ABSTRACT
ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.
ABSTRACT
It has been reported that patients with progressive tuberculosis (TB) express abundant amounts of the antimicrobial peptides (AMPs) cathelicidin (LL-37) and human neutrophil peptide-1 (HNP-1) in circulating cells, whereas latent TB infected donors showed no differences when compared with purified protein derivative (PPD) and QuantiFERON®-TB Gold (QFT)-healthy individuals. The aim of this study was to determine whether LL-37 and HNP-1 production correlates with higher tuberculin skin test (TST) and QFT values in TB household contacts. Twenty-six TB household contact individuals between 26-58 years old TST and QFT positive with at last two years of latent TB infection were recruited. AMPs production by polymorphonuclear cells was determined by flow cytometry and correlation between TST and QFT values was analysed. Our results showed that there is a positive correlation between levels of HNP-1 and LL-37 production with reactivity to TST and/or QFT levels. This preliminary study suggests the potential use of the expression levels of these peptides as biomarkers for progression in latent infected individuals.