Objective To investigate the distribution of avian influenza A viruses in external environment in Urumqi City in order to provide reference for the prevention and control of human infection with avian influenza virus in Urumqi City.Methods Environmental specimens were collected from 2013 to 2015 to detect avian influenza A virus nucleic acid with real-time fluorescent PCR.The positive specimens were further analyzed to identify the subtypes of avian influenza A viruses.Results A total of 1 043 environmental specimens were collected and tested.Among them,123 specimens were positive for avian influenza A virus nucleic acid with an overall positive rate of 11.79%.H9 was the predominant subtype,accounting for 7.77% of all specimens.Quarterly detection rates of avian influenza A virus in the three years peaked in different quarters of the year.Of all surveillance sites,live poultry markets in urban and rural areas had the highest positive rate (14.23%).Specimens collected from poultry drinking water (18.44%) and other specimens (19.44%) were highly positive for avian influenza A virus.Conclusion Avian influenza A viruses,especially the subtypes of H5,H7 and H9,exist in live poultry markets in Urumqi City.It is necessary to conduct surveillance and health education among people exposed to poultry in live poultry markets.

Teaching medical courses in French are the characteristics of teaching programs in medicine school of Shanghai JiaoTong uuiversity.It may help develop the course content and promote the international academic communication.Teaching pathology course in French for Chinese students is also an important task of pathology teaching program.The paper introduced and explored the language use of French,regional variations between Chinese and French pathological major and the cooperation with foreign professors in teaching.It is been testified that teaching in French can contribute to the medical education and arouse student's learning initiative.

Objective To investigate the efficacy of using Xinjiang wild Artemisia rupestris L. crude polysaccharides ( WARCP) as an immunologic adjuvant for influenza virus vaccine( IVV) .Methods ICR mice were subcutaneously immunized with 0.3 μg of IVV and 1.5 μg of IVV alone or co-administered with 200 μg of WARCP on 0 d and 14 d.Antibody levels in serum samples were detected by using indirect ELISA.MTT method was used to measure the proliferation of splenocytes.The growth conditions of mice were observed as well.Results No significant differences in the body weight were observed between mice from different groups (P>0.05).The levels of influenza virus-specific IgG, IgG1 and IgG2a were signifi-cantly increased in mice injected with WARCP adjuvant (P<0.05).The levels of IgG antibody in mice im-munized with low-dose of IVV and WARCP were significantly higher than those in mice immunized with high-dose of IVV alone (P<0.05), indicating at least 80% reduction in vaccine dosage by adding WARCP as adjuvant.Moreover, WARCP significantly promoted the proliferation of lymphocytes (P<0.05).Conclu-sion Adding WARCP to IVV enhanced the efficacy of IVV by boosting humoral and cellular immunity re-sponses with the advantages of high safety and dose-sparing.This study suggested the possibility of using WARCP as a novel immunologic adjuvant for influenza virus vaccine.

Objective To analyze the influence of meteorological factors on the number of influenza-like illness (ILI) cases in Urumqi, Xinjiang, and establish an ARIMAX (autoregressive integrated moving average model-X) model to make short-term prediction of the number of ILI cases, so as to provide theoretical basis for the prevention and control of influenza in Urumqi. Methods The number of ILI cases in Urumqi from January 2015 to September 2017 and meteorological data of the same period were used to establish ARIMAX model and predict the number of ILI cases in Urumqi from October 2017 to March 2018. Results The ARIMA (0,1,1) (1,1,0)₁₂ model was established from January 2015 to September 2017, AIC = 200.09. According to residual correlation function (CCF), there was a positive correlation between monthly average relative humidity and ILI cases, and a negative correlation between monthly sunshine hours and ILI cases. The average monthly relative humidity and monthly sunshine hours were taken as influencing variables to establish the ARIMAX model. Among them, the ARIMAX model incorporating the lagging order of 0 of monthly sunshine hours had the smallest AIC (AIC=197.63), and all parameters of the model were statistically significant. Compared with the univariate time series ARIMA model, the mean absolute percentage error (MAPE) of fitting was reduced by 1.3687%, the predicted MAPE was reduced by 5.25%, and the prediction accuracy was improved. Conclusion The ARIMAX model with meteorological factors established in this study can better predict the incidence trend of ILI cases in a short time, providing evidence for influenza surveillance and prevention and control.