RESUMO
Background@#As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. @*Results@#In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. @*Conclusions@#This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.
RESUMO
Purpose@#This study aims to compare the general characteristics, life-style, health examination results, and sick leave days by airmen medical examination decision and to investigate factors affecting sick leave days. @*Methods@#We obtained data from 2,361 Korean pilots who worked for a commercial airline. Comparison of the results by airmen medical examination decision (Fit or waver) was conducted using the x 2 test or Fisher’s exact test. Factors affecting sick leave days were analyzed using logistic regression. @*Results@#Age, smoking history, blood pressure, obesity, and fasting blood sugar level were significantly different between the Fit and Waver groups. Rate of using sick leave long-term was higher in the Waver than in the Fit. Sick leave days were significantly associated with age, habits of drinking, and smoking in the Fit group. @*Conclusion@#This study demonstrated the health risk factors that affect the number of sick leave days. By providing basic data for the health care of workers, it is expected to be applicable to the provision of health promotion and disease prevention programs for workers.