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.

Despite recent advances in the treatment of some forms of leishmaniasis, the available drugs are still far from ideal due to inefficacy, parasite resistance, toxicity and cost. The wide-spectrum antimicrobial activity of 2-nitrovinylfuran compounds has been described, as has their activity against Trichomonas vaginalis and other protozoa. Thus, the aim of this study was to test the antileishmanial activities of six 2-nitrovinylfurans in vitro and in a murine model of leishmaniasis. Minimum parasiticide concentration (MPC) and 50% inhibitory concentration (IC50) values for these compounds against the promastigotes of Leishmania amazonensis, Leishmania infantum and Leishmania braziliensis were determined, as were the efficacies of two selected compounds in an experimental model of cutaneous leishmaniasis (CL) caused by L. amazonensis in BALB/c mice. All of the compounds were active against the promastigotes of the three Leishmania species tested. IC50 and MPC values were in the ranges of 0.8-4.7 µM and 1.7-32 µM, respectively. The compounds 2-bromo-5-(2-bromo-2-nitrovinyl)-furan (furvina) and 2-bromo-5-(2-methyl-2-nitrovinyl)-furan (UC245) also reduced lesion growth in vivo at a magnitude comparable to or higher than that achieved by amphotericin B treatment. The results demonstrate the potential of this class of compounds as antileishmanial agents and support the clinical testing of Dermofural(r) (a furvina-containing antifungal ointment) for the treatment of CL.