On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers.

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

Looking for Crumbs in the Obesity Forest: Anti-obesity Interventions and Obesity-Associated Cardiometabolic Traits in the Mexican Population. History and Systematic Review With Meta-Analyses.

Mexicans and Mexican Americans share culture, genetic background, and predisposition for chronic complications associated with obesity and diabetes making imperative efficacious treatments and prevention. Obesity has been treated for centuries focused-on weight loss while other treatments on associated conditions like gout, diabetes (T2D), and hypertriglyceridemia. To date, there is no systematic review that synthesizes the origin of obesity clinics in Mexico and the efforts to investigate treatments for obesity tested by randomized clinical trials (RCT). We conducted systematic searches in Pubmed, Scopus, and Web of Science to retrieve anti-obesity RCT through 2019 and without an inferior temporal limit. The systematic review included RCT of anti-obesity treatments in the Mexican adult population, covering alternative medicine, pharmacological, nutritional, behavioral, and surgical interventions reporting metabolism-associated traits such as BMI, weight, waist circumference, triglycerides, glucose, among others. Only the studies with at least 3 months of treatment were included in the meta-analyses in order to reduce placebo effects. We found 634 entries, after removal of duplicates and screening the studies based on eligibility criteria, we analyzed 43 national, and 2 multinational-collaborative studies. Most of the national studies had small sample sizes, and the implemented strategies do not have replications in the population. The nutrition/behavioral interventions were difficult to blind, and most studies have medium-to-high risk of bias. Nutritional/behavioral interventions and medications showed effects on BMI, waist circumference, and blood pressure. Simple measures like pure water instead of sweet beverages decrease triglycerides and systolic blood pressure. Dark chocolate showed the highest effect for BMI and high blood pressure, and treatment with insulin increased weight in those with T2D. The study of obesity in Mexico has been on-going for more than four decades, the interest on RCT just increased until this millennium, but with small sample sizes and lack of replication. The interventions affect different cardiometabolic associated traits, which should be analyzed in detail in the population living near the Mexico-U.S. border; therefore, bi-national collaboration is desirable to disentangle the cultural effects on this population's treatment response. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020221436, identifier: CRD42020221436.

[Consenso experto en tiempos de COVID-19: aplicaciones del método Delphi en materia de salud].

La epidemia de COVID-19 ha modificado la cultura de la comunicación. La solución para los problemas de salud puede ser asertiva cuando es consensuada. El método Delphi es una herramienta de consenso que emplea rondas de listas de preguntas para recopilar información del conocimiento de un panel de expertos que analizan planteamientos y posibles soluciones a problemas. Se basa en la premisa de que, con la libertad del anonimato, la inteligencia combinada mejora el juicio individual y captura la opinión colectiva experta. El proceso del método es muy flexible, pues las rondas de preguntas pueden realizarse de manera presencial o remota. En este artículo se describe cómo implementar el método Delphi convencional en tiempos de confinamiento, y se analizan la utilidad y las limitaciones del método para su uso por expertos en salud para la resolución de problemas de tratamiento, diagnóstico o administrativos. Las tecnologías actuales para recolectar los datos permiten gran flexibilidad en el formato de los cuestionarios y facilitan la recopilación de la opinión experta. Gracias a su adaptabilidad, el método Delphi se está convirtiendo en una estrategia popular que involucra los ámbitos cualitativo y cuantitativo.