflectance geometry, and a two-dimensional scanning subsystem for spatially resolved acquisition. Data acquisition and management were implemented on a Raspberry Pi using Python, Flask, React, Redis, and SocketIO for control, visualization, and real-time updates. A standardized Dark–White–Sample protocol was adopted for baseline correction and the generation of multispectral cubes organized by spatial position and spectral band. The dataset comprised 29 patients, 84 FFPE biomarker-stained histological sections, and 66,510 original point-by-point multispectral measurements. Spectral patterns associated with malignant and non-malignant thyroid samples were analyzed using Linear Discriminant Analysis (LDA), Support Vector Machine with radial basis function kernel (SVM-RBF), and Multilayer Perceptron (MLP). All metrics were evaluated on an independent slide-level test set. LDA achieved 86.8% sensitivity, 95.9% specificity, and 91.0% accuracy. SVM-RBF and MLP achieved accuracies of 90.8% and 90.4%, respectively. Macro-averaged AUC-ROC values were 0.836, 0.865, and 0.761, respectively. These findings support the system as a portable proof-of-concept platform for computer-aided thyroid pathology. Full article

Exhaled breath analysis has emerged as a promising non-invasive approach for disease diagnosis, leveraging gas sensors for their high sensitivity, portability, and real-time monitoring capabilities. Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides (TMDs), MXenes, black phosphorus, and metal–organic frameworks (MOFs), exhibit exceptional gas-sensing properties due to their atomic-scale thickness, ultra-large specific surface area, and tunable electronic structures. These characteristics enable enhanced gas adsorption and room-temperature operation, making them ideal for detecting ppb-level biomarkers like acetone, ammonia, and nitric oxide in breath. However, sensors based on pristine 2D materials face challenges including slow response/recovery kinetics, poor stability, weak humidity resistance, and limited selectivity in complex breath environments. To address these limitations, functionalization strategies have been developed to engineer material properties. Key approaches include heteroatom doping to modulate electronic band structures, heterojunction construction to facilitate charge transfer and improve selectivity, and noble metal decoration for catalytic enhancement of gas adsorption. Additionally, light irradiation has been employed to regulate the carrier concentration on the surface of sensitive materials. These strategies significantly boost sensor performance, achieving ppb-level detection limits, robust humidity resistance, and rapid response. Future directions involve integrating functionalized 2D materials into wearable, multiplexed sensor arrays for simultaneous biomarker detection, coupled with machine learning for real-time diagnostic platforms. Full article

Heat stress remains a significant issue in pig production, particularly in Mediterranean regions, due to the link between climate change and rising temperatures. This study evaluated the effects of heat stress on physiology, oxidative status, animal welfare, histopathological changes, and production in a commercial farrow-to-finish pig farm during the warm season of 2025. Environmental conditions, physiological parameters, welfare, and oxidative stress biomarkers were monitored throughout the study period, while continuous neck skin surface temperature monitoring in lactating sows was carried out using Bluetooth sensor technology. Heat stress was evident over an extended period, as indicated by increased temperature in lactating sows, compromised welfare, oxidative stress, reduced antioxidant capacity, poor reproductive and productive performance, decreased daily growth rate, and higher mortality-related indices. Histopathological examination also revealed multisystemic lesions, including fibrinous microthrombi in renal vessels, hepatocellular degeneration, perivascular oedema with vascular wall thickening in the skin, and lymphocyte depletion in splenic germinal centres. These findings are consistent with endothelial dysfunction, ischaemic tissue damage, and stress-induced immunomodulation. In conclusion, heat stress causes multi-dimensional biological and productive alterations in pigs under intensive farming systems, involving thermoregulatory, oxidative, welfare, reproductive, and histopathological impairments, which support the implementation of integrated precision livestock farming monitoring approaches. Full article

Concussion is associated with persistent motor control deficits that may not be detected using standard clinical assessments. This study examined differences in average velocity, average step length, and single- and double-support percentages during gait under increasing task demands in individuals with a history of concussion. Sixty participants aged 18 to 35 years were recruited, including 32 individuals with a concussion within the past 3 to 18 months and 28 healthy controls. Gait data were collected using an instrumented pressure-sensitive walkway across four conditions: single-task and dual-task walking, with and without obstacles. Repeated-measures analyses of covariance were used to assess group and condition effects, with sex as a covariate, which showed a significant group-by-condition effect for step length, single-support percentage, and double-support percentage. These findings identify step length, single-support percentage, and double-support percentage as candidate sensor-derived gait biomarkers for detecting persistent post-concussion motor control alterations. The results directly support the use of pressure-based gait sensing to quantify deficits missed by conventional clinical measures; however, future work is needed to determine whether these features translate to wearable or real-world monitoring systems. Full article

Nowadays, the medical sector challenges young research teams to develop and propose new non-invasive diagnostic methods. As a potential response, gas sensors for biomarker detection in exhaled breath show promising results. In this paper, various gas sensors based on metal–oxide semiconductors and coated with different polymers are proposed, demonstrating the potential of these sensors in breathomics and health breath tests. The proposed sensors are based on TiO₂ sensing structures and are tuned through different methods. Furthermore, they are coated with polymers such as PV4D4, PTFE, PV3D3, and copolymers such as P(V3D3 + TFE). These polymers show improved efficiency for gas sensing structures as they act as filters for certain molecules. Full article

Purpose: This study aimed to develop and validate a comprehensive multi-sensor integrated platform for objective assessment of skeletal muscle recovery kinetics following exercise-induced muscle damage (EIMD), combining biomechanical, thermal, and biochemical monitoring modalities. Methods: Forty elite male athletes were randomized to microwave diathermy (MWD, n = 20, 2.45 GHz, 160 W, 45 min/session) or control (n = 20) groups. Time-synchronized multi-sensor assessments at baseline, 24 h, 48 h, and 72 h post-EIMD included: biomechanical sensors (knee flexion range of motion via goniometry and isokinetic peak torque), thermal sensor (skin surface temperature via infrared thermometry), and biochemical sensor array (serum CK, IL-6, and CRP via high-sensitivity ELISA). Two-way repeated-measures ANOVA with Bonferroni correction examined group × time interactions across all sensor channels. Results: Pre-study validation confirmed high reliability across all sensor modalities. Cross-modality concordance analysis revealed significant correlations between biomechanical and biochemical recovery trajectories (isokinetic torque vs. IL-6: r = −0.73, p < 0.001; pain vs. IL-6: r = 0.68, p < 0.001). MWD intervention demonstrated accelerated recovery across all sensor channels: complete ROM recovery by 48 h (MWDG post-2 vs. baseline, p > 0.05; CG post-3 43% below baseline, p < 0.001), complete isokinetic torque restoration by 72 h (MWDG post-3 vs. baseline, p > 0.05; CG 44% below baseline, p < 0.001), and near-complete pain resolution (VAS 1.70 ± 2.50 mm, p < 0.05). Biomarker sensors demonstrated differential recovery kinetics: IL-6 normalized by 48 h (1.52 ± 0.14 pg/mL, p > 0.05 vs. baseline), CRP approached baseline by 72 h (0.73 ± 0.24 mg/L, p > 0.05), while CK remained elevated at post-3 (169.70 ± 22.58 U/L, 30% above baseline, p < 0.001), indicating incomplete myofiber membrane integrity recovery despite resolution of systemic inflammatory markers. The control group exhibited persistent deficits across all sensor channels with no clinically meaningful recovery. Conclusions: This study validated an integrated multi-sensor platform for recovery assessment. Microwave diathermy demonstrated efficacy by 72 h with complete functional recovery and inflammatory normalization (though CK remained elevated). Cross-modality concordance (r = −0.73 to 0.68) confirmed superior assessment compared to single-modality approaches. This laboratory-based methodology provides a framework for future portable sensor systems in athletic surveillance. Full article

Dental caries remains one of the most prevalent yet preventable non-communicable diseases worldwide, disproportionately affecting populations with limited access to dental care and persistent socioeconomic inequalities. Early-stage lesions frequently remain undetected because of their asymptomatic nature, inadequate screening infrastructure, and the absence of continuous monitoring technologies, resulting in preventable complications and increased healthcare costs. To address these challenges, this study proposes an Internet of Things (IoT)-enabled intraoral wearable sensing device (I-OWSD) for continuous, quantitative, real-time monitoring of biomarkers associated with caries progression. The proposed framework integrates intraoral wearable sensing, cloud-based telemedicine services, and artificial intelligence (AI)-assisted analytics to support preventive oral healthcare and remote clinical decision-making. Two primary contributions are presented. First, a fractional-order delay-type model (FODM) based on the Caputo–Fabrizio derivative is proposed to capture the memory-dependent and nonlocal dynamics of caries progression. Mathematical analysis establishes the model’s non-negativity, boundedness, existence, uniqueness, and stability properties. Second, a biocompatible intraoral sensor interface is designed to enable continuous data acquisition and secure wireless communication with digital health platforms. Simulation results based on the proposed FODM suggest that, under an estimated adoption rate of 67.49%, the I-OWSD framework could reduce caries prevalence by approximately 15% while improving opportunities for early intervention and preventive care. The findings demonstrate the potential of combining fractional-order modelling, wearable sensing, and AI-driven teledentistry to advance continuous oral health monitoring and preventive dental care. Full article

Neuropeptide Y (NPY) is a stress-modulating neuropeptide and a promising biomarker for non-invasive assessment. Herein, a sensitive electrochemical aptasensor was developed on reduced graphene oxide/gold nanoparticle (rGO/AuNP)-modified screen-printed electrodes for selective NPY detection. A methylene blue (MB)-labeled NPY-specific aptamer was immobilized on the electrode surface through Au–S chemistry, and square-wave voltammetry (SWV) was used for signal readout. The rGO/AuNP-modified interface provided high conductivity and a large effective surface area, facilitating electron transfer and probe immobilization. Under optimized conditions, the aptasensor exhibited a linear detection range of 10–10,000 pg mL⁻¹ in PBS with a low detection limit of 1.17 pg mL⁻¹ and good linearity (R² = 0.991). In addition, the sensor showed satisfactory selectivity, reproducibility, and mechanical stability. Recovery tests in artificial sweat yielded recoveries of 91.8–107.8% with relative standard deviations below 5%, demonstrating good analytical accuracy in complex matrices. Combined with an agarose-hydrogel-assisted sampling interface and a reverse-iontophoresis-compatible wearable platform, this low-cost and facile sensing strategy provides a portable proof-of-concept approach for NPY analysis in artificial sweat and shows potential for future wearable-oriented biofluid monitoring. Full article

Cortisol, as a crucial biomarker reflecting psychological stress and physiological status, requires rapid and sensitive detection for health assessment and disease diagnosis. Conventional methods are time-consuming, operationally complex, and costly, limiting their use for point-of-care testing. This study reports a flexible, aptamer-based capacitive biosensor that exploits alternating current electrokinetics for ultrafast detection of cortisol in small-volume samples. Aptamers are immobilized via Au-S self-assembly on gold interdigitated electrodes on a PET substrate, and ACEK-induced fluid motion and dielectrophoresis rapidly enrich cortisol at the electrode interface, producing measurable interfacial capacitance changes ΔC/C₀. The experimental results demonstrate that the sensor achieves a detection limit of 0.337 ng/mL in artificial sweat, with a response time within 1 min and a good linear response across the concentration range of 1 to 1000 ng/mL. Requiring only 10 μL of sample, the sensor exhibits good repeatability, specificity, and interference resistance, making it suitable for rapid cortisol level detection. To enhance detection stability, this study designed and integrated a microfluidic chip, enabling efficient sample delivery and stable detection. The system demonstrates strong interference resistance, revealing potential applications in health management and disease monitoring. Full article

We developed a smartphone-assisted digital image-based optical biosensor array using a planar glass slide with sensor spots in a 2 × 5 array format for point-of-care multiplex detection of biomarkers. The detection is based on the integration of the capture antibody (Ab^C)-functionalized sensor array with a detection antibody-conjugated gold nanoparticle bioconjugate (AuNP@Ab^D) in the presence of interleukin-8 (IL8) to form a sandwich-type AuNP@Ab^D–IL8–Ab^C nanocomplex on the sensing spot surface. Thus, the colorimetric detection method can be applied to the quantitative analysis of IL8, a clinically relevant pro-inflammatory and pro-angiogenic biomarker. The sensing strategy utilizes digital image-based analysis via ImageJ software (V 1.54 g; Java 1.8.0_345 [64 − bit], Windows 8) to quantify the colorimetric signals generated by the light absorbance of surface-bound gold nanoparticles in response to an IL8 droplet sample of merely 8 μL on the planar glass surface, achieving a low detection limit of 0.23 pg/mL (27 fM) and good reproducibility with a coefficient of variation of 0.95%. Validation using IL8-spiked serum at concentrations of 1 × 10⁻⁹ M and 1 × 10⁻¹⁰ M showed minimal matrix effects with a detection accuracy of 99.5% and 106.1%, respectively. Hence, this low-cost portable digital image-based plasmonic nanoparticle-linked immunosorbent assay serves as an alternative to traditional enzyme-linked immunosorbent assays. Full article

Attempting to detect and improve the management of Ketosis, the objective of this study was to determine and confirm the relationship between hours of activity, rumination time, conductivity, and milk production with the presence of ketosis in cows during the transition period in dairy cows in the Comarca Lagunera region, the heart of the dairy cattle production in Mexico. Data were collected in a large scale dairy cattle study. High-precision electronic collar sensors, high-precision electronic scales, and online electronic weighing sensors were employed to determine activity and ruminating time, milk electrical conductivity, and milk yield, respectively. All data were collected and integrated using an electronic peripheral management and control software. Using urinary ketone bodies measured by qualitative strips as the biomarker for ketosis, 10.50% of the cows were found to be positive for ketosis, while the remaining 89.50% were negative. The mean and standard error for activity time (AT), ruminating time (RT), milk electrical conductivity (CE) and milk yield (MY) in normal (N) vs ketotic (P) cows were: AT N 61.38, ± 0.39, AT P 39.08 ± 0.49; RT N 530.85 ± 2.94, RT P 295.24 ± 10.69; CE N 5.68 ± 0.03, CE P 9.13 ± 0.11; and MY N 38.87 ± 0.29, MY P 20.34 ± 0.54. Exploratory Factor Analysis (EFA) was conducted for the purpose of uncovering the underlying structure of the data by identifying latent constructs that influence the observed variables. The EFA estimated two factors which explained 62% of the variation observed. The Factor 1 (MR1) comprising the variables MY and EC, and Factor 2 (MR2), which consists the variables AT and RT. High-precision measurement sensors along multivariable analyses could facilitate the establishment of a correlation between ketosis and variables associated with the physiology, well-being, and productivity of bovines in the transition period. It further open the possibility of early detection of metabolic diseases such as ketosis. Full article

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms. Full article

Background/Objectives: Automated insulin delivery (AID) systems have improved the management of type 1 diabetes (T1D), but exercise and nutrition remain challenging because they rapidly alter glucose flux, substrate oxidation, hepatic glucose output, insulin requirements, and fuel availability. This narrative review aimed to synthesize current evidence on the interaction between AID systems, physical activity, and nutritional strategies from a metabolism-oriented perspective. Methods: A narrative bibliographic approach was used to integrate evidence from clinical trials, observational studies, technical studies, consensus statements, and reviews involving people with T1D across different life stages, including pediatric, adolescent, adult, and pregnancy-related contexts, when available. The review focused on AID systems, exercise physiology, nutritional strategies, meal announcement, bolus adjustment, dual-hormone systems, metabolic biomarkers, and emerging metabolomic approaches. Results: AID systems generally improve time in range and reduce hypoglycemia across several user groups, although most exercise- and nutrition-specific evidence comes from adult and pediatric/adolescent cohorts rather than pregnancy-specific exercise studies. Exercise-related glucose responses remain highly dependent on user input, exercise modality, insulin on board, meal timing, and metabolic state. Planned exercise announcement, prandial bolus reduction before postprandial activity, and individualized carbohydrate intake remain key strategies. Biomarkers such as lactate, ketone bodies, non-esterified fatty acids, and counter-regulatory hormones may help explain interindividual variability and support future personalization. Conclusions: Nutrition and exercise management in AID users should be interpreted as a dynamic metabolic interface among exogenous insulin, endogenous counter-regulation, substrate availability, and algorithmic control. Emerging approaches, including activity sensors, adaptive algorithms, dual-hormone systems, digital twins, and metabolomics-informed personalization, may improve safety and reduce user burden, but several remain exploratory and require further validation in diverse free-living conditions. Full article