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
Indoor and outdoor air pollution has been considered a serious public health problem worldwide, and is associated annually with around 7 million deaths (4.8 million associated with outdoor air and 2.2 million indoor air). The main reasons for these deaths include: chronic obstructive pulmonary diseases, pneumonia, ischemic cardiopathy and lung cancer. In addition, epidemiological studies have associated exposure to this type of pollutants with a greater susceptibility to the development of infectious and non-infectious diseases. One of the most important infectious diseases is tuberculosis, which over the years has worsened with the emergence of resistant multi-drug strains, as well as with association with other diseases such as diabetes mellitus type 2 and the acquired immune deficiency syndrome (AIDS). In fact, despite efforts made by the World Health Organization to stop the epidemic, a large number of deaths (about 1.7 million worldwide) are still caused by this disease. In this review, a brief summary will be made of the effects of exposure to indoor and outdoor air pollution on the innate immune response against tuberculosis, and how these alterations could be linked to the development of pulmonary tuberculosis.
La contaminación del aire de interiores y de exteriores ha sido considerada un serio problema de salud pública a nivel mundial, el cual se asocia anualmente con alrededor de 7 millones de muertes (4.8 millones asociadas con el aire de exteriores y 2.2 millones con el aire de interiores). Entre las principales razones ligadas a estas muertes se encuentran: enfermedad pulmonar obstructiva crónica, neumonía, cardiopatía isquémica y cáncer de pulmón. Además, estudios epidemiológicos han asociado la exposición a este tipo de contaminantes con mayor susceptibilidad para la aparición de enfermedades infecciosas y no infecciosas. Entre las enfermedades infecciosas, una de las más importantes es la tuberculosis, la cual, durante el transcurso de los años, ha empeorado con el surgimiento de cepas resistentes a múltiples fármacos, así como por la asociación con otras afecciones como la diabetes mellitus tipo 2 y el síndrome de inmunodeficiencia adquirida (sida). A pesar del esfuerzo realizado por la Organización Mundial de la Salud (OMS) por detener esta epidemia, aún existen anualmente un gran número de muertes causadas por esta enfermedad: alrededor de 1.7 millones a nivel mundial. En esta revisión se hará un breve resumen de los efectos que tiene la exposición a la contaminación del aire de interiores y de exteriores en la respuesta inmune innata en contra de la tuberculosis, y cómo estas alteraciones pueden estar ligadas a la aparición de tuberculosis pulmonar.
Subject(s)
Humans , Tuberculosis , Public Health , Acquired Immunodeficiency Syndrome , Air Pollution , Particulate Matter , Immunity , Immunity, InnateABSTRACT
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.