ABSTRACT
Objective@#To analyze the features of Norovirus outbreaks in schools and kindergartens and the associated factors in Chengdu, 2017, and to provide the scientific basis for Norovious outbreaks prevention and control.@*Methods@#A total of 75 schools and kindergartens in Chengdu of 2017 participated in the study about Norovirus outbreaks. ANOVA and binary Logistic regression models were used to analyze the influencing factors and duration of Norovirus outbreaks.@*Results@#Overall 710 cases were included. There was an average of 9.47 cases and 27.52 hours for each outbreak,decreasing by 1.06 cases and 10.56 hours compared with those of 2016. Most outbreaks happened in kindergartens and in the firstlevel economy regions from January to March, with GII type as the main outbreak. Compared with the first case of vomiting at home, vomiting in public area (OR=11.76, 95%CI=1.63-84.69) was much more serious, and compared with the active report of school/ community, being informed of the outbreak passively (OR=4.09，95%CI=1.04-16.03) was positively associated with outbreak severity.@*Conclusion@#To prevent Norovirus infection, specific development and training of dealing with vomiting and feces should be introduced, and measures to increase the ability to surveil and report Norovirus outbreaks should be enhanced.
ABSTRACT
To accurately analyze metabolites in industry-important photosynthetic microbes, LC-MS based metabolomics protocol needs to be optimized specifically for individual species. In this study, an LC-MS based metabolomics method was optimized for cyanobacterium Synechocystis sp. PCC 6803. With the optimized extraction, liquid chromatographic and mass spectral parameters, the method was capable of detecting 24 important metabolites related to central carbohydrate and energy metabolism in Synechocystis sp. PCC 6803. The study laid an important foundation for the metabolomics analysis of cyanobacteria.