Search Results (9,290)

Background/Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating complex disease with an elusive etiology, lacking objective diagnostic biomarkers. This study leverages advanced Automated Machine Learning (AutoML) to analyze plasma metabolomic and lipidomic profiles for the purpose of ME/CFS detection. Methods: We utilized a publicly available dataset comprising 888 metabolic features from 106 ME/CFS patients and 91 matched controls. Three AutoML frameworks—TPOT, Auto-Sklearn, and H2O AutoML—were benchmarked under identical time constraints. Univariate ROC and PLS-DA analyses with cross-validation, permutation testing, and VIP-based feature selection were applied to standardized, log-transformed omics data to identify significant discriminatory metabolites/lipids and assess their intercorrelations. Results: TPOT significantly outperformed its counterparts, achieving an area under the curve (AUC) of 92.1%, accuracy of 87.3%, sensitivity of 85.8%, and specificity of 89.0%. The PLS-DA model revealed a moderate but statistically significant discrimination between ME/CFS and controls. Explainable artificial intelligence (XAI) via SHAP analysis of the optimal TPOT model identified key metabolites implicating dysregulated pathways in mitochondrial energy metabolism (succinic acid, pyruvic acid, leucine), chronic inflammation (prostaglandin D₂, 11,12-EET), gut–brain axis communication (glycocholic acid), and cell membrane integrity (pc(35:2)a). Conclusions: Our results demonstrate that TPOT-derived models not only provide a highly accurate and robust diagnostic tool but also yield biologically interpretable insights into the pathophysiology of ME/CFS, highlighting its potential for clinical decision support and elucidating novel therapeutic targets. Full article

Background: Prostate cancer is one of the most common malignancies in men and a leading cause of cancer-related mortality. Early detection is essential to ensure curative treatment and favorable outcomes, but traditional diagnostic approaches—such as serum prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological confirmation following biopsy—are limited by suboptimal accuracy and variability. Multiparametric magnetic resonance imaging (mpMRI) has improved diagnostic performance but remains highly dependent on reader expertise. Artificial intelligence (AI) offers promising opportunities to enhance diagnostic accuracy, reproducibility, and efficiency in prostate cancer detection. Objective: To evaluate the diagnostic accuracy and reporting timeliness of AI-based technologies compared with conventional diagnostic methods in the early detection of prostate cancer. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between January 2015 and April 2025. Eligible designs included randomized controlled trials, cohort, case–control, and pilot studies applying AI-based technologies to early prostate cancer diagnosis. Data on AUC-ROC, sensitivity, specificity, predictive values, diagnostic odds ratio (DOR), and time-to-reporting were narratively synthesized due to heterogeneity. Risk of bias was assessed using the QUADAS-AI tool. Results: Twenty-three studies involving 23,270 patients were included. AI-based technologies achieved a median AUC-ROC of 0.88 (range 0.70–0.93), with median sensitivity and specificity of 0.86 and 0.83, respectively. Compared with radiologists, AI or AI-assisted readings improved or matched diagnostic accuracy, reduced inter-reader variability, and decreased reporting time by up to 56%. Conclusions: AI-based technologies show strong diagnostic performance in early prostate cancer detection. However, methodological heterogeneity and limited standardization restrict generalizability. Large-scale prospective trials are required to validate clinical integration. Full article

The rapid evolution of sequencing technologies has profoundly advanced precision oncology. Whole-exome sequencing (WES), whole-genome sequencing (WGS), and whole-transcriptome sequencing (RNA-Seq) enable comprehensive characterization of tumor biology by detecting actionable mutations, gene fusions, splice variants, copy number alterations, and pathway dysregulation. These approaches also provide critical insights into biomarkers such as homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI), which are increasingly essential for guiding therapeutic decisions. Importantly, comprehensive genomic profiling not only refines patient stratification for targeted therapies but also sheds light on tumor–immune interactions and the tumor microenvironment, paving the way for more effective immunotherapeutic combinations. WGS is considered the gold standard for detecting germline mutations and complex structural variants, while WES remains central for detecting somatic driver mutations that guide targeted therapies. RNA-Seq complements these methods by capturing gene expression dynamics, identifying clinically relevant fusions, and revealing mechanisms of resistance. Together with advances in bioinformatics and artificial intelligence, these tools translate molecular data into actionable strategies for patient care. This review integrates insights from WGS, WES, and RNA-Seq with an overview of FDA- and EMA-approved targeted therapies, organized by tumor type, and highlights the molecular signaling pathways that drive cancer development and treatment. By bridging genomic profiling with regulatory-approved therapies, we outline current advances and future perspectives in delivering personalized cancer care. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases ,COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article

Degenerative cervical myelopathy (DCM) is a common cause of spinal cord dysfunction in adults and is frequently accompanied by pain, a symptom that remains under-recognised despite its profound impact on quality of life. Conventional magnetic resonance imaging (MRI) is indispensable for identifying structural spinal cord compression; however, it is unable to detect early microstructural alterations, particularly those that may contribute to pain pathophysiology. This narrative review critically appraises the limitations of standard MRI in the diagnostic assessment of DCM and examines the expanding role of advanced imaging modalities—most notably diffusion tensor imaging (DTI)—in evaluating spinal cord integrity. DTI-derived parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), demonstrate sensitivity to axonal and myelin injury. For example, reductions in FA and AD have been linked to axonal disruption in sensory pathways, while elevations in RD suggest demyelination, a hallmark of neuropathic pain. Despite this potential, the widespread implementation of DTI is constrained by technical heterogeneity, limited accessibility, and the absence of standardised protocols. Future research priorities include the incorporation of pain-specific imaging endpoints, longitudinal validation across diverse cohorts, and integration with artificial intelligence frameworks to enable automated analysis and predictive modelling. Collectively, these advances hold promise for enabling earlier diagnosis, refined symptom stratification, and more personalised therapeutic strategies in DCM. Full article

As the global demand for water rises and climate pressures intensify, projections indicate that water scarcity will impact nearly 40% of the world’s population by 2030 and a quarter of all children by 2040. This study reviews the current literature on residential water efficiency, highlighting the most effective strategies for reducing water waste. A systematic literature review—guided by transparent criteria and quality assessments using the Critical Appraisal Skills Program (CASP)—was conducted to extract insights into water distribution management strategies. This study examines current smart water management initiatives aimed at reducing waste, with a particular focus on the policy and regulatory drivers behind global water conservation efforts. Furthermore, it shows innovative smart solutions such as Artificial Intelligence (AI)-powered forecasting, Internet of Things (IoT)-based metering, and predictive leak detection, which have demonstrated reductions in residential water loss by up to 30%, particularly through real-time monitoring and adaptive consumption strategies. The study concludes that innovative technologies must be actively supported and implemented by governments, utilities, and global organizations to proactively reduce water waste, safeguard future generations, and enable data-driven, AI-powered policy and decision-making for improved water use efficiency. Full article

With the development of artificial intelligence, intelligent assessment methods have been applied in post-earthquake emergency rescue. These methods enable rapid and accurate identification and localization of earthquake-induced damage to ceilings in large public buildings, which often serve as emergency shelters. However, in practical applications, challenges remain: damage recognition accuracy is low when using wide-field distant shots, while close-up local shots are unsuitable for identifying panoramic regional damage. As a result, high-precision intelligent safety assessment of the entire ceiling area cannot be achieved. Therefore, this study proposes a panoramic image stitching method based on SIFT feature point detection and registration, optimized by the RANSAC algorithm, to generate high-resolution, wide-angle panoramic images of ceilings in large public buildings. The BRISQUE values of the stitched images range between 20 and 30, indicating good stitching quality. Subsequently, by integrating damage recognition and image stitching techniques, a safety assessment test was conducted on 227 stitched images of earthquake-induced ceiling damage captured in real scenes, using evaluation indicators such as damage type and severity quantification. The safety assessment achieved an overall accuracy of 98.7%, demonstrating the effectiveness of ceiling damage detection technology based on image stitching. This technology enables intelligent post-earthquake safety assessment of ceilings in large public buildings across the entire area. Full article

Welding is a critical joining process in modern manufacturing, with defects contributing to 50–80% of structural failures. Traditional inspection methods are often inefficient, subjective, and inconsistent. To address challenges in weld defect detection—including scale variation, morphological complexity, low contrast, and sample imbalance—this paper proposes ADFE-DET, an adaptive dynamic feature enhancement algorithm. The approach introduces three core innovations: the Dynamic Selection Cross-stage Cascade Feature Block (DSCFBlock) captures fine texture features via edge-preserving dynamic selection attention; the Adaptive Hierarchical Spatial Feature Pyramid Network (AHSFPN) achieves adaptive multi-scale feature integration through directional channel attention and hierarchical fusion; and the Multi-Directional Differential Lightweight Head (MDDLH) enables precise defect localization via multi-directional differential convolution while maintaining a lightweight architecture. Experiments on three public datasets (Weld-DET, NEU-DET, PKU-Market-PCB) show that ADFE-DET improves mAP50 by 2.16%, 2.73%, and 1.81%, respectively, over baseline YOLOv11n, while reducing parameters by 34.1%, computational complexity by 4.6%, and achieving 105 FPS inference speed. The results demonstrate that ADFE-DET provides an effective and practical solution for intelligent industrial weld quality inspection. Full article

Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions. Full article

Background: Pneumothorax (PTX) requires rapid recognition in emergency and critical care. Lung ultrasound (LUS) offers a fast, radiation-free alternative to computed tomography (CT), but its accuracy is limited by operator dependence. Artificial intelligence (AI) may standardize interpretation and improve performance. Methods: This retrospective single-center study included 46 patients (23 with CT-confirmed PTX and 23 controls). Sixty B-mode and M-mode frames per patient were extracted using a Clarius C3 HD3 wireless device, yielding 2760 images. CT served as the diagnostic reference. Experimental studies were conducted within the framework of three scenarios. Transformer-based models, Vision Transformer (ViT) and DINOv2, were trained and tested under two scenarios: random frame split and patient-level split. Also, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were trained on the feature maps extracted by using Video Vision Transformer (ViViT) for ultrasound video sequences in Scenario 3. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve (AUC). Results: Both transformers achieved high diagnostic accuracy, with B-mode images outperforming M-mode inputs in the first two scenarios. In Scenario 1, ViT reached 99.1% accuracy, while DINOv2 achieved 97.3%. In Scenario 2, which avoided data leakage, DINOv2 performed best in the B-mode region (90% accuracy, 80% sensitivity, 100% specificity, F1-score 88.9%). ROC analysis confirmed strong discriminative ability, with AUC values of 0.973 for DINOv2 and 0.964 for ViT on B-mode images. Also, both RF and XGBoost classifiers trained on the ViViT feature maps reached 90% accuracy on the video sequences. Conclusions: AI-assisted LUS substantially improves PTX detection, with transformers—particularly DINOv2—achieving near-expert accuracy. Larger multicenter datasets are required for validation and clinical integration. Full article

International interests in bioaerosols have gained an increased attention to widen the knowledge pool of their identification, distribution, and quantification. Aeromycota signify a complex and diverse group of fungi dispersed through the atmosphere. Aeromycology is an important field of research due to its important role in human health. Aeromycoflora both indoors and outdoors, are responsible for many allergies and other respiratory diseases. The present review describes the diversity of the aeromycoflora, the techniques used for sampling, identification, and taxonomic classification, and the limitations of the traditional culture-based methods as they fail to detect unculturable species. Furthermore, the spatial and temporal variability in aeromycota complicate consistent monitoring. Both indoor and outdoor environments harbor airborne fungi. The diversity in indoor environments is greatly shaped by the moisture content, building design, and ventilation, which are further taken into consideration. Further, the health impacts of the indoor and outdoor fungi have been discussed and what control measures can be taken to reduce the exposure risks and management strategies that can be adopted. Artificial intelligence (AI) can bring revolution in this field of research and can help in improving detection, monitoring, and classification of airborne fungi. The review finally outlines the emerging role of AI in aeromycology. Full article

To address the challenges of variable target scale, complex background, blurred image, and serious occlusion in the yield detection of Yunnan large-leaf tea tree, this study proposes a deep learning network DE-YOLOv13-S that integrates the visual mechanism of primates. DynamicConv was used to optimize the dynamic adjustment process of the effective receptive field and channel the gain of the primate visual system. Efficient Mixed-pooling Channel Attention was introduced to simulate the observation strategy of ‘global gain control and selective integration parallel’ of the primate visual system. Scale-based Dynamic Loss was used to simulate the foveation mechanism of primates, which significantly improved the positioning accuracy and robustness of Yunnan large-leaf tea tree yield detection. The results show that the Box Loss, Cls Loss, and DFL Loss of the DE-YOLOv13-S network decreased by 18.75%, 3.70%, and 2.54% on the training set, and by 18.48%, 14.29%, and 7.46% on the test set, respectively. Compared with YOLOv13, its parameters and gradients are only increased by 2.06 M, while the computational complexity is reduced by 0.2 G FLOPs, precision, recall, and mAP are increased by 3.78%, 2.04% and 3.35%, respectively. The improved DE-YOLOv13-S network not only provides an efficient and stable yield detection solution for the intelligent management level and high-quality development of tea gardens, but also provides a solid technical support for the deep integration of bionic vision and agricultural remote sensing. Full article

The power system, as a critical national infrastructure, faces stealthy and persistent intrusions from Advanced Persistent Threat (APT) attacks. These attack chains span multiple stages and components, while heterogeneous data sources lack unified semantics, limiting the interpretability of current detection methods. To address this, we combine the W3C PROV-DM standard with power-specific semantics to map generic provenance data into standardized provenance graphs. On this basis, we propose a graph neural network framework that jointly models temporal dependencies and structural features. The framework constructs unified provenance graphs with snapshot partitioning, applies Functional Time Encoding (FTE) for temporal modeling, and employs a graph attention autoencoder with node masking and edge reconstruction to enhance feature representations. Through pooling, graph-level embeddings are obtained for downstream detection. Experiments on two public datasets show that our method outperforms baselines across multiple metrics and exhibits clear inter-class separability. In the context of scarce power-domain APT data, this study improves model applicability and interpretability, and it provides a practical path for provenance graph-based intelligent detection in critical infrastructure protection. Full article

Background: Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive imaging technique providing high-resolution en-face and cross-sectional views of the epidermis and superficial dermis for in vivo characterisation of actinic keratosis (AK), Bowen’s disease (BD) and squamous cell carcinoma (SCC). Despite its promise, standardised imaging protocols are lacking. Objective: This systematic review aims to assess the utility of LC-OCT for diagnosing AK, BD and SCC, with particular emphasis on workflow optimisation and protocol standardisation. Methods: A systematic literature search was performed using PubMed, Embase, and Scopus databases (January 2018–October 2024). Two reviewers independently screened the records, extracted data and applied the Confidence in the Evidence from Reviews of Qualitative research (CERQual) framework to assess confidence in key findings. Results: Eleven studies met the inclusion criteria. LC-OCT reliably identified key histopathological correlates. Across studies, LC-OCT consistently visualised hyperkeratosis, keratinocytic atypia, parakeratosis, and acanthosis, as well as characteristic vascular alterations and dermal remodeling. LC-OCT also demonstrated its capacity to detect invasive features by revealing disruptions in the dermo-epidermal junction and the presence of tumour strands infiltrating the dermis. Multimodal imaging combined with technical optimisations such as minimal probe pressure, paraffin oil coupling, and dermoscopy-guided localisation, substantially improved image resolution and interobserver concordance. Conclusions: This systematic review provides a basis for establishing standardised LC-OCT imaging protocols in keratinocytic tumours. While LC-OCT shows promise as a non-invasive diagnostic tool, further multicenter studies are needed to refine imaging workflows and evaluate the integration of artificial intelligence-based analysis to improve diagnostic accuracy and reproducibility. Full article