Search Results (5,210)

Background: The global rise in the aging population presents significant challenges to healthcare systems, especially with increasing rates of chronic illnesses, mental health issues, and functional decline among older adults. In response, holistic and tech-driven approaches, such as telehealth and remote virtual interactive agents (VIAs), are potential emerging solutions to support the physical, cognitive, and emotional well-being of older adults. VIAs are multimodal digital tools that provide interactive and immersive experiences to users. Despite its promise, gaps still exist in the insights that explore ways of delivering geriatric healthcare remotely. Objective: This systematic review examines the existing literature on remote virtual interventions for older adults, focusing on bibliometrics, study purposes, outcomes, and network analysis of studies extracted from major databases using selected keywords and managed using the Covidence application. Methods and Results: Following five stages, namely, problem identification, a literature search, data evaluation, data analysis, and presentation, the review found that the studies on remote VIAs for older adults (2013–2025) were mostly from a positivist perspective, multi-authored, and U.S.-led, mainly showing positive outcomes for most studies (n = 13/15) conducted in home settings with healthy older participants. The dominance of positivist, US-led studies reflect an epistemological stance that emphasizes objectivity, quantification, and generalizability. VIAs, often pre-programmed and internet-based, supported health promotion and utilized visual humanoid avatars on personal devices. Keyword and network analysis additionally revealed four themes resulting from the review: Health and Clinical, Holistic and Cognitive, Home and Caring, and Hybrid and Connection. Conclusion: The review provides innovative insights and illustrations that may serve as a foundation for future research on VIAs and remote healthcare delivery for older adults. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 seconds, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM) , and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

Burn wounds are complex injuries that require timely and accurate assessment to guide treatment decisions and improve healing outcomes. Traditional clinical evaluations are largely subjective, often leading to delays in intervention and increased risk of complications. Imaging technologies have emerged as valuable tools that enhance diagnostic accuracy and enable objective, real-time assessment of wound characteristics. This review aims to evaluate the range of imaging modalities currently applied in burn wound care and assess their clinical relevance, diagnostic accuracy, and cost-effectiveness. It explores how these technologies address key challenges in wound evaluation, particularly related to burn depth, perfusion status, bacterial burden, and healing potential. A comprehensive narrative review was conducted, drawing on peer-reviewed journal articles, NICE innovation briefings, and clinical trial data. The databases searched included PubMed, Ovid MEDLINE, and the Cochrane Library. Imaging modalities examined include Laser Doppler Imaging (LDI), Fluorescence Imaging (FI), Near-Infrared Spectroscopy (NIR), Hyperspectral Imaging, Spatial Frequency Domain Imaging (SFDI), and digital wound measurement systems. The clinical application and integration of these modalities in UK clinical practice were also explored. Each modality demonstrated unique clinical benefits. LDI was effective in assessing burn depth and perfusion, improving surgical planning, and reducing unnecessary procedures. FI, particularly the MolecuLight i:X device (MolecuLight Inc., Toronto, ON, Canada), accurately identified bacterial burden and guided targeted interventions. NIR and Hyperspectral Imaging provided insights into tissue oxygenation and viability, while SFDI enabled early detection of infection and vascular compromise. Digital measurement tools offered accurate, non-contact assessment and supported telemedicine use. NICE recognized both LDI and MolecuLight as valuable tools with the potential to improve outcomes and reduce healthcare costs. Imaging technologies significantly improve the precision and efficiency of burn wound care. Their ability to offer objective, non-invasive diagnostics enhances clinical decision-making. Future research should focus on broader validation and integration into clinical guidelines to ensure widespread adoption. Full article

Antibacterial surfaces inspired by biological micro- and nanostructures, such as those found on the wings of cicadas and dragonflies, have attracted interest due to their ability to inhibit bacterial adhesion and damage microbial membranes without relying on chemical agents. However, conventional fabrication techniques like photolithography or nanoimprinting are limited by substrate shape, size, and high operational costs. In this study, we developed a scalable method using atmospheric-pressure low-temperature plasma (APLTP) to fabricate sharp-edged nanospikes on solvent-cast polymethyl methacrylate (PMMA) films. The nanospikes were formed through plasma-induced modification of pores in the film, followed by annealing to control surface wettability while maintaining structural sharpness. Atomic force microscopy confirmed the formation of micro/nanostructures, and contact angle measurements revealed reversible hydrophilicity. Antibacterial performance was evaluated against Escherichia coli using ISO 22196 standards. While the film with only plasma treatment reduced bacterial colonies by 30%, the film annealed after plasma treatment achieved an antibacterial activity value greater than 5, with bacterial counts below the detection limit (<10 CFU). These findings demonstrate that APLTP offers a practical route for large-area fabrication of biomimetic antibacterial coatings on flexible polymer substrates, holding promise for future applications in healthcare, packaging, and public hygiene. Full article

Background: Skin cancer represents one of the most prevalent malignancies worldwide, with melanoma accounting for approximately 75% of skin cancer-related deaths despite comprising fewer than 5% of cases. Early detection dramatically improves survival rates from 14% to over 99%, highlighting the urgent need for accurate and accessible diagnostic tools. While deep learning has shown promise in dermatological diagnosis, existing approaches lack clinical explainability and deployable interfaces that bridge the gap between research innovation and practical healthcare applications. Methods: This study implemented a comprehensive multimodal deep learning framework using the HAM10000 dataset (10,015 dermatoscopic images across seven diagnostic categories). Three CNN architectures (DenseNet-121, EfficientNet-B3, ResNet-50) were systematically compared, integrating patient metadata, including age, sex, and anatomical location, with dermatoscopic image analysis. The first implementation of XRAI (eXplanation with Region-based Attribution for Images) explainability for skin lesion classification was developed, providing spatially coherent explanations aligned with clinical reasoning patterns. A deployable web-based clinical interface was created, featuring real-time inference, comprehensive safety protocols, risk stratification, and evidence-based cosmetic recommendations for benign conditions. Results: EfficientNet-B3 achieved superior performance with 89.09% test accuracy and 90.08% validation accuracy, significantly outperforming DenseNet-121 (82.83%) and ResNet-50 (78.78%). Test-time augmentation improved performance by 1.00 percentage point to 90.09%. The model demonstrated excellent performance for critical malignant conditions: melanoma (81.6% confidence), basal cell carcinoma (82.1% confidence), and actinic keratoses (88% confidence). XRAI analysis revealed clinically meaningful attention patterns focusing on irregular pigmentation for melanoma, ulcerated borders for basal cell carcinoma, and surface irregularities for precancerous lesions. Error analysis showed that misclassifications occurred primarily in visually ambiguous cases with high correlation (0.855–0.968) between model attention and ideal features. The web application successfully validated real-time diagnostic capabilities with appropriate emergency protocols for malignant conditions and comprehensive cosmetic guidance for benign lesions. Conclusions: This research successfully developed the first clinically deployable skin lesion classification system combining diagnostic accuracy with explainable AI and practical patient guidance. The integration of XRAI explainability provides essential transparency for clinical acceptance, while the web-based deployment democratizes access to advanced dermatological AI capabilities. Comprehensive validation establishes readiness for controlled clinical trials and potential integration into healthcare workflows, particularly benefiting underserved regions with limited specialist availability. This work bridges the critical gap between research-grade AI models and practical clinical utility, establishing a foundation for responsible AI integration in dermatological practice. Full article

With the continuous development of wireless communication technology, the demand for wearable communication devices has rapidly increased. The antenna is one of the key components in communication devices, directly affecting the performance of wearable communication devices. As a result, wearable antenna design has become a research hotspot in recent years. Wearable antennas are widely used in various fields of daily life, including healthcare, sports and entertainment, the internet of things (IoT), and military positioning. In the last decade, related researchers have studied wearable antennas from various perspectives, and this paper summarizes the design and fabrication of wearable antennas more comprehensively and systematically. This review covers material selection, manufacturing techniques, miniaturization technologies, and performance metrics, while addressing key design considerations. It also highlights recent research, applications in critical fields, and future development trends, offering valuable insights for the design and study of wearable antennas. Full article

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics. Full article

This review article provides an introductory overview of organic bioelectronics, focusing on the creation of electrical devices that use specialized carbon-based semiconducting materials to interact successfully with biological processes. These organic materials demonstrate flexibility, biocompatibility, and the capacity to carry both electrical and ionic impulses, making them an ideal choice for connecting human tissue with electronic technology. The review study examines diverse materials, such as the conductive polymers Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and Polyaniline (PANI), along with critical devices like organic electrochemical transistors (OECTs), which are exceptionally efficient for sensitive biosensing applications. Significant applications include implanted neural interfaces for the brain and nerves, wearable health monitoring, tissue engineering scaffolds that facilitate tissue repair, and sophisticated drug delivery systems. The review acknowledges current challenges, including long-term stability and safety, while envisioning a future where these technologies revolutionize healthcare, human–machine interaction, and environmental monitoring via continuous multidisciplinary innovation. Full article

The growing digitization of healthcare has amplified concerns about the privacy and security of medical images, as conventional encryption methods often fail to provide sufficient protection. To address this gap, we propose a privacy-enhancing image encryption algorithm that integrates SHA-256 hashing, block-wise processing (16 × 16 with zero-padding), DNA encoding with XOR operations, and logistic map-driven key generation into a unified framework. This synergistic design balances efficiency and robustness by embedding data integrity verification, ensuring high sensitivity to initial conditions, and achieving strong diffusion through dynamic DNA rules. Experimental results confirm that the scheme achieves high NPCR (0.997), UACI (0.289), entropy (7.995), and PSNR (27.89 dB), outperforming comparable approaches while maintaining scalability to large image formats and robustness under compression (JPEG quality factors 90 and 70). These findings demonstrate that the proposed method offers an efficient and resilient solution for securing medical images, ensuring confidentiality, integrity, and practical applicability in real-world healthcare environments. Full article

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article

The insect gut contains a complex and diverse microbial community, and the composition of the insect gut microbial community is influenced by multiple factors such as the host’s genetics, dietary habits, and the external environment. The host’s immune system maintains the stability and balance of the microbial community through a number of mechanisms. The microorganisms in this community play key roles in the nutrient metabolism, detoxification, immune regulation, development, and behaveior of insects. In recent years, the relevant literature has reported advances in the study of insect gut microbes, indicating the potential applications of insect gut microbes in several fields. The aim of this review is to provide a comprehensive overview of the current information on the structure of insect gut microbial communities and complex host–microbe–environment interactions. The diversity of insects’ gut microbial communities and the functions of their gut microbes are revealed. By studying insect gut microbial communities, we can gain insights into the functions of these microbes in the host and explore the causal relationships between them and the host’s physiology and behavior. This will not only help us to understand the mechanism of action of the microbiome, but also provide a basis for the development of innovative biotechnology based on insect gut microbes. This research has significant theoretical value in academia and also has a wide range of applications in agriculture, environmental protection, industrial production, and healthcare. Full article

Bupleurum is a Chinese medicinal material widely used in clinical practice. Its medicinal component is the dried roots of either the Umbrella plant Bupleurum chinense DC or Bupleurum scorzonerifolium Willd. This review systematically searched major scientific databases such as Web of Science, PubMed, and ScienceDirect, and found that it contains various bioactive substances including saikosaponins, polysaccharides, flavonoids, and volatile oils. These components have demonstrated significant efficacy in anti-tumor, anti-inflammatory, and neuroprotective activities. Research has confirmed that this medicinal herb can exert its pharmacological effects by promoting tumor cell apoptosis, inhibiting cell proliferation, regulating inflammatory signaling pathways, and alleviating neuroinflammation. Additionally, its antipyretic and antiviral properties have also garnered widespread attention. However, clinical data regarding its optimal dosage, administration routes, and safety are still insufficient, necessitating further trials for validation. Investigating the synergistic effects of Bupleurum with other drugs and the safety of its use in different populations are also key directions of current research. Given the urgent need for efficient and sustainable healthcare in modern society, a deep understanding of the mechanisms and safety of Bupleurum is of significant importance for its validation as a foundation for new drug development. In summary, Bupleurum, as a multifunctional natural product, has broad application prospects and is expected to play a greater role in future medical research and clinical practice. Full article

Electronic health record (EHR) data breaches create severe concerns for patients’ privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive data, requiring integration, implementation, and the application of essential security principles, controls, and strategies to safeguard against persistent adversary attacks. This research is an exploratory study into current integrated EHR cybersecurity attacks using United States Health Insurance Portability and Accountability Act (HIPAA) privacy and security breach reported data. This work investigates if current EHR implementation lacks the requisite security control to prevent a cyber breach and protect user privacy. We conduct descriptive and trend analysis to describe, demonstrate, summarize data points, and predict direction based on current and historical data by covered entity, type of breaches, and point of breaches (examine, attack methods, patterns, and location of breach information). An Autoregressive Integrated Moving Average (ARIMA) model is used to provide a detailed analysis of the data demonstrating breaches caused by hacking and IT incidents show a significant trend (coefficient 0.84, p-value < 2.2 × 10⁻¹⁶ ***). The findings reveal a consistent rise in breaches—particularly from hacking and IT incidents—disproportionately affecting healthcare providers. The study highlights that EHR data breaches often follow recurring patterns, indicating common vulnerabilities, and underlines the need for prioritized, data-driven security investments. These findings validate the hypothesis that most EHR cybersecurity attacks are concentrated using similar attack methodologies and face common vulnerabilities and demonstrate the value of targeted mitigation strategies to strengthen healthcare cybersecurity. The findings highlight the urgent need for healthcare organizations and policymakers to prioritize targeted, data-driven security investments and enforce stricter controls to protect EHR systems from increasingly frequent and predictable cyberattacks. Full article

Cardiovascular diseases are known as major contributors to death globally. Accurate identification and classification of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for early diagnosis and treatment of cardiovascular diseases. This research introduces an innovative deep learning architecture that integrates Convolutional Neural Networks with a channel attention mechanism, enhancing the model’s capacity to concentrate on essential aspects of the ECG signals. Unlike most prior studies that depend on single-lead data or complex hybrid models, this work presents a novel yet simple deep learning architecture to classify five arrhythmia classes that effectively utilizes both 2-lead and 12-lead ECG signals, providing more accurate representations of clinical scenarios. The model’s performance was evaluated on the MIT-BIH and INCART arrhythmia datasets, achieving accuracies of 99.18% and 99.48%, respectively, along with F1 scores of 99.18% and 99.48%. These high-performance metrics demonstrate the model’s ability to differentiate between normal and arrhythmic signals, as well as accurately identify various arrhythmia types. The proposed architecture ensures high accuracy without excessive complexity, making it well-suited for real-time and clinical applications. This approach could improve the efficiency of healthcare systems and contribute to better patient outcomes. Full article