BACKGROUND: Medical students are usually under more stress than that experienced by non-medical students. Stress testing tools for Korean medical students have not been sufficiently studied. Thus, we adapted and modified the East Asian Student Stress Inventory (EASSI), a stress testing tool for Korean students studying abroad, and verified its usefulness as a stress test in Korean university students. We also compared and analyzed stress levels between medical and non-medical students. METHODS: A questionnaire survey was conducted on medical and non-medical students of a national university, and the responses of 224 students were analyzed for this study. Factor analysis and reliability testing were performed based on data collected for 25 adapted EASSI questions and those on the Korean version of the Global Assessment of Recent Stress Scale (GARSS). A correlation analysis was performed between the 13 modified EASSI questions and the GARSS, and validity of the modified EASSI was verified by directly comparing stress levels between the two student groups. RESULTS: The 13 questions adapted for the EASSI were called the modified EASSI and classified into four factors through a factor analysis and reliability testing. The Pearson's correlation analysis revealed a significant correlation between the modified EASSI and the Korean version of the GARSS, suggesting a complementary strategy of using both tests. CONCLUSION: The validity and reliability of the EASSI were verified. The modified Korean EASSI could be a useful stress test for Korean medical students. Our results show that medical students were under more stress than that of non-medical students. Thus, these results could be helpful for managing stress in medical students.
Asian Continental Ancestry Group* ; Exercise Test ; Humans ; Reproducibility of Results ; Stress, Psychological ; Students, Medical*

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.