Antiviral capacity of polypropylene/(1-Hexadecyl) trimethyl-ammonium bromide composites against COVID-19.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, scientists from different areas are looking for alternatives to fight it. SARS-CoV-2, the cause of the infectious respiratory disease COVID-19, is mainly transmitted through direct or indirect contact with infected respiratory droplets. The integrity of the virus structure is crucial for its viability to attack human cells. Quaternary ammonium salts are characterized by having antiviral capabilities which alter or destroy the structure of the viral capsid. In this work, polypropylene (PP)/(1-Hexadecyl) trimethyl-ammonium bromide (CTAB) composites have been prepared in order to create an antiviral material. The composites were melt processed and blown to produce thin films. The CTAB content on the antiviral effect was evaluated using antibodies and serum from infected patients with the SARS-CoV-2 virus. In addition, the mechanical and thermal properties of blown films were investigated, and CTAB release kinetics from the films was followed by UV-Vis. The results indicate that the virus tends to remain less on the polymer surface by increasing the amount of CTAB in the PP matrix.

An Analysis COVID-19 in Mexico: a Prediction of Severity.

BACKGROUND: Coronavirus disease 2019 (COVID-19) causes a mild illness in most cases; forecasting COVID-19-associated mortality and the demand for hospital beds and ventilators are crucial for rationing countries' resources. OBJECTIVE: To evaluate factors associated with the severity of COVID-19 in Mexico and to develop and validate a score to predict severity in patients with COVID-19 infection in Mexico. DESIGN: Retrospective cohort. PARTICIPANTS: We included 1,435,316 patients with COVID-19 included before the first vaccine application in Mexico; 725,289 (50.5%) were men; patient's mean age (standard deviation (SD)) was 43.9 (16.9) years; 21.7% of patients were considered severe COVID-19 because they were hospitalized, died or both. MAIN MEASURES: We assessed demographic variables, smoking status, pregnancy, and comorbidities. Backward selection of variables was used to derive and validate a model to predict the severity of COVID-19. KEY RESULTS: We developed a logistic regression model with 14 main variables, splines, and interactions that may predict the probability of COVID-19 severity (area under the curve for the validation cohort = 82.4%). CONCLUSIONS: We developed a new model able to predict the severity of COVID-19 in Mexican patients. This model could be helpful in epidemiology and medical decisions.