Mohamed G Khattap’s research while affiliated with Suez University and other places

Introduction: Radiography students experience high levels of stress due to demanding academic studies and clinical training. Expanding this study to different settings could provide insights for customized interventions. This study aims to investigate the prevalence and causes of stress experienced by radiography students across university settings in Egypt, the UAE, and Jordan, with particular attention to gender-based differences. By analyzing stress levels across these diverse academic environments, the study seeks to identify key stressors specific to each region, as well as any differences in stress impact related to gender, academic demands, and cultural contexts. Methods: This cross-sectional study involved 608 radiography students from accredited programs in Egypt, UAE, and Jordan, selected through convenience and snowball sampling. A 96-question online survey, developed following CHERRIES guidelines, was distributed via university channels and social media. Data was analyzed using IBM SPSS. The questionnaire's reliability was confirmed through a test-retest method, yielding a Pearson correlation coefficient of 0.82. Results: Out of 608 radiography students surveyed, stress levels differed by gender. Specifically, 11.7 % of students reported extremely high stress, with 5 % of males and 14.4 % of females. Gender disparities were evident that females experienced significantly higher stress than males in areas such as quality of education , exams, GPA, fear of mistakes, clinical factors, patient care, lack of relaxation time, and health problems (p < 0.001). In the UAE, 41.8 % of students believe a lack of time with family and friends significantly affects their stress, followed closely by 41.7 % in Egypt and 30.5 % in Jordan (p ¼ 0.001). Conclusion: The study identifies gender-specific discrepancies in stress among university students, demonstrating that female students experience higher stress levels, influenced by factors such as academic standards, examinations, GPA, and work-life balance. Furthermore, students in Jordan and the UAE are facing academic workload stress, while Egyptian students deal with career uncertainty and family-related stress. Impact on practice: The study emphasizes the importance of understanding diverse stressors among radiography students to provide insights that can inform tailored support strategies to enhance the well-being and academic performance of radiography students in these regions.

This paper proposes an alternative detection framework for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90%.

Breast cancer is a significant and deadly threat to women globally. Moreover, Breast cancer metastasis is a complicated process involving multiple biological stages, which is considered a substantial cause of death, where cancer cells spread from the original tumor to other organs in the body-representing the primary mortality factor. Circulating tumor cells (CTCs) are cancer cells detached from the primary or metastatic tumor and enter the bloodstream, allowing them to establish new metastatic sites. CTCs can travel alone or in groups called CTC clusters. Studies have shown that CTC clusters have more potential for metastasis and a poorer prognosis than individual CTCs in breast cancer patients. However, our understanding of CTC clusters' formation, structure, function, and detection is still limited. This review summarizes the current knowledge of CTC clusters' biological properties, isolation, and prognostic significance in breast cancer. It also highlights the challenges and future directions for research and clinical application of CTC clusters.