Search Results (15,792)

Background: Vitamin D is involved not only in calcium–phosphate homeostasis but also in immune and endothelial regulation. Vitamin D deficiency has been suggested to worsen disease activity in systemic lupus erythematosus (SLE). Environmental and lifestyle factors, including seasonal sun exposure, smoking, diet, and supplementation, may influence vitamin D status and disease manifestations. This study aimed to evaluate the association between serum 25-hydroxyvitamin D [25(OH)D] levels, disease activity, and anti-double-stranded DNA (anti-dsDNA) antibody titers in patients with SLE, taking selected lifestyle and environmental factors into account. Methods: Serum 25(OH)D concentrations, SLE disease activity assessed by the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) score, and anti-dsDNA antibody titers were measured in patients with SLE and healthy controls. Blood samples were collected during sunny (April–September) and non-sunny (October–March) months. Information on vitamin D supplementation, smoking status, and dietary habits was obtained using a structured questionnaire. Associations between vitamin D status, disease activity, anti-dsDNA seropositivity, season of blood collection, supplementation, smoking, and diet were analyzed statistically. Results: Patients with SLE had significantly higher mean serum 25(OH)D levels than controls, mainly due to frequent vitamin D supplementation. No significant associations were observed between serum 25(OH)D levels and SLEDAI-2K scores or anti-dsDNA antibody positivity. Seasonality, smoking status, and adherence to special diets were not significantly related to disease activity or anti-dsDNA seropositivity. Vitamin D supplementation was strongly associated with sufficient 25(OH)D levels but did not translate into reduced disease activity or lower anti-dsDNA prevalence. Conclusions: Serum 25(OH)D concentration was not associated with clinical or immunological activity of SLE in this cross-sectional study, despite effective correction of deficiency through supplementation. These findings likely reflect the heterogeneity of SLE and the limitations of single time-point assessments, although regular monitoring and individualized vitamin D supplementation may still be considered in SLE care, particularly in the context of recommended photoprotection. Full article

Background: The global prevalence of disabilities highlights the urgent need for accessible digital interfaces, particularly in healthcare. Methods: A systematic literature review was conducted to examine the current state of digital accessibility in healthcare interfaces, analyzing 32 scholarly articles to identify key challenges and recommendations for improvement. The selection process was rigorous, ensuring a comprehensive overview of existing research on accessibility in digital healthcare platforms. Results: Our research highlights significant issues such as the digital divide faced by individuals with disabilities and the need for inclusive design to enhance usability and accessibility. Key challenges identified include inadequate compliance with accessibility standards, limited user-centered design practices, and insufficient integration of assistive technologies. This study synthesizes best practices for creating accessible healthcare applications and websites, focusing on addressing these challenges. Conclusions: The authors highlight strategic recommendations aimed at ensuring that digital healthcare systems are inclusive and effective, enhancing accessibility and quality of life for individuals with disabilities. Full article

Accurate assessment of pipeline defect integrity and proactive risk warning are essential for the safe, reliable, and economical transportation of oil and gas. Existing approaches are largely based on static assessment models, such as the Det Norske Veritas Recommended Practice for corroded pipelines (DNV-RP-F101), and often produce conservative failure-pressure predictions because time-dependent defect evolution and operational pressure fluctuations are not considered. To address this limitation, this study develops a dynamic defect-growth–failure-pressure coupling model that integrates a long short-term memory (LSTM) network with an enhanced DNV-RP-F101 framework. Time-varying axial and circumferential correction coefficients are introduced to update the bulging factor dynamically, thereby supporting defect-growth prediction and time-variant failure-pressure calculation. The model is validated against four established standards using public pipeline datasets. For single defects, the proposed model achieves the lowest mean square error (MSE) of 0.81 MPa and an average error of 1.18 MPa among the compared methods. For defect clusters, the prediction error remains within 8.64%. A five-level dynamic risk-warning system is further established by integrating Monte Carlo simulation with API 579 standards, enabling quantification of failure probability and prediction of remaining service life. Engineering case studies show that the proposed method can identify the time points at which pipelines enter hazardous failure-probability stages. This capability supports more precise early warning and provides a technical basis for intelligent pipeline integrity management and predictive maintenance. Full article

This review examined study characteristics and common practices in continuous glucose monitoring (CGM)-based exercise studies. Following PRISMA guidelines, a systematic search in the PubMed database was conducted. Data were extracted from 93 publications. A minority of studies (12.9%) focused on CGM system validation. Acute exercise studies were more common (87.1%) than chronic exercise studies (12.9%). Randomized crossover designs predominated (71.0%). Participant populations varied and included 46.2% non-diabetic individuals (7.5% athletes), 36.6% individuals with type 1 diabetes mellitus, and 17.2% individuals with type 2. The upper arm was the most common sensor placement site (41.9%), although nearly one-third of studies did not report placement details. Devices were primarily from Abbott (40.9%), Medtronic (36.6%), and Dexcom (26.9%). Sample sizes were typically small, with 40.9% of studies including 10–14 participants. Reporting practices frequently deviated from the International Consensus Statement on CGM Metrics for Clinical Trials. Many studies used modified or non-standard metrics, whereas “mean sensor glucose” was reported in compliance with consensus recommendations in 58.1% of studies. Regarding data completeness, “data gaps” was the most frequently reported consensus-compliant metric (43.0%). In validation studies, accuracy metrics predominated, with “absolute relative difference” representing the most common outcome (87.5%). Overall, substantial heterogeneity limits comparability across studies, highlighting the need for standardized CGM reporting. Full article

Foamed concrete has been used to construct lightweight embankments as a substitute for conventional fills, aiming to promote its engineering application in soft-soil regions. However, the dynamic response and safety mechanism of foamed concrete embankments during vehicle collision are not yet fully understood. In this paper, the safety performance of lightweight foamed concrete embankments under vehicle–guardrail collision and the optimal design of the guardrail foundation are investigated from the perspectives of lateral displacement and stress distribution. Through static uniaxial compression tests, the stress–strain curves, compressive strength, elastic modulus, and statistical variability of foamed concrete with six different mix proportions were obtained. On this basis, a coupled finite element model of the vehicle–guardrail–lightweight embankment system was established (the guardrail and its foundation were modeled using a linear elastic constitutive model, the embankment using a crushable foam model, and the vehicle using a 1.5 t passenger car model validated by full-scale crash tests). According to the passenger car impact conditions specified in current Chinese regulations (velocity 100 km/h, angle 20°), the peak lateral displacement and peak principal stress of the lightweight embankment were analyzed for four foundation base slab lengths (L₀, 1.1 L₀, 1.2 L₀, 1.3 L₀). The results show that increasing the base slab length effectively reduces lateral displacement and stress concentration. Increasing the length by 10–20% reduces the peak lateral displacement by up to 68%, and the peak principal stress remains far below the material strength. From the perspectives of structural stability and cost-effectiveness, a 10–20% increase in the base slab length is recommended. The ratio of the peak principal stress to the material strength can serve as a criterion for evaluating the safety margin and assessing the rationality of the foundation design. This study provides quantitative evidence for optimizing the guardrail foundation base slab length to enhance the collision safety of lightweight foamed concrete embankments, and the proposed design range offers a cost-effective reference for practical engineering applications in soft-soil regions. Full article

Intraoperative anaesthetic monitoring is essential in rabbits due to their high perioperative morbidity and mortality and the limited availability of species-specific evidence, despite their increasing role as companion animals. This narrative review summarises the available scientific literature on intraoperative monitoring in anaesthetised rabbits, focusing on central nervous system assessment, cardiovascular and respiratory monitoring, and temperature management during general anaesthesia. Findings indicate that anaesthetic depth assessment based solely on ocular reflexes is unreliable and should be integrated with jaw tone, reflex responses, and respiratory patterns. Cardiovascular monitoring relies on heart rate, electrocardiography, and arterial pressure measurement, although invasive and non-invasive techniques have limitations in accuracy and practicability in small-size patients. Pulse oximetry and capnography assess oxygenation and ventilation but may be affected by peripheral perfusion, equipment dead space, and technical limitations. Temperature monitoring is critical due to high risk of hypothermia, with continuous or frequent measurements recommended. Overall, multimodal monitoring improves detection of physiological disturbances and supports safer anaesthetic management. Full article

Background: Evidence-based clinical guidelines are essential for high-quality care yet translating them into personalized clinical decision support remains resource-intensive and time-consuming. Large language models (LLMs) show promise for supporting clinical decision-making, but their limited access to patient-specific data and explicit guideline sources constrains trustworthiness, personalization, and clinical applicability. Retrieval-augmented generation (RAG) addresses part of this challenge by grounding model outputs in curated evidence sources; however, true personalization requires structured access to electronic health record data. Methods: This study presents FHIR-RAG-MEDS, a medical decision support system that integrates HL7 Fast Healthcare Interoperability Resources (FHIR) with an RAG-enhanced LLM to enable patient-specific, guideline-concordant clinical recommendations. Through SMART on FHIR, the system retrieves real-time patient data from FHIR servers, generates structured medical summaries, and incorporates this personalized context into the RAG pipeline, grounding responses in evidence-based clinical guidelines stored in a vector database. Results: FHIR-RAG-MEDS was evaluated using 139 physician-generated clinical questions covering dementia, chronic obstructive pulmonary disease, hypertension, and sarcopenia. Performance was assessed using automated metrics, RAG-specific evaluation frameworks, and independent expert physician review. The system consistently outperformed state-of-the-art medical LLMs, demonstrating higher semantic accuracy, improved faithfulness to guideline content, and stronger clinical relevance. Conclusions: Integrating HL7 FHIR with RAG-based LLMs enables trustworthy, personalized clinical decision support, bridging the gap between static language models and real-world, patient-centered care. Full article

The increasing demand for permanent post-disaster housing highlights the need for rapid and high-quality construction methods. This study investigates the feasibility of prefabricated modular façade systems in accelerating post-disaster permanent housing construction, while maintaining cost efficiency and construction quality. Unlike previous studies that primarily focus on fully modular building systems, this research examines façade-level prefabrication as an intermediate and scalable strategy that can be integrated into conventional post-disaster housing construction. A mixed-methods approach was adopted: semi-structured interviews were conducted with 15 industry stakeholders, and thematic analysis was applied to extract qualitative insights. Subsequently, a quantitative survey involving 366 construction professionals was carried out and statistically analyzed to validate the findings. Additionally, case studies from previous post-disaster reconstruction efforts were reviewed to contextualize the results. The findings reveal that prefabricated modular façade systems improve construction speed, implementation efficiency, and quality control. Evidence from semi-structured interviews, the survey of 366 construction professionals, and representative case-project comparisons consistently supported the applicability of façade-level prefabrication in post-disaster housing delivery. Most participants also noted quality control benefits inherent to factory-based production. However, the study identifies several limitations, including challenges related to cost and workforce training. The research contributes to the evolving discourse on disaster-responsive housing policies and provides strategic recommendations to enhance the adoption of modular façade technologies in construction practices. Full article

Healthcare systems are complex adaptive environments in which clinical work, digital technologies, and organizational routines interact continuously, often challenging the integration of artificial intelligence (AI) into everyday practice. Although explainable AI (xAI) has been proposed to address concerns related to algorithmic opacity and professional trust, explainability is still frequently approached. Grounded in General Systems Theory, sociotechnical systems theory, and complexity science, this study conceptualizes explainability as an emergent system-level property of healthcare systems. Using Design Science Research as a systems-oriented inquiry methodology, a human-centered conceptual framework for AI-supported clinical decision-making was developed through iterative cycles of problem framing, design, demonstration, and evaluation. The framework was explored in rehabilitation nursing, a domain characterized by multidimensional patient data, longitudinal decision processes, and close professional–patient interaction. Iterative engagement with Rehabilitation Nursing Specialists informed design principles related to user participation, contextualized explanations, and workflow alignment. An exploratory evaluation with 144 Specialists assessed perceived usefulness, comprehensibility of explanations, and acceptance of AI-supported recommendations in realistic scenarios. The findings indicate that explainability is experienced not as a property of the algorithm alone, but as an outcome emerging from interactions between AI behavior, human interpretation, and organizational context. The framework shows potential to meaningfully support clinical decision-making in Rehabilitation Nursing by providing contextually aligned, human-centered explanatory outputs. Full article

Background/Objectives: Long-term care (LTC) is becoming a strategic priority for health systems facing population ageing, multimorbidity, frailty, and increasing demand for coordinated medical and social support. Greece faces these pressures in a context of fragmented governance, limited formal LTC capacity, heavy reliance on family care, and quality oversight that remains largely compliance-oriented rather than performance-oriented. This policy review aims to translate international and national evidence into an operational framework for measuring and improving LTC quality in Greece. Methods: The review combined a structured search of peer-reviewed literature, international policy reports, statistical sources, and Greek legislative and regulatory texts with a pragmatic feasibility assessment of candidate indicators. Results: Evidence from OECD and EU systems suggests that mature LTC quality systems share four operational features: legally mandated reporting, standardised indicators, public transparency, and use of data for provider-level improvement. For Greece, the analysis identifies major gaps in legal reporting obligations, data interoperability, workforce monitoring, public reporting, and user-experience measurement. We propose a three-tier indicator framework covering safety, clinical care processes, workforce and staffing, person-centredness, access and equity, efficiency, governance, and digital readiness. Implementation should proceed through a five-year roadmap: foundation, scale-up, and consolidation. Provider-level dashboards and a National LTC Quality Observatory are recommended as key mechanisms for transforming data into continuous quality improvement. Conclusions: A phased, feasible, and legally anchored approach could strengthen patient safety, dignity, operational efficiency, and accountability in Greek LTC. Full article

Post-surgical pyoderma gangrenosum (PSPG) is a challenging diagnosis associated with significant morbidity, often misidentified as postoperative infections, leading to inappropriate management. This systematic review aims to elucidate the characteristics, management strategies, and outcomes of PSPG in the context of foot and ankle surgery to improve diagnostic accuracy and patient care. A systematic literature search was conducted across multiple databases, including PubMed, Scopus, and Embase, for cases of PSPG following foot and ankle surgery published up to January 2023. Data on demographics, clinical presentation, management, and outcomes were extracted and analyzed. Ten cases met the inclusion criteria, predominantly females presenting with rapidly worsening and painful postoperative ulcers. A high rate of negative cultures was observed during the patients’ treatment period. Dermatology consults initially suspected 83.33% of the cases. Notably, 30% of patients underwent amputation of various parts of the lower extremity, all diagnosed more than 35 days after symptom onset, and were female. The mainstream treatment for PSPG involved systemic immunosuppressants, with corticosteroids being the most common, effectively resolving symptoms in the majority of instances. PSPG should be suspected in patients with unexplained, worsening postoperative wounds. Early recognition and appropriate treatment with immunosuppressants are crucial to prevent severe outcomes. Multidisciplinary management involving dermatologists and surgeons is recommended to optimize patient outcomes. Further research is needed to establish robust diagnostic and management protocols for PSPG in the surgical context. Full article

Road traffic crashes remain a major global public health and economic challenge, with heavy vehicle drivers disproportionately involved in severe incidents, particularly in low- and middle-income countries. In Ethiopia, limited access to continuous professional training constrains efforts to improve drivers’ safety-related knowledge and awareness. This study explored the impact potential and user acceptance of gamified e-learning modules designed to enhance heavy vehicle drivers’ knowledge and awareness of fatigue management, speed-related behavior, and eco-driving practices. A randomized pretest–post-test control-group design was employed, in which professional drivers were assigned to either an intervention group that completed three gamified e-learning modules or a control group that received no training. Data were analyzed using mixed repeated-measures analysis of variance. The results revealed significant time × group interaction effects across all domains (p < 0.001), with substantially greater improvements in the intervention group and large effect sizes. Participants also reported high perceived usefulness, behavioral intention, and trust in the system. These findings provide preliminary evidence that gamified e-learning may be a feasible and promising approach for improving short-term safety-related knowledge among professional heavy vehicle drivers. Further research is needed to determine whether these improvements are sustained over time and translate into behavioral change and measurable road safety outcomes before broader implementation can be recommended. Full article

Graph motifs capture crucial structural patterns within user–item interaction graphs and offer meaningful semantics that are typically underutilised in conventional graph-based collaborative filtering. Existing contrastive learning methods in recommender systems rely primarily on simple perturbations, which limits their ability to leverage deeper motif-based structural information. To address this gap, we propose a novel motif-guided contrastive learning framework for recommender systems. To exploit complementary structural biases inherent to user–item interactions, our approach explicitly incorporates three distinct motifs into the construction of the contrastive view. By contrasting views that capture the structural differences among different motifs, our model learns meaningful group-level relationships and potentially suppresses noise arising from sparse or isolated interactions. Extensive experiments on four real-world recommendation datasets validate that our motif-based contrastive approach achieves the best overall performance, outperforming state-of-the-art baselines on three of the four benchmarks with statistically significant margins on the more skewed datasets, while remaining competitive on the fourth, which demonstrates notable robustness and improved accuracy. Full article

Graph neural networks (GNNs) have shown great advantages in collaborative filtering recommender systems due to their capacity to model user–item relationships through information propagation. However, traditional GNN-based recommenders often fail to distinguish nodes with the same local structure, leading to identical representations after propagation. Some studies address this issue by introducing positional encoding. However, most existing positional encoding approaches break the permutation and orthogonal symmetries of graph representations and degrade generalization ability. To address this limitation, we propose EPCF (equivariant positional collaborative filtering), a novel GNN model for collaborative filtering that introduces an equivariant propagation mechanism for Laplacian positional features. The proposed mechanism preserves equivariance of positional features under orthogonal transformations while maintaining the permutation equivariance inherent to graphs, which can improve generalization. The equivariant positional features are further leveraged to guide node embedding propagation. Our experiments on real-world datasets show that EPCF achieves better average performance than the evaluated baselines, achieving average improvements of 7.01% in Recall@20 and 1.17% in area under the curve (AUC) over the strongest baselines. Furthermore, integrating EPCF as a plug-in mechanism into five different GNN backbone models achieves improvements of 23.13% in Recall@20 and 2.14% in AUC across five datasets, demonstrating its generalization capability. Full article

E-commerce platforms increasingly rely on recommendation systems whose internal logic is often opaque, making explanation design important for consumer evaluation. Drawing on procedural justice theory, this study examines whether process-based explanations function as procedural justice cues in e-commerce recommendations and how they relate to algorithmic trust and continuance intention. In a between-subjects online experiment with 394 Chinese consumers (197 per condition), participants received either an outcome-based recommendation or a process-disclosure package that disclosed data inputs and reasoning and therefore bundled procedural content with greater specificity and informational richness. Relative to outcome-based explanations, this package increased perceived procedural justice and was associated with higher trust in the algorithm and greater continuance intention. Perceived procedural justice and trust formed a theoretically ordered indirect pathway, but this ordering should be read as theory-grounded rather than causally established because the mediators and outcome were measured contemporaneously. Exploratory moderation analyses suggested that responsiveness to process-based explanations reflected broader self-reported digital interpretive capacity rather than algorithm-specific literacy alone. Robustness checks further indicated that the procedural justice pathway was not eliminated by explanation clarity, cognitive load, scenario realism, product attractiveness, or privacy intrusiveness. The findings position process-disclosure packages as practical transparency tools while cautioning that their benefits depend on consumers’ interpretive capacity and processing costs. Full article