Background: Ventilator-associated events (VAEs) are major healthcare-associated complications among mechanically ventilated patients, contributing to increased morbidity, prolonged intensive care unit (ICU) stay, and higher healthcare costs. Critical care nurses play a pivotal role in implementing evidence-based strategies to prevent VAEs; however, evidence regarding their knowledge, attitudes, and practices (KAP) remains limited, particularly in Saudi Arabia. Objective: To assess critical care nurses' knowledge, attitudes, and practices regarding VAE prevention, examine the relationships among these domains, and determine differences according to demographic and professional characteristics. Methods: A descriptive cross-sectional correlational study was conducted among 89 critical care nurses working in the adult ICU, pediatric ICU, and neonatal ICU at King Abdulaziz Hospital, Al Ahsa in Saudi Arabia. Participants were recruited through convenience sampling and completed a validated self-administered questionnaire assessing demographic characteristics, knowledge, attitudes, and practices related to VAE prevention. Descriptive statistics summarized participant characteristics and domain scores. Pearson's correlation examined relationships among KAP domains, while Mann–Whitney U and Kruskal–Wallis tests compared scores across participant characteristics. Statistical significance was set at p < .05. Results: Participants demonstrated highly positive attitudes and high self-reported adherence to recommended VAE prevention practices, with median scores of 20 for both domains, whereas knowledge scores were more variable (median = 6). Knowledge was positively associated with practice (r = 0.257, p = .015). Specialized training was associated with higher knowledge scores (p = .010), while age was associated with attitude (p = .038). No significant differences in practice were observed across demographic or professional characteristics. Conclusion: Critical care nurses demonstrated positive attitudes and high adherence to evidence-based VAE prevention practices despite variability in knowledge. The association between knowledge and practice highlights the importance of structured education and competency-based training to strengthen evidence-based care. These findings provide baseline evidence to support nursing education, leadership, and quality improvement initiatives aimed at enhancing patient safety in Saudi critical care settings.

Capsule retention is a rare but clinically significant complication of video capsule endoscopy (VCE), particularly in patients with refractory celiac disease. We report the case of a 42-year-old man with type II refractory celiac disease who developed jejunal capsule retention after VCE despite a negative magnetic resonance enterography (MRE). Conservative treatment with prednisone and laxatives resulted in spontaneous capsule passage after 30 days, avoiding endoscopic or surgical retrieval. This case highlights the importance of careful patient selection, pre-procedural risk assessment, and individualized management of capsule retention in inflammatory small bowel disease.

Losses of data are a widespread issue of the real-world tabular data, utilized in machine learning (ML). Missing values may dramatically hamper the quality of the model, be biased, and result in incorrect inferences unless addressed correctly. This is a systematic literature review (SLR) that explores and syntheses 52 research articles published 2020-2026 in high-impact peer review journals. The review is done under the guidelines of PRISMA (Preferred Reporting Items to Systematic Reviews and Meta-Analyses). Methods of imputation can be divided into 5 broad categories: statistical and conventional imputation methods (mean, median, mode, and Last Observation Carried Forward), machine learning-based methods (k-Nearest Neighbors, Random Forest, Decision Trees and Support Vector Machines), multiple imputation methods (including MICE, missForest and missRanger), deep learning-based methods (including Autoencoders, Vari There is a systematic comparison between methods based on type of dataset, missing data mechanism (MCAR, MAR, MNAR), evaluation measures (RMSE, MAE, accuracy, AUC), computational complexity and scalability. Using our results, it appears that, up to low missingness rates, conventional approaches are equally competitive, but that deep generative models (with GAN-based models or diffusion-based models being two different approaches to the same task) are matched when applied to high-dimensional and heterogeneous tabular data. However, there is no one particular approach that prevails in all situations. This review finds the overall gaps in research, such as the absence of standardized benchmarks, the relative dearth of interest in MNAR mechanisms, and the lack of research on imputation in federated learning. The results give practical advice to practitioners and researchers to use the right imputation techniques when using tabular ML tasks.

Background: Thin gingival phenotype is associated with increased susceptibility to gingival recession, soft-tissue instability, and compromised esthetic outcomes. The present study evaluated the effectiveness of microneedling (MN) with and without concentrated platelet-rich fibrin (C-PRF) for gingival phenotype modification. Methods: This prospective split-mouth comparative clinical study included 42 sites, with 21 sites each in the group A and group B. Ground A sites received MN with adjunctive C-PRF, whereas Group B sites received MN alone. Gingival thickness (GT) and keratinized tissue width (KTW) were assessed at baseline, 3 months, and 6 months. Postoperative pain was assessed using a visual analogue scale (VAS). Patient-level mean values were used for statistical analysis. Results: Both groups showed significant improvement in GT and KTW from baseline to follow-up. Group A demonstrated significantly greater GT than Group B at 3 and 6 months (P <0.05). Intergroup differences in KTW were not statistically significant. Pain scores decreased progressively in both groups without significant intergroup differences. Conclusion: Adjunctive use of C-PRF with microneedling resulted in superior improvement in gingival thickness and may represent an effective minimally invasive option for gingival phenotype enhancement.

The Regional Security Complex Theory (RSCT) developed by Buzan and Wæver (2003) has conventionally treated the Indian Ocean Region (IOR) and West Asia as analytically distinct security complexes, connected only loosely through great-power overlay. The 2024 enlargement of BRICS which brought Egypt, Ethiopia, Iran, and the United Arab Emirates into full membership alongside founding member India, with Saudi Arabia occupying an unresolved associate status has drawn actors from both complexes into a single multilateral institutional framework. This paper asks whether this development constitutes a genuine merger of the two regional security complexes, an instance of institutional overlay, or a distinct pathway of regional change that RSCT has not yet theorized. Using a qualitative, historical-comparative method that examines BRICS summit declarations, energy and connectivity linkages, and the persistence of intra-bloc security frictions, the paper argues that BRICS enlargement has produced institutional convergence without corresponding security convergence. This decoupling carries direct implications for India, which now occupies the institutional seam between both complexes and must manage this convergence while preserving its strategic autonomy.

Artificial intelligence [AI] has become an indispensable part of scientific and medical research, however, the exponential growth in model complexity and computational requirements has made AI adoption challenging for resource-limited settings. The concept of Frugal AI has emerged to address this gap by designing efficient, accessible, and sustainable AI systems that achieve comparable accuracy with reduced computational, financial, and environmental costs. In the context of medical research, Frugal AI enables data-driven discovery, diagnostics, and decision-making through low-cost, scalable, and adaptable methodologies. This review explores the evolution and influence of Frugal AI on research methodology in biomedical sciences over the past decade. It discusses its principles, technological innovations, and integration into experimental design, data management, and analytical frameworks. Furthermore, the paper critically evaluates the benefits and limitations of Frugal AI in medical research, highlighting its transformative role in improving accessibility, reducing bias, and fostering innovation in low-resource environments. Finally, it underscores the future perspectives of Frugal AI in achieving equitable, reproducible, and sustainable medical research worldwide.

Background: Abnormal uterine bleeding (AUB) in perimenopausal women may indicate a spectrum of endometrial pathology ranging from atrophic change to carcinoma, and transvaginal sonographic measurement of endometrial thickness (ET) has been proposed as a non-invasive tool to triage women who require histopathological sampling. This study aimed to determine the distribution of ET and histopathological diagnoses among perimenopausal women with AUB and to evaluate the diagnostic performance of ET cut-off values in predicting endometrial pathology. Materials & Methods: This cross-sectional study was conducted in the Department of Radiology and Imaging, Sylhet MAG Osmani Medical College Hospital, Bangladesh, in collaboration with the Departments of Obstetrics and Gynaecology and Pathology, from July 2021 to June 2023. Ninety-seven perimenopausal women aged 39 years and above presenting with AUB underwent transvaginal sonography followed by histopathological evaluation of endometrial samples. ET was correlated with histopathological findings using the chi-square test. Results: The mean age was 43.04±2.54 years; menorrhagia was the commonest symptom (51.5%). The overall mean ET was 7.98±3.32 mm, ranging from 5.83±1.06 mm in atrophic endometrium to 12.15±2.36 mm in endometrial polyps. ET was significantly associated with histopathological diagnosis (χ²=21.74, p<0.001). An ET cut-off of >4.9 mm gave 100% sensitivity and negative predictive value but 24.0% specificity; >7.9 mm gave the best balance (66.0% sensitivity, 76.0% specificity, 71.1% accuracy); >11.9 mm gave 100% specificity and positive predictive value but 29.8% sensitivity. Conclusion: Endometrial thickness correlates significantly with histopathological diagnosis in perimenopausal women with AUB. No single cut-off simultaneously optimises both rule-in and rule-out performance; a graded diagnostic approach combining ET with clinical judgement is recommended before proceeding to invasive sampling.