Assessment of work-related stress utilizing the Find My Stress mobile application among university students and adult workers amidst the COVID-19 pandemic.

OBJECTIVES: This cross-sectional study aimed to evaluate work-related stress and the efficacy associated with the newly developed Find My Stress mobile application. The global impact of the COVID-19 pandemic has significantly influenced the quality of life, transcending geographical boundaries and inducing stress that has detrimentally affected health and work efficiency. METHODS: A total of 440 male and female participants, comprising university students and adult workers, were enrolled in the study. Participants completed an assessment in the application that consisted of three components: 1) perceived work stress, 2) environmental stress factors, and 3) application efficiency. RESULTS: University students exhibited higher perceived stress levels compared to adult workers (p = 0.031). The predominant physical factors contributing to musculoskeletal disorders in university students were identified as movement and posture factors, particularly related to vibration and organization. Conversely, environmental factors took precedence in adult workers, followed by posture and movement. The reliability of the perceived work stress questionnaire was evaluated by Cronbach's alpha coefficient and yielded a value of 0.96. The Find My Stress application demonstrated high efficiency. CONCLUSIONS: Elevated levels of work stress were observed in both university students and adult workers. Initial signs of musculoskeletal disorders in university students primarily manifested in the neck and upper back, arms, and hands, while adult workers predominantly reported complaints related to the arms and hands. The Find My Stress application emerges as a valuable tool for screening occupational stressors.

Development of a diagnostic support system for the fibrosis of nonalcoholic fatty liver disease using artificial intelligence and deep learning.

Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or possibly hepatocellular carcinoma. The development of artificial intelligence and deep learning have begun to play a significant role in fibrosis detection. This study aimed to develop SMART AI-PATHO, a fully automated assessment method combining quantification of histopathological architectural features, to analyze steatosis and fibrosis in nonalcoholic fatty liver disease (NAFLD) core biopsies and employ Metavir fibrosis staging as standard references and fat assessment grading measurement for comparison with the pathologist interpretations. There were 146 participants enrolled in our study. The correlation of Metavir scoring system interpretation between pathologists and SMART AI-PATHO was significantly correlated (Agreement = 68%, Kappa = 0.59, p-value <0.001), which subgroup analysis of significant fibrosis (Metavir score F2-F4) and nonsignificant fibrosis (Metavir score F0-F1) demonstrated substantial correlated results (agreement = 80%, kappa = 0.61, p-value <0.001), corresponding with the correlation of advanced fibrosis (Metavir score F3-F4) and nonadvanced fibrosis groups (Metavir score F0-F2), (agreement = 89%, kappa = 0.74, p-value <0.001). SMART AI-PATHO, the first pivotal artificially intelligent diagnostic tool for the color-based NAFLD hepatic tissue staging in Thailand, demonstrated satisfactory performance as a pathologist to provide liver fibrosis scoring and steatosis grading. In the future, developing AI algorithms and reliable testing on a larger scale may increase accuracy and contribute to telemedicine consultations for general pathologists in clinical practice.