Background. Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic and infected millions of people. As the first country proclaimed the SARS-CoV-2 outbreak, China implemented travel ban measure, and curbed the epidemic quickly. We performed a phylogenetic analysis to reveal the spread dynamics detail of SARS-CoV-2 in China and the impact of travel ban on SARS-CoV-2. Method. Focusing on SARS-CoV-2 sequences collected from China in public database released as of March 31, 2020, we performed a Bayesian inference phylogenetic analyses to estimate the effective population size (Ne) curve of SARS-CoV-2 epidemic. Furthermore, we displayed the geographic spread mode of SARS-CoV-2 among different China regions by using Bayesian stochastic search variable selection (BSSVS) method. Results. The most recent common ancestor (tMRCA) of SARS-CoV-2 in China was traced back to December 9, 2019. According the Ne estimation and geographic spread reconstruction, January 25, 2020 was considered as the crucial time point during the SARS-CoV-2 epidemic in China,which was 2 days after the travel ban implemented. On the point, the tendency of viral population size changed from ascending to decreasing, and the cross-regional spread paths were blocked. Conclusions. Travel ban is an effective measure to intervene in the spread of SARS-CoV-2, It is necessary to continue efforts in research for prevention and control measures.
Cutaneous melanoma is quite often encountered in dermato-oncology. This paper describes a new genetic method to predict the prognostic outcome of melanoma.Data were collected from the TCGA databases. According to tumor progression status, the data were divided into two groups to evaluate the significant biological processes and key genes influencing the outcome of melanoma using a bioinformatics method. By adopting a statistical regression analysis method, a novel score based on the contributing genes was developed. Cox regression analysis was used to validate the effectiveness of the genetic risk score in predicting the outcome.Seven biological processes associated with melanocytes were identified. A protein-protein interactions network showed that 27 functional genes were associated with the outcome of melanoma. Among these, three genes (COL17A1, ITGA6, and SPRR2F) were used to calculate the genetic risk score, which was regarded as an independent and effective risk factor for disease progression or overall survival in melanoma.
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