Search Results (1,140)

Background/Objectives: This study aimed to estimate self-reported prevalence of HIV and gonorrhea among primary healthcare attendees in Riyadh and to identify key demographic, behavioral, and clinical predictors, acknowledging that diagnoses were based on participant self-report rather than laboratory confirmation. Methods: A cross-sectional survey was conducted between March and July 2023 across 48 primary healthcare centers in Riyadh. A total of 14,239 adult participants (aged ≥18 years) completed an electronically administered questionnaire that included self-reported prior diagnoses of HIV and gonorrhea. Multivariable logistic regression models were used to identify independent predictors of self-reported HIV and gonorrhea. Results: The self-reported prevalence of HIV was 2.6% (95% CI: 2.35–2.87%), and gonorrhea was 3.1% (95% CI: 2.83–3.40%). Several factors were independently associated with higher odds of self-reported infection. Younger age (<50 years) increased risk (HIV: AOR = 2.19; gonorrhea: AOR = 1.57), as did female sex (HIV: AOR = 1.67; gonorrhea: AOR = 1.59), higher education (HIV: AOR = 1.29; gonorrhea: AOR = 1.23), married status (HIV: AOR = 1.76,; gonorrhea: AOR = 1.49,), and insurance coverage (HIV: AOR = 2.01,; gonorrhea: AOR = 1.88). Behavioral and clinical factors included smoking (HIV: AOR = 4.79,; gonorrhea: AOR = 2.41,), hypertension (HIV: AOR = 2.58; gonorrhea: AOR = 1.49,), obesity (HIV: AOR = 11.55; gonorrhea: AOR = 9.02), hypercholesterolemia (HIV: AOR = 2.24; gonorrhea: AOR = 2.53,), and heart disease (HIV: AOR = 11.31; gonorrhea: AOR = 8.77). The notably high associations for obesity and heart disease should be interpreted with caution, as they may be influenced by detection bias or residual confounding within the healthcare-seeking sample. Conclusions: This study provides key insights into the self-reported burden and predictors of HIV and gonorrhea in Saudi Arabia. While identifying significant demographic and metabolic risk profiles, the high magnitude of certain clinical associations must be interpreted with caution due to potential detection bias and residual confounding. Given the reliance on self-reported data, these findings should be viewed as an epidemiological baseline rather than absolute prevalence. Prioritizing clinical context over statistical values and strengthening integrated, laboratory-based surveillance within primary care will be essential for improving early detection and evidence-based prevention strategies in the region. Full article

This study explored the impact of ethanol as a co-solvent in supercritical fluid extraction on the recovery of bioactive compounds from carrot seeds and assessed the resulting extracts for antimicrobial, α-amylase and α-glucosidase, and butyrylcholinesterase inhibitory potential. Ethanol supplementation significantly improved extraction performance, with the yield increasing from 110 mg in the absence of ethanol to 134 mg at 5% ethanol, followed by a slight decrease to 132 mg at 10%. High-performance liquid chromatography (HPLC) revealed pronounced phytochemical enrichment at 5% ethanol, particularly for chlorogenic acid (1541.24 µg/g), gallic acid (1279.27 µg/g), and hesperetin (1513.68 µg/g), indicating enhanced recovery of phenolic and flavonoid constituents. The 5% ethanol extract demonstrated superior antimicrobial activity, producing inhibition zones of 19 mm against Enterococcus faecalis, 26 mm against Klebsiella pneumoniae, 25 mm against Staphylococcus aureus, and 29 mm against Candida albicans. Values of both minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were markedly reduced, while antibiofilm activity reached 93.11% for E. faecalis and 91.00% for K. pneumoniae. The extract also exhibited potent inhibitory effects with IC₅₀ values of 7.74 and 13.37 µg/mL, against α-amylase and α-glucosidase, correspondingly, as well as strong butyrylcholinesterase inhibition (IC₅₀ = 2.51 µg/mL), highlighting promising α-amylase/α-glucosidase and butyrylcholinesterase inhibitory potential. Molecular docking further supported these findings, showing that chlorogenic acid bound more strongly than vanillin to OmpK36, lysosomal acid-α-glucosidase, and butyrylcholinesterase, with docking scores ranging from −6.1 to −6.9 kcal/mol. These findings identify ethanol-modified supercritical fluid extraction as a sustainable and effective green strategy for improving the recovery of carrot seed bioactives and enhancing their multifunctional in vitro biological properties. Notably, this study provides the first comprehensive evidence that 5% ethanol modification selectively enriches key phenolic constituents, including chlorogenic acid, gallic acid, and hesperetin, in carrot seed extracts, with corresponding enhancement of α-amylase, α-glucosidase, and butyrylcholinesterase inhibitory activities. Full article

Drug–drug interaction (DDI) prediction plays a critical role in optimizing therapeutic outcomes and enhancing patient safety. DDIs pose challenges in drug discovery, often leading to adverse effects, reduced efficacy, or unexpected outcomes. AI in DDIs acts as an effective tool for analyzing and predicting DDIs which introduced efficient computational approaches to DDI prediction. This paper aims to provide a comprehensive understanding of how ML and DL models perform in DDI prediction. This paper presents a comparative analysis based on key performance metrics such as accuracy, precision, recall and F-score for different ML and DL Models. We used Synthetic Minority Oversampling Technique (SMOTE) and the Gray Wolf Optimizer (GWO) which achieved the best accuracy of 95.42%. Combining the GWO with SMOTE addresses both optimization and data imbalance challenges in DDI prediction. Effectively, SMOTE addresses the class imbalance issue that leads to poor performance. SMOTE improves model performance by generating synthetic examples of the minority class rather than merely duplicating existing ones. This helps create a balanced dataset, enabling the model to learn the decision boundaries more accurately. SMOTE reduces the risk of overfitting. The GWO serves as a metaheuristic optimization framework that enhances model performance by guiding optimal feature selection subsets. This optimization process improves the model’s ability to capture complex, non-linear interaction patterns, leading to enhanced results. In our result, we achieve an accuracy of over 94% which helps in drug safety and therapeutic decision-making in health informatics. Full article

Background: Parkinson’s Disease is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. Effective management is critical to improving patient outcomes but is limited by the subjectivity of retrospective assessments of motor symptom burden. The objectives were to describe the weekly variability of objective bradykinesia score (BKS) derived from a wrist-worn device and assess the correlation between BKS variability and the Quality of Life (QoL) of People with Parkinson’s disease (PwP). Method: This observational, longitudinal study is part of the overall self-management project, Empower-PD. The study population consists of 80 PwP. Participants wore a wrist-worn accelerometer watch that measures movement during daily activities to evaluate the degree of bradykinesia as the bradykinesia score (BKS). Weekly median BKSs were collected over a 10-week period. Quality of life was assessed with the PDQ-39 questionnaire at baseline and after 10 weeks. Descriptive statistics assessed BKS variation and distribution. Linear and logistic regression models examined correlations between variability in BKS and QoL. Results: Results showed that 95% of BKS weekly changes were in a range between −4.3 and +4.4 points, with a central tendency near zero. The distribution of BKSs over time indicated that most participants exhibited stable BKSs. No significant correlation was found between BKS changes and QoL at 10 weeks. Conclusions: This study successfully described weekly BKS variations over ten weeks in PwP using objective measurements. The results showed that 95% of all value changes in BKS ranged from −4.3 to 4.4, values which may serve as preliminary distribution-based reference bounds that could inform the design of future studies examining clinically significant changes in BKS. Full article

Background: Among the superficial flexor muscles of the upper limb, the Palmaris longus muscle is the most susceptible to anatomical variation. The most common anatomical variant is complete bilateral absence, followed by unilateral absence. Although considerable study has been conducted on the frequency of Palmaris longus muscle absences in Africa, much of it has been conducted at the national level. The pooled prevalence of Palmaris longus absence in Africa has not been established. Objectives: To assess the absence of Palmaris longus muscle and its clinical significance in Africa cadaveric and clinical studies: systematic review and meta-analysis. Methods: We thoroughly examined Google Scholar, PubMed/med line, Science Direct, Hinari, African online journals, Web of Sciences, Central, Embase, Scopus, Cochrane, and institutional repositories. The studies’ quality were assessed using the Newcastle–Ottawa Scale. The pooled prevalence of Palmaris longus muscle absences was estimated using a random-effects meta-analysis model. Data analysis was conducted using STATA 17; heterogeneity, funnel plots, and meta-regression were examined. Sensitivity analyses, publication bias, and subgroup analysis by study time code, location code, and sample size code were also carried out. Result: A total of 23 studies were included in the meta-analysis. The pooled prevalence of Palmaris longus absence in Africa was 14.0% (95% CI: 10.0–18.0). However, there was significant variation in reported prevalence rates, as seen by the significant heterogeneity found across studies (I² = 99.13%). The results were not significantly changed by sensitivity analysis. Conclusions and recommendation: This study found that the Palmaris longus muscle is absent in 14% of African populations. Comparison with international studies revealed both similarities and differences, influenced by methodology and genetic factors. Clinicians should consider this prevalence when advising patients requiring tendon grafts. Further long-term studies using imaging techniques (MRI; ultrasound) are recommended to improve understanding in African populations and globally. Full article

Deformable medical image registration aims to spatially align anatomical structures across volumetric scans. Recent transformer-based methods achieve high overlap accuracy but often produce deformation fields with topological violations. We propose CTCF, a Cascade Transformer for Coarse-to-Fine registration that wraps a lightweight coarse-and-refined envelope around a core registration module. Level 1 provides a coarse displacement estimate at quarter resolution, Level 2 performs the main registration via a Swin Transformer encoder with deformable cross-attention and a learned super-resolution decoder, and Level 3 applies error-driven flow refinement at half resolution. The two outer levels add only 3.0% parameter overhead yet improve registration accuracy while maintaining competitive deformation regularity relative to external baselines. The model is trained end-to-end with a composite unsupervised loss combining local normalized cross-correlation, diffusion regularization, inverse-consistency, and Jacobian-based topology preservation. On the OASIS brain MRI benchmark, CTCF achieves the highest Dice score of 0.8208 among the compared unsupervised methods while maintaining competitive SDlogJ, with all Dice improvements statistically significant at $p<0.001$ by the Wilcoxon signed-rank test. On IXI, CTCF also achieves the best Dice, HD95, SDlogJ, and fold percentage among the compared methods. A five-round ablation study validates each component: cascade decomposition isolates each level’s contribution, and resolution scaling experiments confirm the framework’s scalability, yielding further accuracy gains with zero parameter overhead. Full article

Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak containment. This study proposes a veterinary-aware digital traceability framework that embeds herd health data, milk-quality testing, and inspection outcomes directly into batch-level EPCIS event records. By representing veterinary public health controls as structured, machine-actionable traceability elements, the framework enables automatic logging of mandatory control points, systematic compliance verification, and rule-based risk state transitions within standard EPCIS infrastructures. Using regulation-consistent dairy simulations modeling delayed Listeria detection during maturation, we evaluate the operational impact of event-level causal traceability within the proposed architecture. Compared with conventional time-window recall strategies, provenance-based trace-forward queries reduced recall scope under the evaluated synthetic scenarios. Integrating structured veterinary controls into EPCIS-based traceability systems supports automated regulatory evidence generation and more targeted recall decisions, contributing to improved auditability and reduced food waste in dairy supply chains. Full article

Large language models (LLMs) have rapidly advanced natural language processing and are increasingly used to analyze clinical narratives. Their ability to extract information, summarize records, and support clinical workflows makes them potential tools for enhancing documentation efficiency and the secondary application in the analysis of electronic health record (EHR) data. The aim of this work is to synthesize recent evidence on methodological approaches and applications of LLMs for clinical narrative processing, and to assess their performance, benefits, limitations, and implications for clinical practice. Across 2022–2026 studies, LLMs demonstrated strong performance in information extraction, summarization, triage prediction, section classification, and synthetic text generation, often surpassing traditional machine-learning models. Overall, LLMs improved the conversion of unstructured notes into actionable clinical insights, reduced documentation burden, and supported decision-making tasks. Key challenges included hallucinations, variable reproducibility, sensitivity to prompting, domain adaptation gaps, and limited transparency. Our findings indicate that LLMs show substantial promise for transforming clinical narrative processing, but safe adoption requires rigorous evaluation and continuous model auditing. This work provides a structured, non-systematic synthesis of representative studies and is intended as a high-level overview of emerging applications rather than a comprehensive systematic review. Full article

Background: Chronic nonspecific low back pain (CNLBP) with lumbar hyperlordosis leads to pain, dysfunction, and poor quality of life. Virtual reality (VR)-based training may enhance exercise engagement and outcomes. This study compared VR-based pelvic rocking training with conventional pelvic rocking training exercises. Methods: A triple-blind randomized controlled trial enrolled 100 participants with CNLBP and hyperlordosis, who were randomly allocated into two groups: the group, which performed pelvic rocking exercises using the TBed VR system (TbG), and the conventional group (CG), which performed the same exercises without VR. Both groups completed three sessions per week for eight weeks. Primary outcomes included pain (Numerical Pain Rating Scale, NPRS), lumbar lordotic angle (LLA), lumbar range of motion (ROM), and functional disability (Oswestry Disability Index, ODI). Secondary outcomes were patient satisfaction and commitment to exercise sessions. Assessments were conducted at baseline, immediately post-intervention, and after a one-month follow-up. Results: Both groups showed significant improvements in all outcome measures post-treatment (p < 0.016). Furthermore, some outcomes—specifically pain, LLA, and extension ROM—continued to improve during the follow-up period. The TbG demonstrated significantly greater reductions in pain, greater ROM improvements, greater functional gains, and higher levels of satisfaction and commitment than the CG (p < 0.05). These between-group differences persisted at the one-month follow-up, particularly for pain and ROM, which remained statistically significantly better in the TbG. Moreover, all between-group differences demonstrated medium to high clinical effects (d ≥ 0.3). Conclusions: Pelvic rocking exercises using the TBed VR system were superior to conventional exercises in terms of pain, ROM, and function at the immediate and intermediate time points. Using TBed led to better patient satisfaction and higher exercise commitment. Full article

Missing data are a persistent challenge in large healthcare datasets, often undermining both statistical validity and machine learning performance when handled using simplistic assumptions. In this work, we examine how entropy-based diagnostics can guide the selection of imputation strategies for high-dimensional health survey data using the National Health and Nutrition Examination Survey (NHANES) 2021–2023. Shannon entropy is used to identify variables with structurally complex missingness, and a range of classical approaches (mean imputation, k-nearest neighbors, and multiple imputation by chained equations) are evaluated alongside deep generative methods, including variational autoencoders, generative adversarial networks (GANs), Wasserstein GANs (WGANs), and diffusion-based models. All methods are compared under a controlled masked-entry evaluation using root mean square error (RMSE) and Kolmogorov–Smirnov (KS) statistics to capture both reconstruction accuracy and distributional fidelity. Results show that diffusion-based models provide the most consistent balance between numerical accuracy and distributional preservation across high-entropy dietary variables, while WGAN demonstrates competitive performance for selected distributions. Structural equation modeling further indicates that entropy is a useful diagnostic signal for identifying variables that are difficult to reconstruct. Overall, this study provides a reproducible framework for aligning imputation strategy with missingness complexity in healthcare data, with implications for improving reliability in downstream analytics. Full article

Online courses and distance learning are playing a significant role nowadays. These open, massive online courses (MOOCs) have garnered significant attention from academics, scholars, and policymakers; however, the literature offers limited empirical evidence, especially from a Saudi Arabian perspective. MOOCs help educators gain not only knowledge but also promote sustainability. The objective of this study was to investigate the impact of self-determination theory on the behavioral intention and actual use of MOOCs through the mediation of behavioral intention and media richness. For this purpose, convenience sampling was used, and data were collected from 145 respondents, including faculty members and students, across public and private sector universities. Smart PLS-SEM and CB-SEM were used to investigate the reliability, convergent validity, and discriminant validity by developing and testing measurement models using a confirmatory factor analysis. The hypotheses were tested using bootstrapping by developing structural models. The findings indicate that all the scales are reliable and valid, meeting the required threshold levels. Furthermore, all hypothesized relationships are positive and significant, except for the effect of perceived relatedness on the behavioral intention and actual use of MOOCs. Behavioral intention does not mediate the relationship involving perceived relatedness; however, it does mediate the relationships among perceived autonomy, competence, and actual use. Media richness also mediates the relationship between behavioral intention and actual use of MOOCs. The results suggest that MOOC providers should offer courses through renowned universities and adopt self-paced learning formats rather than fixed schedules. Additionally, learners should receive credits upon course completion, and these credits should be recognized by employers to enhance motivation for the continued use of MOOCs. Full article

Background/Objectives: Efficient management of intensive care unit (ICU) resources is a critical challenge for modern healthcare systems, which must balance high-quality patient care with operational and financial performance. ICU length of stay (LOS) is a key metric of clinical complexity and hospital efficiency. However, traditional methods for predicting LOS often fail to capture the complex, nonlinear interactions among physiological, demographic, and treatment-related variables. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for enhancing predictive accuracy and supporting data-driven decision-making. Methods: This study presents a systematic review and meta-analysis of ML and DL approaches for predicting ICU LOS in adult patients. Following PRISMA guidelines, eight scientific databases were searched, yielding 33 eligible studies published between 2015 and 2025. Results: Mixed medical–surgical ICUs were the most common setting (51.5%), and 45.5% of datasets were sourced from public repositories. Most studies (19/33) focused on binary classification of prolonged stays, although thresholds ranged from >48 h to ≥14 days. The pooled results from ten studies yielded an AUROC of 0.9005 (95% CI: 0.8890–0.9121), indicating strong predictive capability across diverse clinical contexts. Subgroup analyses showed comparable performance between specialized surgical and general ICUs. Conclusions: These findings suggest that AI-driven LOS prediction models exhibit strong discriminatory power for ICU LOS prediction, supporting hospital capacity planning. However, to translate this into reliable clinical support, the methodological heterogeneity, scarcity of external validation, and near absence of calibration reporting identified in this review need to be addressed. Full article

Polycyclic aromatic hydrocarbons (PAHs), such as benzo[a]pyrene (B[a]P), are major risk factors for lung cancer and other diseases, acting through the aryl hydrocarbon receptor (AHR). Alveolar macrophages (AMs) help regulate the lung microenvironment by responding to inhaled toxicants and resident microbiota. Although small extracellular vesicles (sEVs, aka exosomes) released by AMs mediate intercellular communication and immune responses, the influence of lung microbiota on sEV biogenesis and the mechanisms underlying sEV dysregulation during PAH exposure remain unknown. Here, we investigated the interplay between AMs, B[a]P, and lung microbiota, focusing on sEV-associated miRNAs (exo-miRNAs). Murine AMs (MH-S) were exposed to varying B[a]P concentrations in the presence or absence of murine lung microbiota with or without an AHR antagonist. sEVs from each condition were characterized and profiled for miRNA. Distinct miRNA signatures emerged: high-dose B[a]P enriched miRNAs linked to cancer progression, whereas lung microbiota alone or with low-dose B[a]P induced tumor-suppressor miRNAs that limit proliferation and metastasis and promote apoptosis, an effect enhanced by AHR antagonism. Lung microbiota appeared to counteract high-dose B[a]P by modulating tumor-suppressive exo-miRNAs. This study demonstrates that lung microbiota-induced exo-miRNAs critically shape AM-derived sEV-miRNA signaling during PAH exposure. The identified exosomal miRNAs could serve as important exposure biomarkers and therapeutic targets for mitigating B[a]P-induced toxicity and cancer development. Full article

Background/Objectives: Nurses and dialysis technicians are primarily responsible for cannulation in in-center and satellite dialysis units. Despite being a core component of hemodialysis care, existing clinical guidelines offer limited standardization, resulting in practice variability across facilities. Therefore, clinical expertise and adherence to consistent standards are essential to ensure safe and effective vascular access management. The study aimed to investigate the variables related to patients and nurses that contribute to unsuccessful vascular access cannulations, as well as the actions taken in response to cannulation failure, in a tertiary dialysis center in the Eastern Region of Saudi Arabia. Methods: This retrospective analytic study reviewed the records of 228 adult hemodialysis patients at King Fahad Military Medical Complex from 2020 to 2024, analyzing demographic, clinical, vascular access, and nursing variables associated with cannulation failure using descriptive statistics, the chi-square test, and t-tests. Ethical approval was obtained, and data were de-identified and manually extracted from nursing and dialysis documentation. Results: Most patients had hypertension and diabetes, with significant comorbidity burdens. Infiltration (61%) and clot formation (30.7%) were the primary complications of cannulation failure. Significant associations emerged with recurrent stroke and peripheral vascular disease, but not with nurse or patient demographics, suggesting vascular factors outweigh staff variables in cannulation risk. Cannulation failures were most common in patients with vascular comorbidities, while staff experience and education had no significant impact. Conclusions: Recommendations include implementing tailored protocols, providing ongoing nurse education, conducting systematic vascular assessments, and holding regular team reviews to enhance access outcomes and patient safety. Full article

As optical access networks continue to evolve toward higher capacity, longer reach, and increased user density, accurately predicting transmission performance has become increasingly complex. Conventional physics-based models often struggle to capture the nonlinear and stochastic behavior of modern passive optical networks (PONs), particularly under diverse operating conditions. In this study, a hybrid deep learning (DL) framework is proposed for the prediction of key performance indicators, including Q-factor, receiver sensitivity, and bit error rate (BER), in asymmetric 160/80 Gbps TWDM-PON systems, which is the target capacity by ITU-T G.989.1 specifications. The proposed approach integrates Gradient Boosting Regression and Multi-Layer Perceptron models within an ensemble learning structure to enhance robustness and predictive accuracy. A synthetic dataset comprising 1000 samples was generated to emulate realistic transmission scenarios with variations in distance, power level, and noise conditions for both upstream and downstream channels. Experimental results demonstrate strong agreement between the proposed DL-based predictions and conventional optical simulation outcomes, while the proposed predictions achieve superior adaptability and reduced computational complexity. High coefficients of determination (R² > 0.94) and low error metrics confirm the effectiveness of the framework, highlighting its potential as a fast and reliable alternative to traditional performance evaluation methods in next-generation optical access networks. Full article