Background The renin-angiotensin-aldosterone system was shown to be activated in severe COVID-19 infection. We aimed to investigate the relationship between angiotensin converting enzyme (ACE) levels, ACE gene polymorphism, type 2 diabetes (T2DM), and hypertension (HT) and the prognosis of COVID-19 infection. Methods This cross-sectional study analyzed the clinical features of adult patients with SARS-CoV-2 infection. ACE gene analysis and ACE level measurements were performed. The patients were grouped according to ACE gene polymorphism (DD, ID or II), disease severity (mild, moderate, or severe), and the use of dipeptidyl peptidase-4 enzyme inhibitor (DPP4i), ACE-inhibitor (ACEi) or angiotensin receptor blocker (ARB). Intensive care unit (ICU) admissions and mortality were also recorded. Results A total of 266 patients were enrolled. Gene analysis detected DD polymorphism in the ACE 1 gene in 32.7% (n = 87), ID in 51.5% (n = 137), and II in 15.8% (n = 42) of the patients. ACE gene polymorphisms were not associated with disease severity, ICU admission, or mortality. ACE levels were higher in patients who died (p = 0.004) or were admitted to the ICU (p<0.001) and in those with severe disease compared to cases with mild (p = 0.023) or moderate (p<0.001) disease. HT, T2DM, and ACEi/ARB or DPP4i use were not associated with mortality or ICU admission. ACE levels were similar in patients with or without HT (p = 0.374) and with HT using or not using ACEi/ARB (p = 0.999). They were also similar in patients with and without T2DM (p = 0.062) and in those with and without DPP4i treatment (p = 0.427). ACE level was a weak predictor of mortality but an important predictor of ICU admission. It predicted ICU admission in total (cutoff value >37.092 ng/mL, AUC: 0.775, p<0.001). Conclusion Our findings suggest that higher ACE levels, but not ACE gene polymorphism, ACEi/ARB or DPP4i use, were associated with the prognosis of COVID-19 infection. The presence of HT and T2DM and ACEi/ARB or DPP4i use were not associated with mortality or ICU admission.

Natural hazard assessments are core to risk definition and early warning systems and play a fundamental role in the prevention of major damages. Traditional hazard identification methods are static. For this reason, new information and conditions cannot be easily included in the pre-defined hazard assessments. The Bayesian Networks can be used effectively for dynamic hazard identification. In this study, a methodology based on the Bayesian Networks model is presented for dynamic avalanche hazard assessment, in which changed and renewed data can be included in the system. In the proposed methodology, the integration of the Bayesian Networks and Geographical Information Systems (GIS) is modeled in the National Spatial Data Infrastructure (NSDI) perspective. In this structure, it is possible to combine and analyze the data obtained from different sources and factors for avalanche hazard can be dynamically updated with real-time updated data and temporal hazard mapping can be produced. The proposed methodology provides a generic structure and has an attribute making it applicable for dynamic mapping studies for other disasters.

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