Search Results (1,610)

Facial occlusion induces sample-wise reliability shifts in facial expression recognition (FER), where the usefulness of global context and local discriminative cues varies dramatically with the amount of visible facial information. Existing occlusion-robust FER studies often evaluate under limited or homogeneous occlusion settings and commonly adopt static fusion strategies, which are insufficient for complex and heterogeneous real-world occlusions. In this work, we establish a rigorous occlusion robustness evaluation protocol by constructing a fixed offline test benchmark with diverse synthetic occlusion patterns (e.g., masks, sunglasses, texture blocks, and mixed occlusions) on top of public FER test splits. We further propose a Dual-Stream Adaptive Weighting Mixture-of-Experts framework (DS-AW-MoE) that fuses a global contextual expert and a local discriminative expert via an occlusion-aware weighting network. Crucially, we introduce a facial visibility assessment as a task-agnostic prior to explicitly regulate expert contributions, enabling dynamic re-allocation of model capacity according to input-dependent feature reliability. Extensive experiments on public datasets and the constructed occlusion benchmark demonstrate that DS-AW-MoE achieves more stable recognition under complex occlusions, characterized by a smaller and more consistent performance drop. To support reproducibility under dataset license constraints, we will release an anonymous, fully runnable repository containing the complete occlusion synthesis pipeline, evaluation protocol, and configuration files, allowing researchers to reproduce the benchmark after obtaining the original datasets. Full article

The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered. Full article

Background/Objectives: While caregiver burden in Inflammatory Bowel Disease (IBD) is well documented, the association between informal support and patient-reported outcomes (PROs), particularly health-related quality of life (QoL) and psychological well-being, remains underexplored. This systematic review synthesizes evidence on the association of informal caregiving on patient-reported QoL and psychosocial outcomes and maps the available evidence on clinical outcomes. Methods: Following international reporting guidelines and prospective protocol registration, a systematic search was conducted across five electronic databases between May and October 2025. Observational studies in adults with IBD assessing informal support and patient-reported or psychosocial outcomes were included. Owing to substantial heterogeneity in constructs and outcome measures, results were synthesised using a structured Synthesis Without Meta-analysis (SWiM) approach. Methodological quality was assessed using standardised critical appraisal checklists. Results: Six cross-sectional studies involving 1036 patients and 417 informal caregivers met the inclusion criteria. All studies reported a positive direction of association between higher levels or better quality of informal caregiver support and improved patient-reported QoL. Several studies identified psychological and relational factors, such as lower patient psychological distress and caregiver-related positive feelings and caring ability, as mechanisms statistically associated with this relationship. Conclusions: Available cross-sectional evidence suggests a positive association between informal support and patient-reported QoL/psychological outcomes in IBD, but causality cannot be inferred. Priorities include longitudinal dyadic studies and caregiver-inclusive interventions, alongside standardised definitions and measures of support. Full article

Background/Objectives: Transverse maxillary deficiency in growing patients can be treated using rapid maxillary expansion (RME) or slow maxillary expansion (SME) with spring-based appliances, such as the Leaf Expander (LE), but their comparative dentoskeletal effects remain debated. This study evaluated the transverse dentoskeletal outcomes of LE-based SME versus conventional RME. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO. Electronic searches were performed in PubMed, Scopus, Embase, Web of Science, and Cochrane Library up to 9 January 2026. Randomized controlled trials (RCTs) comparing LE-based SME and RME in skeletally immature patients were included. Primary outcomes were transverse maxillary change; secondary outcomes included dentoalveolar side effects. Risk of bias was assessed using the RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. When possible, a meta-analysis was performed using standardized mean differences and a random-effects model. Results: Four RCTs met the inclusion criteria. Both SME and RME achieved significant transverse expansion. Meta-analysis showed no statistically significant differences between protocols for inter-canine distance, inter-second deciduous molar distance, inter-first permanent molar distance, or basal maxillary width. Intergroup differences varied by anatomical site and measurement method: RME showed greater anterior dental and skeletal transverse gains, whereas SME achieved comparable intermolar expansion with greater molar distorotation. Three-dimensional analyses indicated similar morphological enlargement. Risk of bias ranged from low to high; the certainty of evidence was low to very low for most transverse parameters and moderate only for molar distorotation. Conclusions: Both LE-based SME and RME effectively correct transverse maxillary deficiency. Quantitative synthesis showed comparable overall transverse expansion, with differences mainly related to the distribution and biomechanical pattern of dentoskeletal effects rather than the absolute amount of expansion achieved. Appliance selection should be guided by biomechanical features and individual treatment objectives. Further high-quality RCTs with standardized three-dimensional protocols and longer follow-up are needed. Full article

Background and Objectives: Burnout is highly prevalent among emergency and acute care healthcare workers (HCWs), yet biological correlates remain debated because candidate biomarkers are strongly shaped by circadian timing, shift work, sleep loss, and overlapping affective symptoms. We mapped post-2018 evidence of biological biomarkers assessed alongside validated burnout measures in emergency department (ED), emergency medical services (EMS), and related acute care settings. Specifically, we asked whether reproducible biological correlates of burnout can be identified in emergency and acute-care healthcare workers when biomarker endpoint class and sampling context are systematically considered. Materials and Methods: We conducted a systematic scoping review with evidence mapping (PRISMA-ScR). PubMed/MEDLINE and the MDPI platform were searched for English-language studies published from 2018 onward (through January 2026). Eligible quantitative studies enrolled ED/EMS or acute care HCWs, assessed burnout using validated instruments, and reported at least one biological biomarker. Evidence was charted by biomarker domain and endpoint class (basal measures, stress reactivity paradigms, and chronic indices such as hair-based markers). Results: Overall, 19 studies were included in mapping/synthesis. Biomarker selection clustered around the hypothalamic–pituitary–adrenal axis (cortisol; n = 10/19), with fewer studies focused on autonomic function (heart rate variability; n = 2/19) and immune–inflammatory markers (n = 2/19), and single-study coverage for oxidative stress (n = 1/19), cardiometabolic candidates (n = 1/19), cellular aging (n = 1/19), neuroglial/multi-system candidates (n = 1/19), and feasibility-oriented multi-marker designs (n = 1/19). Reported associations with burnout were heterogeneous in direction and magnitude, but were more interpretable when endpoint class, timing anchors, and shift/sleep-related covariates were explicitly reported. Rates of confounder adjustment were low across studies (e.g., only 3/19 reported multivariable adjustment, and none systematically measured sleep or circadian factors), substantially limiting interpretability. Conclusions: The 2018+ literature does not support a single reproducible biomarker for burnout in emergency and acute care workforces. Evidence instead suggests multi-system dysregulation that is highly sensitive to endpoint class, sampling timing, and contextual confounding. Future studies should prioritize timing-anchored repeated-measures protocols across shift and recovery windows, jointly model sleep/circadian factors and depressive symptoms, and evaluate multi-marker panels and intervention responsiveness. Full article

The usage of magnetic nanoparticles (MNPs), particularly iron oxide-based systems such as magnetite (${\mathrm{Fe}}_3$${\mathrm{O}}_4$) and maghemite ($\gamma$-${\mathrm{Fe}}_2$${\mathrm{O}}_3$), has significantly advanced the field of theranostics. These nanoparticles unite therapeutic and diagnostic capabilities due to their favorable magnetic properties and surface engineering potential. However, the path from synthesis to clinical application poses substantial challenges, including optimization of structure–property–function relationships, biocompatibility issues, and effective surface functionalization. Various synthesis methods, such as co-precipitation and thermal decomposition, aim to achieve specific nanoparticle characteristics, although they encounter obstacles related to scalability and reproducibility. Furthermore, characterizing these systems through structural, microstructural and spectroscopic techniques is vital to determine their functional efficacy and ensure their safe biomedical usage. This review comprehensively examines recent advancements and identifies existing challenges in the clinical translation of MNPs, highlighting the need for refined methods and standardized protocols to effectively exploit their theranostic potential. It outlines future directions, emphasizing the importance of green synthesis and robust characterization frameworks to enhance the integration of MNPs in personalized medicine. Full article

Background: Carcinogenic, Mutagenic, and Reprotoxic (CMR) substances are among the most significant occupational health hazards in healthcare and research laboratories. Despite preventive measures and regulations, exposure assessment and risk management remain complex due to varied working practices, mixed exposures, and the lack of harmonized monitoring protocols. This systematic review investigates occupational exposure to CMR substances in laboratory and healthcare environments. Methods: Searches were conducted in PubMed, Scopus, and Web of Science up to February 2025 using tailored keyword strategies. Studies published between 2020 and 2025 reporting exposure assessment, monitoring, and/or risk management of CMR chemicals were included; non-English papers and irrelevant studies were excluded. Titles/abstracts and full texts were screened independently by two reviewers with arbitration by a third. Risk of bias was assessed by three authors who independently evaluated each study. A narrative synthesis with frequency tables was performed; no meta-analysis was conducted. Results: Of 446 screened records, 50 studies were included. Formaldehyde (25 studies) and antineoplastic drugs (18 studies) were most frequently examined. Healthcare settings—e.g., hospital pharmacies, oncology wards, and pathology laboratories—were predominant, while research laboratories were underrepresented. Inhalation was the main exposure route for formaldehyde, whereas dermal uptake and surface contamination predominated for antineoplastic drugs. Monitoring methods included air sampling, surface wipe testing, and biological assays; preventive strategies varied and were inconsistently applied. Most included studies involved environmental monitoring and did not report participant numbers, so a total number of participants cannot be aggregated; for the main outcomes, participant counts were often not available. Limitations of the evidence include marked heterogeneity across settings, matrices, analytical methods, and reporting units, which precluded meta-analysis, as well as imprecision and incomplete reporting in several studies. Conclusions: Findings reveal persistent gaps in harmonized exposure limits, monitoring standards, and long-term health surveillance, underscoring the need for comprehensive prevention strategies. This review was not registered and did not receive any external funding. Full article

Growing concerns about environmental pollution and the sustainability of conventional nanomaterial synthesis have accelerated interest in plant-based routes for nanoparticle production. This review provides an in-depth analysis of more than 290 peer-reviewed research and review articles published between 2010 and 2025, extracted from the Web of Science and Scopus databases, on the green synthesis of metallic and metal oxide nanoparticles using plant extracts, with particular emphasis on their characterization and application in water treatment. Plant-derived phytochemicals serve as natural reducing and stabilizing agents, enabling nanoparticle formation without hazardous reagents. Key physicochemical characterization techniques, including UV–Visible spectroscopy, X-ray diffraction, Fourier Transform Infrared spectroscopy, scanning and transmission electron microscopy, and energy-dispersive X-ray analysis, are evaluated for their roles in confirming nanoparticle structure, morphology, surface chemistry, and optical behavior. The review focuses on water purification applications, highlighting adsorption and photocatalytic degradation as the most extensively investigated removal pathways. Particular attention is given to widely studied material classes such as silver, zinc oxide, titanium dioxide, and iron-based nanoparticles, which demonstrate effective removal of heavy metals, synthetic dyes, pesticides, and pharmaceutical residues. Current limitations related to synthesis reproducibility, mechanistic understanding, stability, and scalability are critically discussed. The review concludes by identifying priority research directions, including standardized synthesis protocols, deeper chemical analysis of plant extracts, and the integration of green nanoparticles into immobilized and membrane-based systems to advance their practical implementation in sustainable water treatment technologies. Full article

Cardiac telerehabilitation has become a promising alternative to traditional programmes for preventing acute coronary syndrome (ACS) in the secondary phase. However, current implementations are still reactive and standardised, lacking personalisation and flexibility in clinical settings. By integrating artificial intelligence (AI), it may be possible to overcome these limitations and provide intelligent, scalable, and patient-centred care. Methods: We conducted a structured literature review across PubMed, Scopus, the Cochrane Library, and Web of Science, targeting English-language studies published from January 2015 to May 2025. Inclusion criteria included adult populations with a history of ACS or high cardiovascular risk, assessing interventions based on AI, telerehabilitation, or their combination. Studies are needed to report clinical, functional, behavioural, or technological outcomes. A thematic narrative synthesis was utilised. Results: AI-enhanced telerehabilitation demonstrates potential advantages over conventional digital care in selected domains, including adaptive risk prediction, personalised exercise modulation, and adherence support. Several systems report real-time adjustment of exercise protocols, early dropout detection, and predictive analytics for rehospitalisation. AI integration may also contribute to personalised behavioural feedback and psychosocial monitoring. Nevertheless, the overall level of evidence remains preliminary and heterogeneous, with most AI-based interventions evaluated in pilot, feasibility, or modelling studies rather than large-scale randomized trials. Conclusions: The integration of AI into telerehabilitation represents a promising evolution in post-ACS care, shifting from predominantly reactive monitoring toward more adaptive and data-driven support models. While early-phase studies suggest feasibility and potential clinical benefit, robust multicentre randomized controlled trials and cost-effectiveness analyses are required before definitive conclusions regarding superiority or widespread implementation can be drawn. Full article

Movable guardrail systems are increasingly used in work zones, reversible lanes, and temporary traffic operations; however, evidence on their crashworthiness, material performance, and operational reliability remains dispersed across multiple design typologies and regulatory frameworks. This PRISMA-compliant systematic review synthesizes 78 studies involving full-scale crash tests, validated finite-element simulations, field performance evaluations, and compliance evaluations under MASH, EN 1317, NCHRP 350, and AS/NZS 3845.1. The findings indicate that modular rigid barriers reliably achieve TL-3/TL-4 performance when joint alignment and foundation conditions are properly controlled; semi-rigid steel systems provide a practical balance between containment capacity and redeployability, but remain sensitive to post spacing and connector detailing; and flexible polymer systems are best suited for short-duration, low-speed applications. Material-focused research highlights the advantages of UHPC section refinement, high-strength steels, and hybrid FRP–metal configurations in enhancing energy absorption without exceeding occupant-risk thresholds. Across studies, connection integrity consistently emerges as the dominant factor governing redirection stability and working-width performance. Field evaluations confirm satisfactory operational performance in constrained environments, while life-cycle assessments identify refurbishment intervals and mass-related logistics as major cost contributors. This review provides an integrated, evidence-based synthesis and a structured engineering foundation for advancing next-generation movable barrier designs, testing protocols, and deployment strategies. Full article

Background: Dental fear and anxiety are highly prevalent in children, resulting in avoidance or incomplete dental treatment; sedation emerges as a possible behavioral management strategy. This umbrella review aimed to provide a structured and critical synthesis of the available knowledge on sedative single-agent efficacy and routes of administration employed for achieving sedation (excluding deep sedation/general anesthesia) during dental procedures in children for behavior management, as well as to evaluate acceptability and satisfaction for child, caregiver, and provider, and to assess the influence of clinical setting and provider. Methods: In line with the PRISMA statement, the protocol was registered on PROSPERO (CRD420251043738), and 18 systematic reviews were included and synthesized qualitatively. Results: Single-agent sedation was safe and effective for managing behavior in children during dental procedures, with midazolam and nitrous oxide being the most studied agents. Different routes of administration showed distinct characteristics in onset, recovery time, adverse effects and cooperation, while agent selection appeared influenced by clinical setting and provider type. However, data on acceptability and satisfaction from children, caregivers, and providers remains limited. Conclusions: Evidence suggests potential effectiveness of selected agents and routes in appropriately monitored settings, but data heterogeneity precludes strong comparative recommendations. Further studies are therefore needed to address the existing gaps in pediatric dental sedation. Full article

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article

Background/Objectives: Chronic kidney disease (CKD) affects almost 800 million people worldwide. Cardiovascular disease is the main cause of death in this population. Although physical activity is fundamental to systemic health, the evidence regarding its impact on the clinical outcomes of CKD populations is inconclusive. This protocol outlines the methodology for a systematic review and meta-analysis aimed at evaluating the association between physical activity and intensity and all-cause mortality, cardiovascular mortality, and cardiovascular disease. Methods: This protocol adheres to PRISMA-P and Cochrane Handbook guidelines and has been registered with PROSPERO (CRD420261302904). A systematic search will be conducted in MEDLINE, Scopus, Web of Science and the Cochrane Library until June 2026. Studies estimating the association between physical activity and all-cause mortality, cardiovascular mortality and cardiovascular disease in populations with CKD will be included. Two independent reviewers will perform study selection, data extraction and quality assessment using the Study Quality Assessment Tool from the United States National Institute of Health tool. The certainty of the evidence will be evaluated using the Grading of Recommendations, Assessment, Development and Evaluation tool. Narrative synthesis and random-effects meta-analysis will be conducted to calculate pooled effect estimates. Random-effects meta-analyses will be performed according to the level of physical activity, and meta-regressions will be used to control for the association with major covariates. Ethical approval is not required for this study. Results: The results will provide a comprehensive synthesis of the evidence regarding the use of physical activity as a non-pharmacological intervention to modify CKD progression. Conclusions: The findings will be disseminated through peer-reviewed journals and international conferences. Full article

Deep learning has achieved expert-level performance in medical imaging analysis. However, models often fail to generalize across patient populations due to cross-population domain shifts, distributional differences arising from demographic variability, variations in imaging protocols, scanner hardware, and differences in disease prevalence. This challenge limits the real-world deployment and can increase health inequities. This review systematically examines the nature, causes, and impact of cross-population domain shift in deep learning-based medical imaging analysis. We analyzed 50 peer-reviewed studies from 2020 to 2025, evaluating the proposed methodologies for handling population shifts, the datasets employed, and the metrics used to assess performance. Our findings demonstrate that performance degradation ranged from 10–25% when models were tested on unseen populations, emphasizing the substantial impact of domain shifts on model generalizability. The literature reveals that mitigation strategies broadly fall into two categories: data-centric approaches, such as augmentation and harmonization, and model-centric approaches, including domain adaptation, transfer learning, adversarial learning, multi-task learning, and continual learning. While domain adaptation and transfer learning are the most widely used, their performance gains across populations remain modest, ranging from 5–15%, and are not supported by external validation. Our synthesis reveals a significant reliance on large, publicly available datasets from limited regions, with an underrepresentation of data from low- and middle-income countries. Evaluation practices are inconsistent, with few studies employing standardized external test sets. This review provides a structured taxonomy of mitigation techniques, a refined analysis of domain shift characteristics, and an in-depth critique of methodological challenges. We highlight the urgent need for more geographically and demographically inclusive datasets, adaptable modeling techniques, and standardized evaluation protocols to enable accurate and equitable AI-driven diagnostics across diverse populations. Finally, we outline future research directions to guide the development of robust, generalizable, and fair models for medical imaging analysis. Full article

This review summarizes scientific advances about the sonochemical synthesis of starch nanoparticles (St-NPs) for the food industry, as well as nutraceutical and drug delivery applications. High-intensity ultrasonication (HIU) has been explored as a versatile and environmentally friendly alternative to conventional methods for synthesizing St-NPs with high yields (>90%), controlled size (~100 nm), and minimal effluent generation. Thus, HIU has been explored (pre- or post-treatment) to mitigate the inherent disadvantages (high-cost, low yields, and environmental impact) of hydrothermal gelatinization, acid/alkaline hydrolysis, enzymatic hydrolysis, enzyme branching, water-in-oil and oil-in-water emulsions, non-solvent nanoprecipitation, extrusion, high-pressure homogenization, high-energy milling, and cold plasma. Conventional sources of starch (corn [normal, waxy, high-amylose] and potato) and other unconventional sources (tubers [cassava, yam, malanga], seeds and grains [sorghum, barley, quinoa, lotus], breadfruit, pinhao seed, Araucaria angustifolia) have been subjected to single or assisted sonochemical protocols to obtain St-NPS with unique structural, physicochemical, and technological properties. The physical–mechanical effects of ultrasonication (cavitation, heat, and pressure) directly promote surface functionalization (i.e., esterification, pore formation) and impact the St-NPS’s particle size, double-helix structure, enzymatic-resistance properties, crystallinity, and intra- and intermolecular arrangements. Pickering additives in food systems, colloids in beverages, nanocomposites in biofilms for food packaging, and nanocarriers for drug and nutraceutical delivery (oral and transdermal) have been the most reported applications. Full article