Demographic and Comorbidities Data Description of Population in Mexico with SARS-CoV-2 Infected Patients(COVID19): An Online Tool Analysis.

The Word Health Organization (WHO) declared in March 2020 that we are facing a pandemic designated as COVID-19, which is the acronym of coronavirus disease 2019, caused by a new virus know as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In Mexico, the first cases of COVID-19, was reported by the Secretary of Health on 28 February 2020. More than sixteen thousand cases and more than fifteen thousand deaths have been reported in Mexico, and it continues to rise; therefore, this article proposes two online visualization tools (a web platform) that allow the analysis of demographic data and comorbidities of the Mexican population. The objective of these tools is to provide graphic information, fast and updated, based on dataset obtained directly from National Governments Health Secretary (Secretaría de Salud, SSA) which is daily refreshed with the information related to SARS-CoV-2. To allow a dynamical update and friendly interface, and approach with R-project, a well-known Open Source language and environment for statistical computing and Shiny package, were implemented. The dataset is loaded automatically from the latest version released by the federal government of Mexico. Users can choose to study particular groups determined by gender, entity, type of result (positive, negative, pending outcome) and comorbidity. The image results are plots that can be instantly interpreted and supported by the text summary. This tool, in addition to being a consultation for the general public, is useful in Public Health to facilitate the visualization of the data, allowing its timely interpretation due to the changing nature of COVID-19, it can even be used for decision-making by leaders, for the benefit of the health of the community.

Nested case-control study reveals increased levels of urinary proteins from human kidney toxicity panels in women predicted to develop preeclampsia.

PURPOSE: The aim of this study was to evaluate the usefulness of urine concentrations of 12 proteins as a risk parameter for developing preeclampsia (PE). METHODS: A nested case-control study was designed to determine protein concentrations in urine from women predicted to develop PE (WPD-PE) and normotensive pregnancies (controls). Protein profiles were determined at 12, 16 and 20 gestational weeks (GW) using the Bio-Plex Pro human kidney toxicity Panel 1 and Panel 2 (Bio-Rad). Receiver operating characteristic (ROC) curve analyses were performed. Correlations between proteins and clinical parameters at the time of PE diagnosis were also assessed. RESULTS: Significant differences were observed in urine cystatin C (Cys C) levels at 16 and 20 GW and clusterin at 20 GW between WPD-PE and controls (P < 0.05). ROC analysis revealed that Cys C at 16 GW had the highest area under the ROC curve (0.758). At 16 GW, patients with urine Cys C levels above 73.7 ng/mL had eightfold increased odds for developing PE (odds ratio 7.92; 95 % CI 1.3-47.5; P = 0.027). A positive correlation was found between urinary Cys C (at 16 and 20 GW) and leukocyte counts, total proteins, aspartate aminotransferase, alanine aminotransferase, bilirubin and lactate dehydrogenase at the time of PE diagnosis (P value < 0.05). CONCLUSIONS: Urinary Cys C and clusterin showed predictive value for PE development in our cohort. Further studies are needed to validate their use as predictive biomarkers for PE and/or their participation in PE pathogenesis.