Search Results (63)

Accurate assessment of extraocular muscle mechanics is crucial for diagnosing and treating ocular motility disorders, yet current methods, such as the forced duction test, rely on subjective tactile sensation and gross visual observation. To overcome the limitations of subjectivity and the impracticality of previous quantitative devices, we developed a novel biosensing system capable of simultaneously and objectively measuring passive ocular tension and rotation angle during forced duction. The system integrates custom-engineered surgical forceps equipped with dual strain gauges and an infrared video camera that precisely tracks pupil displacement to calculate real-time rotation angle. We clinically validated this system in a prospective study involving 10 patients (20 eyes) with intermittent exotropia, with measurements performed under general anesthesia. Reliable tension–angle curves were successfully obtained in all cases without complications. Passive tension increased progressively with ocular rotation, following a linear-parabolic trajectory up to 40°. The mean duction force of the medial and lateral rectus muscles showed comparable symmetry. This lightweight, practical, and objective biosensing system offers a reliable tool for quantifying ocular mechanics, with the potential to enhance diagnostic accuracy, enable individualized surgical planning, and support fundamental research in ocular motility disorders. Full article

Capillary-driven microfluidic chips have emerged as promising platforms for point-of-care diagnostics, offering portable, inexpensive, and pump-free operation. Accurate tracking of cell flow in these systems is vital for quantitative applications such as on-chip cytometry, cell counting, and biomechanical analysis. However, tracking in capillary-driven devices is challenging due to rapid cell displacements, flow instabilities, and visually similar cells. Under these conditions, conventional tracking algorithms such as TrackPy, TrackMate, SORT, and DeepSORT exhibit frequent identity switches and trajectory fragmentation. Here, we introduce FloCyT, a robust, high-speed centroid tracking tool specifically designed for capillary-driven and microfluidic flow. FloCyT leverages microchannel geometry for tracking and uses anisotropic gating for association, global flow-aware track initialisation, and channel-specific association. This enables precise tracking even under challenging conditions of capillary-driven flow. FloCyT was evaluated on 12 simulated and 4 real patient datasets using standard multi-object tracking metrics, including IDF1 and MOTA, ID switches, and the percentage of mostly tracked objects. The results demonstrate that FloCyT outperforms both standard and flow-aware-modified versions of TrackPy and SORT, achieving higher accuracy, more complete trajectories, and fewer identity switches. By enabling accurate and automated cell tracking in capillary-driven microfluidic devices, FloCyT enhances the quantitative sensing capability of image-based microfluidic diagnostics, supporting novel, low-cost, and portable cytometry applications. Full article

Background: Genome sequencing is transforming infectious-disease diagnostics, surveillance, and precision therapy by enabling rapid, high-resolution pathogen identification, transmission tracking, and genomic-informed antimicrobial stewardship. Methods: We review contemporary sequencing platforms (short- and long-read), targeted and metagenomic approaches, and operational workflows that connect laboratory outputs to clinical and public health decision-making. We highlight strengths and limitations of genomic AMR prediction, the role of plasmids and mobile elements in resistance and virulence, and practical steps for clinical translation, including validation, reporting standards, and integration with electronic health records. Results: Comparative and population genomics reveal virulence determinants and host–pathogen interactions that correlate with clinical outcomes, improving risk stratification for high-risk infections. Integrating sequencing with epidemiological and clinical metadata enhances surveillance, uncovers cryptic transmission pathways, and supports infection control policies. Despite these advances, clinical implementation faces technical and interpretative barriers, as well as challenges related to turnaround time, data quality, bioinformatic complexity, cost, and ethical considerations. These issues must be addressed to realize routine, point-of-care sequencing. Conclusions: Emerging solutions, including portable sequencing devices, standardized pipelines, and machine-learning models, promise faster, more actionable results and tighter integration with electronic health records. The widespread adoption of sequencing in clinical workflows has the potential to shift infectious disease management toward precision medicine, thereby improving diagnostics, treatment selection, and public health responses. Full article

This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)₂ on Ni foam—stabilizing α-nanoflakes at 0.7 V·s⁻¹ and β-platelets at 0.007 V·s⁻¹—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm⁻² at 5 mA·cm⁻²) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring. Full article

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for the precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions. Full article

Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in emergency and intensive care settings. AI and ML algorithms can process complex biomedical data, improve diagnostic accuracy, and enable early disease detection for better patient outcomes. Predictive analytics in POC devices supports proactive healthcare by analyzing data to forecast health issues and facilitating early intervention and personalized treatment. This review covers the key areas of ML and AI integration in POC devices, including data analysis, pattern recognition, real-time decision support, predictive analytics, personalization, automation, and workflow optimization. Examples of current POC devices that use ML and AI include AI-powered blood glucose monitors, portable imaging devices, wearable cardiac monitors, AI-enhanced infectious disease detection, and smart wound care sensors are also discussed. The review further explores new directions for POC sensors and ML integration, including mental health monitoring, nutritional monitoring, metabolic health tracking, and decentralized clinical trials (DCTs). We also examined the impact of integrating ML and AI into POC devices on healthcare accessibility, efficiency, and patient outcomes. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

This study presents the development and the mechanical and clinical characterization of a flexible biodegradable chitosan–glycerol–graphite composite strain sensor for real-time respiratory monitoring, where the main material, chitosan, is derived and extracted from Tenebrio Molitor larvae shells. Chitosan was extracted using a sustainable, low-impact protocol and processed into a stretchable and flexible film through glycerol plasticization and graphite integration, forming a conductive biocomposite. The sensor, fabricated in a straight-line geometry to ensure uniform strain distribution and signal stability, was evaluated for its mechanical and electrical performance under cyclic loading. Results demonstrate linearity, repeatability, and responsiveness to strain variations in the stain sensor during mechanical characterization and performance, ranging from 1 to 15%, with minimal hysteresis and fast recovery times. The device reliably captured respiratory cycles during normal breathing across three different areas of measurement: the sternum, lower ribs, and diaphragm. The strain sensor also identified distinct breathing patterns, including eupnea, tachypnea, bradypnea, apnea, and Kussmaul respiration, showing the capability to sense respiratory cycles during pathological situations. Compared to conventional monitoring systems, the sensor offers superior skin conformity, better adhesion, comfort, and improved signal quality without the need for invasive procedures or complex instrumentation. Its low-cost, biocompatible design holds strong potential for wearable healthcare applications, particularly in continuous respiratory tracking, sleep disorder diagnostics, and home-based patient monitoring. Future work will focus on wireless integration, environmental durability, and clinical validation. Full article

Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa⁻¹ over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article

The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny. Full article

Recent advancements in wearable electronics have significantly enhanced human–device interaction, enabling applications such as continuous health monitoring, advanced diagnostics, and augmented reality. While progress in material science has improved the flexibility, softness, and elasticity of these devices for better skin conformity, their optical properties, particularly transparency, remain relatively unexplored. Transparent wearable electronics offer distinct advantages: they allow for non-invasive health monitoring by enabling a clear view of biological systems and improve aesthetics by minimizing the visual presence of electronics on the skin, thereby increasing user acceptance. Hydrogels have emerged as a key material for transparent wearable electronics due to their high water content, excellent biocompatibility, and tunable mechanical and optical properties. Their inherent softness and stretchability allow intimate, stable contact with dynamic biological surfaces. Furthermore, their ability to support ion-based conductivity is advantageous for bioelectronic interfaces and physiological sensors. Current research is focused on advancing hydrogel design to improve transparency, mechanical resilience, conductivity, and adhesion. The core components of transparent wearable systems include physiological sensors, energy storage devices, actuators, and real-time displays. These must collectively balance efficiency, functionality, and long-term durability. Practical applications span continuous health tracking and medical imaging to next-generation interactive displays. Despite progress, challenges such as material durability, scalable manufacturing, and prolonged usability remain. Addressing these limitations will be crucial for the future development of transparent, functional, and user-friendly wearable electronics. Full article

We present a compact and cost-effective real-time PCR system designed for point-of-care testing (POCT), utilizing a PCB-based disposable chip and an open-platform CMOS camera. The system integrates precise thermal cycling with software-synchronized fluorescence detection and provides real-time analysis through a dedicated user interface. To minimize cost and complexity, a polycarbonate reaction chamber was integrated with a PCB-based heater and thermistor. A slanted LED illumination setup and an open-platform USB camera were employed for fluorescence imaging. Signal alignment was enhanced using device-specific region-of-interest (ROI) tracking based on copper pad corner detection. Thermal cycling performance achieved a heating rate of 8.0 °C/s and a cooling rate of −9.3 °C/s, with steady-state accuracy within ±0.1 °C. Fluorescence images exhibited high dynamic range without saturation, and the 3σ-based ROI correction method improved signal reliability. System performance was validated using Chlamydia trachomatis DNA standard (10³ copies), yielding consistent amplification curves with a Ct standard deviation below 0.3 cycles. These results demonstrate that the proposed system enables rapid, accurate, and reproducible nucleic acid detection, making it a strong candidate for field-deployable molecular diagnostics. Full article

Orthopedics is undergoing a transformative shift driven by personalized medical technologies that enhance precision, efficiency, and patient outcomes. Virtual surgical planning, robotic assistance, and real-time 3D navigation have revolutionized procedures like total knee arthroplasty and hip replacement, offering unparalleled accuracy and reducing recovery times. Integrating artificial intelligence, advanced imaging, and 3D-printed patient-specific implants further elevates surgical precision, minimizes intraoperative complications, and supports individualized care. In sports orthopedics, wearable sensors and motion analysis technologies are revolutionizing diagnostics, injury prevention, and rehabilitation, enabling real-time decision-making and improved patient safety. Health-tracking devices are advancing recovery and supporting preventative care, transforming athletic performance management. Concurrently, breakthroughs in biologics, biomaterials, and bioprinting are reshaping treatments for cartilage defects, ligament injuries, osteoporosis, and meniscal damage. These innovations are poised to establish new benchmarks for regenerative medicine in orthopedics. By combining cutting-edge technologies with interdisciplinary collaboration, the field is redefining surgical standards, optimizing patient care, and paving the way for a highly personalized and efficient future. Full article

Liquid atomization has wide applications in jet-type and reciprocating engines, powder generation, cooling towers, and atmosphere dust removal. Droplet size and distribution are the decisive factors in the performance of the above applications. The rapid development and usage of computer science brings huge differences in the research manner of liquid atomization and has shed great light on the micro-phenomena of the formation, deformation, and rupture of liquid ligaments. However, the numerical methods of liquid atomization still lack efficiency due to their huge cost of computer resources and their accuracy due to their dependence on empirical correlations. Before achieving reliable implementation in atomization device design, such computational models must undergo rigorous validation against experimentally measured data acquired through advanced diagnostic techniques. The present paper reviews the mainstream numerical methods of liquid atomization including interface capturing, particle tracking, smoothed particle hydrodynamics, etc. Their respective numerical kernels and some representative simulation cases are summarized. The aim of the present review is to provide a general idea and future research orientation on the capabilities of modern computer and numerical models in calculating atomization and designing relative devices and hopefully guide future research to strive efficiently and productively. Full article

Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These devices enable non-invasive, real-time monitoring of biochemical, electrophysiological, and biomechanical signals, offering personalized and proactive healthcare solutions. In parallel, implantable BioMEMS have significantly enhanced long-term diagnostics, targeted drug delivery, and neurostimulation. From continuous glucose and intraocular pressure monitoring to programmable drug delivery and bioelectric implants for neuromodulation, these devices are improving precision treatment by continuous monitoring and localized therapy. This review explores the materials and technologies driving advancements in wearable and implantable BioMEMSs, focusing on their impact on chronic disease management, cardiology, respiratory care, and glaucoma treatment. We also highlight their integration with artificial intelligence (AI) and the Internet of Things (IoT), paving the way for smarter, data-driven healthcare solutions. Despite their potential, BioMEMSs face challenges such as regulatory complexities, global standardization, and societal determinants. Looking ahead, we explore emerging directions like multifunctional systems, biodegradable power sources, and next-generation point-of-care diagnostics. Collectively, these advancements position BioMEMS as pivotal enablers of future patient-centric healthcare systems. Full article