Statins are pharmacological inhibitors of the activity of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR), an enzyme responsible for the synthesis of cholesterol. Some recent experimental studies have shown that besides their effects on the primary and secondary prevention of cardiovascular diseases, statins may also have beneficial anti-inflammatory effects through diverse mechanisms. On the other hand, the induction and activity of regulatory T cells (Treg) are key processes in the prevention of pathology during chronic inflammatory and autoimmune diseases. Hence, strategies oriented towards the therapeutic expansion of Tregs are gaining special attention among biomedical researchers. The potential effects of statins on the biology of Treg are of particular importance because of their eventual application asin vivoinducers of Treg in the treatment of multiple conditions. In this paper we review the experimental evidence pointing out to a potential effect of statins on the role of regulatory T cells in different conditions and discuss its potential clinical significance.

Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images. Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. Our best-performing model utilizes a seven-channel input and correctly predicted 12,876 out of 12,954 cases. We also generated a cross-validation confusion matrix that showed the results of five iterations, achieving 63,654 out of 64,126 true predictions. The model’s accuracy reached 99.51%, a precision of 99.26%, a recall of 99.26%, a specificity of 99.63%, an F1 score of 99.26%, and a loss of 2.3%. We are now developing a system based on real-world quality images to create a comprehensive detection tool for remote regions where trained microscopists are unavailable.

Objetivo Describir y analizar la actividad científica sobre la COVID-19 en Latinoamérica.Materiales y Métodos Mediante una revisión sistemática de la literatura en PubMed, LILACS y los repositorios preimpresos BioRxiv y medRxiv, se recuperaron todos los documentos disponibles sobre la COVID-19 en Latinoamérica desde el primero de enero hasta el 24 de abril de 2020, para su análisis bibliométrico.Resultados Un total de 29 publicaciones fueron incluidas en el análisis. El país con más producción científica fue Brasil (10/29; 34,4%), seguido de Colombia (6/29; 20,6%) y México (6/29; 20,6%). La universidad con más artículos fue la Universidad Tecnológica de Pereira, Colombia (5/29; 17,24%). El 41,3% de las publicaciones fueron documentos preimpresos. La mayoría de los estudios excluidos fueron comentarios editoriales u opiniones de expertos. Tres de cada cuatro investigaciones tenían un enfoque epidemiológico (21/29; 72,4%); limitados estudios sobre el diagnóstico (5/29; 17,2%), la fisiopatogenia (2/29; 2,8%) y los aspectos terapéuticos (1/29; 3,4%) fueron encontrados.Conclusión Pese al crecimiento exponencial de publicaciones en el mundo, existe una limitada cantidad de información sobre el comportamiento de esta infección en Latinoamérica. Se requiere la publicación de estudios con alta calidad metodológica, que aporten conocimiento sobre el impacto de la pandemia en la región.

Background: The deterioration of Venezuela's health system in recent years undoubtedly contributes to an increased impact of the COVID-19 pandemic. Understanding healthcare workers' (HCWs) knowledge, attitudes, and practices (KAPs) toward COVID-19 in the early stages of the pandemic could inform their medical training and improve their preparedness.Methods: A online national cross-sectional survey was conducted between May 26th and May 30th, 2020, to assess KAPs among HCWs in Venezuela.Results: A total of 1,441 HCWs from all 24 regions of the country responded to the survey. The mean age of the HCWs was 44 (SD [standard deviation] 14) years; most were women (66.4%). Most HCWs were specialized doctors (48%), followed by nurses (13%) and resident doctors (12.3%). The majority of HCWs had good knowledge (76.3%), obtained information mainly from scientific literature (85.4%); had negative attitudes (53.6%), felt uncomfortable with their work during the current pandemic (59.8%); and reported appropriate practices (76.9%). However, participation in COVID-19 related training was absent in more than half of the HCWs. Positive attitudes were significantly more frequent in frontline workers than in non-frontline workers (p = 0.001). Bioanalysts, students, and doctors were more likely to have good knowledge; participating in training was a predictor for positive attitudes and older age was an appropriate practice predictor.Conclusions: HCWs, knowledge in Venezuela could be improved by strengthening education and training programs. Strategies should focus on reducing fear and improving attitudes toward the care of COVID-19 patients, as well as the promotion of preventive practices.