Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Abstract: Objective　To investigate the antimicrobial activity of omadacycline (OMC) against clinical Streptococcus agalactiae (GBS) isolates, as well as its relationship with biofilm formation, resistance genes and virulence genes. Methods　A total of 136 strains of Streptococcus agalactiae isolated from Shenzhen Nanshan People's Hospital between 2015 to 2020. The minimum inhibitory concentration (MIC) of OMC against Streptococcus agalactiae was determined by broth microdilution. Crystal violet staining was used to detect the biofilm formation ability of GBS. Resistance genes (tetM, tetO, tetK, ermB, OptrA) and virulence genes (cpsⅢ, bca, fbsA, cpsA, scpB) were investigated by polymerase chain reaction (PCR). Results　Among the 136 clinical isolates of GBS, 20 strains (14.7%) were resistant to OMC, 64 (47.1%) were intermediate, and 52 (38.2%) were sensitive. Fifty-seven strains (41.9%) were biofilm-positive, 20 of which (35.1%) were sensitive to OMC. Seventy-nine strains (58.1%) were biofilm-negative, 32 of which (40.5%) were susceptible to OMC. There was a statistically significant difference in the sensitivity rates between the two groups of strains (χ2=63.062, P<0.001), but there was no significant difference in the sensitivity of OMC among the biofilm-positive strains (Fisher's exact test, P=0.824). The resistance rates of tetM, tetO, ermB and OptrA positive strains were higher than those of negative strains, while tetK was opposite. The presence of tetM (Z=0.815, P=0.415), tetO (Z=0.151, P=0.88), tetK (Z=0.567, P=0.571), ermB (Z=1.198, P=0.231) resistance genes in Streptococcus agalactiae had no significant impact on the sensitivity of OMC. However, the presence of the OptrA resistance gene showed a statistically significant effect on the sensitivity of OMC (Z=2.913, P=0.004). The virulence factors cpsⅢ, bca, fbsA, cpsA and scpB were all detected at a rate higher than 50%. The presence of the virulence genes cpsⅢ (Z=0.222, P=0.824), bca (Z=0.141, P=0.888), fbsA (Z=0.813, P=0.416), and cpsA (Z=1.615, P=0.106) in Streptococcus agalactiae had no significant impact on the sensitivity of OMC. However, there was a significant inter-group difference in the scpB virulence gene (Z=2.844, P=0.004), but the rank mean values and resistance rates of scpB-positive strains were lower than those of the negative strains. Conclusions　The formation of biofilm in Streptococcus agalactiae reduces its sensitivity to OMC, but there was no significant difference in the sensitivity to OMC among the biofilm-positive strains. The presence of resistance genes tetM, tetO, tetK, ermB, and virulence genes cpsⅢ, bca, fbsA, cpsA, scpB in Streptococcus agalactiae is not associated with OMC resistance, but the presence of the resistance gene OptrA is correlated with OMC resistance..

Objective To evaluate the diagnostic value of color Doppler ultrasonography(CDUS)and molybdenum mammography for breast tumor.Methods 86 breast diseases which were diagnosed by the surgery and pathology,were prospectively analysed,40 in mammary carcinoma,and 46 in mammary benign disease.All the patients were examined with CDUS,CDFI and molybdenum mammography.Diagnosis was made in every case compared using two methods(CDUS and molybdenum mammagraphy)with any using only one method.Results There is significant difference between mammary carcinoma and mammary benign disease in CDUS.The mammary carcinoma patients'blood stream signal is abundant. The diagnostic veracity of using both CDUS and molybdenum mammography for breast tumor is obvious higher than that of the any only one method.Conclusion Combined CDUS with molybdenum mammography which is the optimal methods of the mammary image examination,can improve the diagnostic veracity of breast tumor.