RESUMO
Aim: The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) in estimating the severity and epidemic trend of COVID-19 in China. Subjects and methods: Epidemiological data on COVID-19 in China and Hubei Province were obtained from the National Health Commission of the People's Republic of China from January 20, 2020, to March 31, 2020. The number of daily new confirmed cases, daily confirmed deaths, daily recovered cases, the proportion of daily deaths and total deaths of discharged cases were collected, and the total discharge case fatality rate (tDCFR), daily discharge case fatality rate (dDCFR), and stage-discharge case fatality rate (sDCFR) were calculated. We used the R software (version 3.6.3, R core team) to apply a trimmed exact linear time method to search for changes in the mean and variance of dDCFR in order to estimate the pandemic phase from dDCFR. Results: The tDCFR of COVID-19 in China was 4.16% until March 31, 2020. According to the pattern of dDCFR, the pandemic was divided into four phases: the transmission phase (from January 20 to February 2), the epidemic phase (from February 3 to February 14), the decline phase (from February 15 to February 22), and the sporadic phase (from February 23 to March 31). The sDCFR for these four phases was 43.18% (CI 39.82-46.54%), 13.23% (CI 12.52-13.94%), 5.86% (CI 5.49-6.22%), and 1.61% (CI 1.50-1.72%), respectively. Conclusion: DCFR has great value in assessing the severity and epidemic trend of COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01895-4.
RESUMO
The molecular basis of the mechanism and the potential biomarkers of endometrial cancer (EC) remain to be studied. In the present study, we hypothesized that the comprehensive characterization of transcriptional changes in EC could help achieve this aim. By taking advantage of RNA-seq data from The Cancer Genome Atlas, we determined the profile of differently expressed genes (DEGs) between EC tumor tissues and normal samples. On this basis, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analyses. The interacting partners for each of the DEGs were explored and a protein-protein interaction network was constructed. Consequently, 10 hub genes were identified and their association with mortality in EC patients was investigated. The genes, AURKA, CENPA, and KIF2C, were found to be potential biomarkers for EC with a significant prognostic effect. Our work provided a basis for EC studies in both biological and clinical settings.