Search Results (21,690)

Implantable Cardioverter-Defibrillators (ICDs) are life-sustaining devices used in the prevention of sudden death in patients suffering from advanced cardiac diseases. Although improvements in ICD technology and monitoring capabilities have been made, existing techniques are still not effective in predicting accelerated battery drain, thereby causing unplanned generator replacement and clinically significant device-related events. In this study, machine learning techniques were employed in the assessment of the early detection of ICD battery depletion risk using the collected device interrogation reports. The dataset used consisted of 32 devices in the training set and nine in the testing set. In order to mitigate the problem of a small sample size, a GMM-based data augmentation technique was applied solely to the training data, and actual devices were used for the testing data. Five supervised models, including Logistic Regression, Random Forest, SVM, CatBoost, and a Neural Network (MLP), have been utilized using a repeated stratified cross-validation and a separate held-out test data set. All the models have been tested for their performance using classification metrics. All models demonstrated variable performance with wide confidence intervals due to limited sample size. Support vector machines showed the highest cross-validation discrimination 0.889 ± 0.203, though uncertainty remains substantial given the small datasets (n = 41). From the feature analysis, it was found that atrial sensing amplitude, RV/LV capture threshold, output settings, and implant duration were the most important features for the prediction of high battery depletion risk. These findings suggest that changes in device parameters and implant age are associated with elevated battery depletion risk, supporting the feasibility of telemetry-driven risk stratification for device management. Full article

This review evaluates the emerging role of circulating tumour cells (CTCs) as clinically meaningful, minimally invasive biomarkers for oral squamous cell carcinoma (OSCC). Despite advances in management, OSCC continues to demonstrate high morbidity and mortality, largely due to late diagnosis and the absence of validated biomarkers for early detection or real-time monitoring. Conventional diagnostic tools, tissue biopsy, and imaging provide only static snapshots and fail to capture tumour heterogeneity or evolving biological behaviour. CTCs offer a novel and significant opportunity to address these limitations. Key findings from recent studies highlight that CTC enumeration correlates with tumour burden, nodal metastasis, recurrence, and overall prognosis. Molecular and phenotypic characterisation further reveals dynamic traits such as epithelial–mesenchymal transition, stemness, and therapy resistance, providing insights into metastatic potential and treatment failure. Technological advances, including immunocytochemistry, microfluidic capture platforms, PCR-based assays, and next-generation sequencing, have enhanced the sensitivity and specificity of CTC detection and enabled detailed multi-omic profiling. Collectively, evidence suggests that integrating CTC analysis into OSCC clinical workflows could improve early detection, refine risk stratification, personalise therapeutic strategies, and support longitudinal monitoring of disease dynamics. As research progresses, CTC-based diagnostics represent a promising frontier in shifting OSCC management toward more precise, adaptive, and biologically informed care. Full article

With the increasing popularity of battery energy storage technology, safety issues have become increasingly important. The battery management system (BMS) is a key device for ensuring the safety of lithium-ion battery systems. While the BMS can effectively prevent faults such as external overheating, overload, or deep discharge, it cannot completely eliminate the possibility of internal short-circuit (ISC) faults—these faults may be caused by multiple factors, such as manufacturing defects. Therefore, reliable ISC detection or mitigation strategies need to be designed within the BMS to reduce the consequences of such faults. This study focuses on the critical role of the BMS in responding to thermal runaway (TR) and thermal propagation (TP) events caused by ISC faults and proposes an active energy-dissipation BMS control strategy. This strategy is compared with existing battery current interrupt device (CID) protection and threshold-type BMS protection schemes. A coupled electro-thermal simulation model was constructed based on thermal runaway test data of 280 Ah lithium iron phosphate batteries, and the proposed strategy was verified within this model. The proposed strategy can effectively suppress thermal propagation and thermal runaway in battery energy storage systems, providing a reference for the safety of battery energy storage systems (BESS). Full article

Shooting activities offer educational and recreational value; however, their application in school physical education and recreational settings remains limited due to safety concerns, high costs, and restricted access to specialized facilities and equipment. To address these constraints, this study designed and implemented a low-cost laser shooting system suitable for school physical education and recreational use. The proposed system comprises a laser-gun module, a physical electronic target providing immediate on-site feedback using an illuminance sensor, a Fresnel lens, and RGB LEDs, and a web-based electronic target that enables real-time scoring, logging, and visualization via smartphone or tablet cameras and browser-based processing. By adopting a low-power, projectile-free laser structure with pulse-limited emission, the system enhances operational safety, while the use of general-purpose components and web standards reduces cost and lowers barriers to adoption. Technical verification conducted under controlled indoor conditions demonstrated stable single-shot operation, reliable hit detection, and accurate score calculation for both the physical and web-based targets. Expert validation involving specialists in physical education, educational technology, and sports technology yielded consistently high evaluations across safety, cost efficiency, functional completeness, and field applicability. These findings suggest that the proposed system represents a practical and scalable alternative for school physical education classes and recreational programs. Future research should examine user-level usability, learning outcomes, system robustness under diverse environmental conditions, and structured expert consensus processes. Full article

This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare. Full article

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins. Full article

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems. Full article

Background: Retinal and optic nerve disorders are a leading cause of irreversible visual impairment worldwide. Advances in molecular genetics—including next-generation sequencing, genome-wide association studies, and gene-based therapeutic technologies—have reshaped understanding of both inherited and complex retinal diseases. However, translating genetic discovery into sustained clinical benefit remains biologically and practically constrained. Methods: A structured literature search was conducted using PubMed and Scopus to identify relevant studies published between 2015 and 2025. The search focused on molecular genetics, epigenetic modulation, mitochondrial biology, and translational applications in inherited retinal dystrophies and selected complex retinal diseases, prioritizing high-impact original research and systematic reviews addressing diagnostic innovation and therapeutic development. Results: Inherited retinal dystrophies represent the most advanced model of precision ophthalmology, with diagnostic yields approaching 70–80% in well-characterized cohorts. Gene augmentation and genome-editing strategies have demonstrated proof-of-concept efficacy, yet clinical benefit depends on residual cellular viability, delivery efficiency, and durability of expression. Emerging platforms include AAV-mediated gene transfer, in vivo CRISPR-based editing, RNA-directed splice modulation, and mitochondrial-targeted approaches. Persistent barriers include unresolved non-coding and structural variants, variant interpretation uncertainty, and endpoint selection in clinical trials. In contrast, complex retinal diseases such as glaucoma, age-related macular degeneration, and pathological myopia reflect polygenic susceptibility interacting with environmental and aging-related factors. Although polygenic risk scores refine probabilistic prediction, their utility is limited by ancestry bias and incomplete predictive performance. Epigenetic and mitochondrial mechanisms further modulate disease expression but remain largely non-actionable in routine practice. Conclusions: Retinal genetics has progressed from gene discovery to early therapeutic implementation. Future advances will depend on improved variant detection, functional validation, biomarker-guided staging, and integration of genomics with imaging and longitudinal modeling to achieve durable and equitable precision ophthalmology. Full article

Against the backdrop of the global transition toward clean and low-carbon energy systems, hydrogen has emerged as a promising alternative to fossil fuels owing to its carbon-free characteristics and broad cross-sector applicability. However, the high diffusivity and wide flammability range of hydrogen pose significant safety challenges for its large-scale deployment. Conventional detection methods are generally limited to point-based data acquisition and struggle to capture the transient flow-field characteristics associated with hydrogen diffusion as well as combustion or explosion processes. This review aims to systematically clarify the exclusive technical advantages of schlieren visualization technology for hydrogen research, summarize its application progress in hydrogen and hydrogen mixture diffusion distribution and combustion/explosion flow-field testing, and propose future optimization directions and application expansion paths. Schlieren visualization, based on optical refraction principles, has evolved from a traditional experimental technique into a comprehensive system adapted to diverse scenarios, including high-speed schlieren, Z-type schlieren, background-oriented schlieren (BOS), and color schlieren. Owing to its non-intrusive nature, high spatiotemporal resolution and full-field visualization capability, schlieren technology can directly observe the fundamental diffusion behavior of hydrogen jets and capture distinctive flow features throughout all stages of hydrogen mixture combustion and explosion. It effectively overcomes the limitations of conventional detection methods and has become an indispensable tool in hydrogen energy safety research. Future research should focus on improving technical performance, strengthening interdisciplinary integration with machine learning and digital twin technologies, and expanding application scenarios to multi-field coupling systems, so as to support the safe and efficient development of the hydrogen industry and contribute to global carbon peaking and carbon neutrality goals. Full article

Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Full article

Oral mucosal melanoma (OMM) is a rare and aggressive malignancy that differs markedly from cutaneous melanoma in terms of epidemiology, genetic characteristics, clinical presentation, and treatment response. Despite advances in understanding OMM pathogenesis and the development of novel therapeutic strategies, early diagnosis remains challenging due to its low prevalence, anatomically concealed locations, and frequent multifocality. This review emphasizes the importance of the early detection of OMM using non-invasive imaging methods—specifically dermoscopy and reflectance confocal microscopy (RCM)—and explores their potential role in guiding treatment decisions, preventing disease progression, and improving prognosis. A narrative review of the PubMed database was conducted using the terms “oral melanoma,” “oral melanoma dermoscopy,” and “oral melanoma reflectance confocal microscopy.” Seventy-two relevant review articles were included. In addition, two illustrative clinical cases from our practice are presented to demonstrate the diagnostic value of non-invasive imaging techniques. Although biopsy and histopathology remain the diagnostic gold standards, they are invasive, time-consuming, and may be poorly tolerated, particularly in patients with multifocal lesions. Dermoscopy and RCM provide real-time, high-resolution imaging that enables the detection of early tissue abnormalities not visible to the naked eye. These techniques show good correlation with clinical and histopathological findings, thereby enhancing diagnostic accuracy and facilitating follow-up without the need for repeated biopsies. In our cases, they were instrumental in identifying recurrence and guiding clinical management. However, several limitations should be considered, including restricted accessibility, anatomical constraints, and the requirement for specialized training and expertise. Non-invasive imaging techniques may support clinicians in the early recognition and evaluation of suspicious oral lesions; however, histopathologic examination remains essential for definitive diagnosis. Wider implementation and further technological refinement are needed to optimize their integration into clinical practice. Full article

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

Ultra-high-field (UHF) magnetic resonance imaging, defined as imaging at field strengths of 7 Tesla (7T) and above, represents a frontier technology in neuroimaging with emerging applications in prenatal brain research. This narrative review examines the current evidence on the potential benefits of UHF-MRI for investigating embryonic and fetal brain development. Through analysis of 97 studies identified across multiple databases, we find that UHF-MRI offers substantial advantages in spatial resolution, tissue contrast, and anatomical detail compared to conventional clinical field strengths (1.5T and 3T). The primary applications to date have been in ex vivo imaging of post-mortem fetal specimens and preclinical animal models, where UHF-MRI has enabled unprecedented visualization of laminar cortical organization, early sulcation patterns, microstructural development, and subtle anatomical features critical for understanding normal and abnormal neurodevelopment. Key benefits include enhanced delineation of transient developmental zones, improved characterization of cortical folding, superior detection of subtle malformations, and the ability to create high-resolution three-dimensional atlases of fetal brain development. However, significant technical and safety challenges currently limit in utero human applications, including concerns about specific absorption rate, acoustic noise, and fetal motion artifacts. This review identifies critical knowledge gaps and future directions for translating UHF-MRI technology to clinical prenatal diagnostics. Full article

Eutectic solvents have become a viable choice to create innovative pharmaceutical technologies within the framework of the green chemistry approach. Despite the growing applicative interest, a general gap remains in the pharmaceutical sector regarding thorough and systematic research of their properties and useful applications. In this work, eutectic solvents have been prepared from choline chloride and polyols (sorbitol, xylitol, mannitol, and isomalt) at different molar ratios (1:1, 2:3, and 3:2), characterised, and used for the solubility enhancement of poorly water-soluble drugs (ibuprofen and naproxen) as well as the potential drug candidate apigenin. The interactions between the eutectic solvent components were investigated by DSC, FTIR, and refractive index methods. In all eutectic solvents, the water content detected by Karl Fischer titration and loss on drying was less than 3%. Solubility studies, carried out using the shake-flask method, showed significant solubility enhancement of the following: ibuprofen: ~152-fold increase, naproxen: ~144-fold increase, and apigenin: ~188-fold increase. These findings highlighted the great potential of eutectic solvents as solubility enhancers in the development of novel and more effective drug delivery systems. Full article

Microplastics have become a ubiquitous environmental contaminant in natural waters, raising significant concerns regarding aquatic ecosystem health and potential human exposure. A comprehensive synthesis of current knowledge on microplastic pollution in freshwater and marine systems is presented, focusing on sources, distribution patterns, environmental behavior, and associated risks. In freshwater environments, microplastic inputs are closely linked to human activities and land use, with wastewater treatment plant effluent, urban runoff, and agricultural drainage serving as major pathways. In marine systems, microplastics undergo dynamic transport influenced by particle properties, hydrodynamic conditions, and biological interactions such as biofouling and aggregation, leading to widespread distribution from coastal zones to deep sea sediments. Importantly, the role of the freshwater–estuarine–marine continuum is emphasized, highlighting the coupled processes of transport, retention, and remobilisation that govern the spatiotemporal distribution and ultimate fate of microplastics across interconnected aquatic systems. Toxicological effects on aquatic organisms are further examined, particularly immunotoxicity and neurotoxicity, alongside potential human health risks via ingestion, inhalation, and dermal exposure. Attention is drawn to the discrepancy between experimental exposure conditions and environmentally relevant concentrations, which constrains robust risk assessment. Current mitigation strategies, including source reduction, wastewater treatment upgrades, transport interception, and degradation technologies, are critically evaluated in terms of effectiveness and limitations. A clear distinction is made between apparent removal and actual degradation, with further consideration of the environmental implications associated with sludge retention and degradation byproducts. Finally, key research priorities are identified, including the need for standardized detection methods, improved exposure assessment, development of environmentally benign alternatives, and strengthened policy-driven source control. These insights provide a basis for advancing sustainable management strategies for microplastic pollution in natural waters. Full article