This study investigates the impact of nutritional status on academic performance among schoolchildren in Eastern Morocco. Focusing on the prevalence of overweight, obesity, and their associations with academic outcomes, the research underscores the significance of physical well-being in educational achievement. Conducted as a cross-sectional analysis in March 2022, the survey encompassed eight public and two private schools, selected through random sampling. Classes within these schools were also randomly chosen. Utilizing a self-administered, anonymous questionnaire, completed individually by students in the presence of a trained dietician, the study also involved anthropometric measurements and clinical examinations. Additionally, students' grade point averages (GPAs) were obtained from school records. The survey comprised 596 students, with an average age of 14.86 ± 1.98 years, height of 160.47 ± 11.84 cm, and weight of 51.28 ± 11.49 kg. The prevalence of underweight was recorded at 8.7%, overweight at 10.7%, and obesity at 2.7%. Statistical analysis using the Analysis of Variance (ANOVA) test revealed a significant association between obesity and diminished academic performance, indicating the need for attention to obesity among adolescents in this region. The findings suggest that national-level prevalence determination of overweight and obesity by health policymakers is crucial for this age group. Identifying risk factors associated with these conditions is imperative for effective prevention and early intervention. In this context, the promotion of physical activity and healthy eating habits is vital for fostering healthy, successful school environments. This research contributes to the understanding of how physical health, particularly nutritional status, influences academic outcomes. It highlights the need for integrated approaches that consider the physical well-being of students as a critical factor in educational success. The study's implications extend beyond academic circles, offering insights for policymakers and educators in developing holistic strategies to enhance both health and educational outcomes.

Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.

Tuberculosis (TB), an airborne disease caused by Mycobacterium, poses a significant global health challenge due to its rapid transmission through air and interaction with infected individuals. This study presents a comprehensive dynamic model to assess the impact of TB treatment and vaccination strategies in Nigeria, focusing on the comparative analysis of untreated and treated populations, as well as evaluating mortality and recovery outcomes. Through simulations conducted using the Berkeley Madonna Software, it was observed that the populations of latent and susceptible individuals exhibit a near-equivalence, yet the cohort undergoing treatment markedly surpasses other groups. Interestingly, the infected demographic aligns closely with the average values across all compartments. An alarming trend was noted in chronic patients, whose numbers initially increase, followed by a decline over a six-year period, and then a subsequent rise, while the count of treated individuals demonstrates a continuous decrease. The study further reveals a pressing need for treatment among vaccinated individuals, highlighting a nuanced interplay between vaccination and therapeutic interventions. By employing stability and sensitivity analyses, this research underscores the critical importance of treatment in managing TB infection, advocating for enhanced strategies to mitigate the spread of this infectious disease. The findings contribute valuable insights into the dynamics of TB infection and the pivotal role of treatment, underscoring the necessity for integrated approaches in addressing the TB epidemic, particularly in regions burdened by high infection rates.