Probabilistic survival modeling in health research: an assessment using cohort data from hospitalized patients with COVID-19 in a Latin American city.

Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic.

Genome sequencing and assessment of plant growth-promoting properties of a Serratia marcescens strain isolated from vermicompost.

BACKGROUND: Plant-bacteria associations have been extensively studied for their potential in increasing crop productivity in a sustainable manner. Serratia marcescens is a species of Enterobacteriaceae found in a wide range of environments, including soil. RESULTS: Here we describe the genome sequencing and assessment of plant growth-promoting abilities of S. marcescens UENF-22GI, a strain isolated from mature cattle manure vermicompost. In vitro, S. marcescens UENF-22GI is able to solubilize P and Zn, to produce indole compounds (likely IAA), to colonize hyphae and counter the growth of two phytopathogenic fungi. Inoculation of maize with this strain remarkably increased seedling growth and biomass under greenhouse conditions. The S. marcescens UENF-22GI genome has 5 Mb, assembled in 17 scaffolds comprising 4662 genes (4528 are protein-coding). No plasmids were identified. S. marcescens UENF-22GI is phylogenetically placed within a clade comprised almost exclusively of non-clinical strains. We identified genes and operons that are likely responsible for the interesting plant-growth promoting features that were experimentally described. The S. marcescens UENF-22GI genome harbors a horizontally-transferred genomic island involved in antibiotic production, antibiotic resistance, and anti-phage defense via a novel ADP-ribosyltransferase-like protein and possible modification of DNA by a deazapurine base, which likely contributes to its competitiveness against other bacteria. CONCLUSIONS: Collectively, our results suggest that S. marcescens UENF-22GI is a strong candidate to be used in the enrichment of substrates for plant growth promotion or as part of bioinoculants for agriculture.