RESUMEN
SARS-CoV-2 has caused a worldwide pandemic. Existing research on coronavirus mutations is based on small data sets, and multiple sequence alignment using a global-scale data set has yet to be conducted. Statistical analysis of integral mutations and global spread are necessary and could help improve primer design for nucleic acid diagnosis and vaccine development. Here, we optimized multiple sequence alignment using a conserved sequence search algorithm to align 24,768 sequences from the GISAID data set. A phylogenetic tree was constructed using the maximum likelihood (ML) method. Coronavirus subtypes were analyzed via t-SNE clustering. We performed haplotype network analysis and t-SNE clustering to analyze the coronavirus origin and spread. Overall, we identified 33 sense, 17 nonsense, 79 amino acid loss, and 4 amino acid insertion mutations in full-length open reading frames. Phylogenetic trees were successfully constructed and samples clustered into subtypes. The COVID-19 pandemic differed among countries and continents. Samples from the United States and western Europe were more diverse, and those from China and Asia mainly contained specific subtypes. Clades G/GH/GR are more likely to be the origin clades of SARS-CoV-2 compared with clades S/L/V. Conserved sequence searches can be used to segment long sequences, making large-scale multisequence alignment possible, facilitating more comprehensive gene mutation analysis. Mutation analysis of the SARS-CoV-2 can inform primer design for nucleic acid diagnosis to improve virus detection efficiency. In addition, research into the characteristics of viral spread and relationships among geographic regions can help formulate health policies and reduce the increase of imported cases.
RESUMEN
The major Corona Virus Disease 2019 (COVID-19) outbreak caused tens of thousands of diagnosed patients quarantined and treated in designated hospitals in Wuhan, the epicenter of the disease in China. Evidence for the psychological problems of COVID-19 patients was limited. Here we report a cross-sectional study of the mental distress and sleep quality of patients in a single center in Wuhan. The study was based on a combined questionnaire of basic questions designed by the study group, Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index (PSQI). On Feb 17th and Mar 14th, two groups of patients were recruited respectively in a designated hospital for COVID-19. Univariate analysis and regression models were used to identify predictors for patients' psychological distress and sleep quality. In total, there were 202 participants in our combined sample. The average SAS, SDS, and PSQI score of participants were 44.2, 51.7, and 9.3 respectively. Factors associated with SAS score include gender, subjective evaluation of disease symptoms, and evaluation of medical staffs' attitude. Gender, age, education level, frequency of contacting with family, subjective knowledge level of COVID 19, and evaluation of medical staffs' attitude are associated with participants SDS score. Factors associated with PSQI score are age and subjective evaluation of disease symptoms.