RESUMO
Severe COVID-19 patients develop acute respiratory distress syndrome that may progress to respiratory failure. These patients also develop cytokine storm syndrome, and organ dysfunctions, which is a clinical picture that resembles sepsis. Considering that neutrophil extracellular traps (NETs) have been described as an important factors of tissue damage in sepsis, we investigated whether NETs would be produced in COVID-19 patients and participate in the lung tissue damage. A cohort of 32 hospitalized patients with a confirmed diagnosis of COVID-19 and respective healthy controls were enrolled. NETs concentration was assessed by MPO-DNA PicoGreen assay or by confocal immunofluorescence. The cytotoxic effect of SARS-CoV-2-induced NETs was analyzed in human epithelial lung cells (A549 cells). The concentration of NETs was augmented in plasma and tracheal aspirate from COVID-19 patients and their neutrophils spontaneously released higher levels of NETs. NETs were also found in the lung tissue specimens from autopsies of COVID-19 patients. Notably, viable SARS-CoV-2 can directly induce in vitro release of NETs by healthy neutrophils in a PAD-4-dependent manner. Finally, NETs released by SARS-CoV-2-activated neutrophils promote lung epithelial cell death in vitro. These results unravel a possible detrimental role of NETs in the pathophysiology of COVID-19. Therefore, the inhibition of NETs represent a potential therapeutic target for COVID-19.
RESUMO
The emergence of the coronavirus SARS-CoV-2 has raised a global issue and a pandemic disease outbreak, COVID-19, was declared by the World Health Organization on March 12th, 2020. The new virus is rapidly spreading in humans and cases of severe acute respiratory syndromes are being reported worldwide. Health authority advisors and governments from small towns to large countries need to quickly manage and deal with growing epidemiological data on a daily basis. In this work, current available data from reported cases and deaths over time were analyzed and treated. Lethality has been calculated by finding linearization of death cases against reported ones, using a time-delayed data transposition. A two-wave statistical model, 2WM, based on the superposition of normal distributions was used to fit current data and to estimate the evolution of infections and deaths, using Microsoft(R) Excel. The model showed good agreement even for apparent single wave behavior in some countries and can easily be extended to any number of waves. A gamma distribution was used as a risk function to estimate death probability from patient admission to reported death. Evolution of fatality cases over time can then be estimated from the model with reasonable accuracy. Data from South Korea, China, Australia, Germany, Italy and Spain were used to validate the model. Data from The United States, United Kingdom and Brazil were used to study the epidemiology as the pandemic progresses. Additionally, the 2WM was applied to world data and to the Brazilian state of Santa Catarina. The model was implemented in MS-Excel, a popular and easy to use analytical tool. A template spreadsheet is provided as supplementary material. Constant lethality can be determined from the initial stage of the pandemic wave. Values ranged from 1.7% to 15.3%, depending on the degree of possible sub notification cases. Even for places with low testing, a linear relationship can be found. The two-wave model can be fine-tuned to properly adjust the data. The second wave pattern was estimated according to the first wave parameter. The accuracy for estimating COVID-19 evolution was compared to the classic SIR model with good agreement. According to the model, based on current trends, health protocols and policies, approximately 10,000,000 cases and 860,000 deaths will be recorded worldwide. Approximately 99% of that number would be reached by the end of July 2020 given constant conditions.
