Abstract Background: Acute respiratory tract infections (ARTIs) remain one of the leading causes of morbidity and Published: June 15, 2026 hospitalization among preschool children worldwide. particularly in low- and middle- income countries. Vitamin D plays a crucial immunomodulatory role in respiratory health by enhancing innate immunity and maintaining epithelial barrier integrity. However, vitamin D deficiency is highly prevalent among children, including those living in sun- rich regions such as Libya. Objectives: This study aimed to assess serum vitamin D levels among preschool children admitted with ARTIs at Gharyan Central Teaching Hospital and to evaluate the association between vitamin D status, disease severity, and recurrence. Methods: A cross-sectional observation study was conducted among 100 preschool children aged 1-6 years admitted with clinically diagnosed ARTIs between January and December 2025. After applying the inclusion and exclusion criteria, 79 eligible participants were included in the final analysis. Serum 25-hydroxyvitamin D [25(OH)D] levels were measured and categorized according to Endocrine Society guidelines. Associations between vitamin D levels, ARTIs severity, and recurrence were analysed using chi-square tests and logistic regression models. Results: Vitamin D deficiency and insufficiency were highly prevalent, affecting 87.3% of the studied children. Although lower vitamin D levels were more frequently observed among children with moderate and severe ARTIs, the association with disease severity was not statistically significant (p=0.115). In contrast, a significant association was identified between low vitamin D levels and recurrent ARTIs (p=0.019). Conclusion: Vitamin D deficiency is highly prevalent among preschool children hospitalized with ARTIs and is significantly associated with recurrent infections. Improving vitamin D status may contribute to reducing the recurrence of ARTIs in this population.

Medical education is being reshaped by two major technological disruptions: the rapid expansion of online learning since the COVID-19 pandemic and the equally rapid adoption of artificial intelligence (AI) in teaching, assessment, and clinical training. These developments are often treated as distinct phases of change. In reality, they are part of the same broader challenge: educational institutions have repeatedly adopted new technologies faster than they have developed the pedagogic, ethical, and regulatory frameworks needed to use them well. 1-3

Pneumonia remains a major global health burden, where timely recognition on chest X ray images is clinically important yet often challenged by subtle radiographic signs and variability in interpretation. This paper presents a controlled comparative evaluation of four convolutional neural network architectures, MobileNet, ResNet50, VGG, and InceptionV3, for binary classification of chest X ray images into diseased and normal cases. Experiments were conducted using a publicly available Kaggle dataset of 4,479 images under a unified preprocessing and evaluation protocol. Performance was assessed on a held out test set of 300 images, including 200 diseased and 100 normal cases, using accuracy and macro averaged precision, recall, and F1 score, supported by confusion matrix analysis. The results show that MobileNet achieved the highest test accuracy at 95.0 percent, while ResNet50 and VGG achieved 94.7 percent, and InceptionV3 achieved 92.0 percent. Confusion matrix inspection indicates that MobileNet produced the fewest false negatives for diseased cases in this setting, which is important for screening oriented use. Inference time measurements using batch size 1 at 180 × 180 input on CPU further highlight the efficiency advantage of lightweight architectures. Overall, these findings provide a reproducible benchmark to support architecture selection for computer assisted pneumonia screening and clinical triage

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