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BackgroundRapid identification and differentiation of mosquito-transmitted flaviviruses in acute-phase sera of patients and field-caught vector mosquitoes are important for the prediction and prevention of large-scale epidemics.ResultsWe developed a flexible reverse-transcription loop-mediated isothermal amplification (RT-LAMP) unit for the detection and differentiation of dengue virus serotypes 1-4 (DENV1-4), Japanese encephalitis virus (JEV), and West Nile virus (WNV). The unit efficiently amplified the viral genomes specifically at wide ranges of viral template concentrations, and exhibited similar amplification curves as monitored by a real-time PCR engine. The detection limits of the RT-LAMP unit were 100-fold higher than that of RT-PCR in 5 of the six flaviviruses. The results on specificity indicated that the six viruses in the assay had no cross-reactions with each other. By examining 66 viral strains of DENV1-4 and JEV, the unit identified the viruses with 100% accuracy and did not cross-react with influenza viruses and hantaviruses. By screening a panel of specimens containing sera of 168 patients and 279 pools of field-caught blood sucked mosquitoes, results showed that this unit is high feasible in clinical settings and epidemiologic field, and it obtained results 100% correlated with real-time RT-PCR.ConclusionsThe RT-LAMP unit developed in this study is able to quickly detect and accurately differentiate the six kinds of flaviviruses, which makes it extremely feasible for screening these viruses in acute-phase sera of the patients and in vector mosquitoes without the need of high-precision instruments.

In Ningbo, many women with cervical cytologic abnormalities have HPV infection. Vaccines targeting HPV52 and HPV58 in conjunction with HPV16 and HPV18 are required for the prevention and treatment of cervical lesions in Chinese women.

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This study aimed to identify circulating influenza virus strains and vulnerable population groups and investigate the distribution and seasonality of influenza viruses in Ningbo, China. Then, an autoregressive integrated moving average (ARIMA) model for prediction was established. Influenza surveillance data for 2006–2014 were obtained for cases of influenza-like illness (ILI) (n = 129,528) from the municipal Centers for Disease Control and virus surveillance systems of Ningbo, China. The ARIMA model was proposed to predict the expected morbidity cases from January 2015 to December 2015. Of the 13,294 specimens, influenza virus was detected in 1148 (8.64%) samples, including 951 (82.84%) influenza type A and 197 (17.16%) influenza type B viruses; the influenza virus isolation rate was strongly correlated with the rate of ILI during the overall study period (r = 0.20, p < 0.05). The ARIMA (1, 1, 1) (1, 1, 0)12 model could be used to predict the ILI incidence in Ningbo. The seasonal pattern of influenza activity in Ningbo tended to peak during the rainy season and winter. Given those results, the model we established could effectively predict the trend of influenza-related morbidity, providing a methodological basis for future influenza monitoring and control strategies in the study area.

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