BackgroundAfter the 2009 influenza A(H1N1)pdm09 pandemic, China established its first severe acute respiratory infections (SARI) sentinel surveillance system.MethodsWe analyzed data from SARI cases in 10 hospitals in 10 provinces in China from February 2011 to October 2013.ResultsAmong 5,644 SARI cases, 330 (6%) were influenza-positive. Among these, 62% were influenza A and 38% were influenza B. Compared with influenza-negative cases, influenza-positive SARI cases had a higher median age (20.0 years vs.11.0, p = 0.003) and were more likely to have at least one underlying chronic medical condition (age adjusted percent: 28% vs. 25%, p < 0.001). The types/subtypes of dominant strains identified by SARI surveillance was almost always among dominant strains identified by the influenza like illness (ILI) surveillance system and influenza activity in both systems peaked at the same time.ConclusionsData from China’s first SARI sentinel surveillance system suggest that types/subtypes of circulating influenza strains and epidemic trends among SARI cases were similar to those among ILI cases.

Objective: To investigate the clinical value of ADNEX model in early diagnosis and staging of benign and malignant ovarian tumors. Method: 136 cases of ovarian cancer patients treated in our hospital were retrospectively analyzed using the ADNEX risk model and MRI data. The accuracy of the two diagnostic methods was compared with the results of pathological examination as gold standard. Results: For qualitative assessment, the accuracy and sensitivity of the ADNEX model were 78.70% and 93%, while the accuracy and sensitivity of MRI examination were 80.1%, and 90.7%, respectively. The diagnostic values of the two methods were not statistically different (P > 0.05). For ovarian tumor staging, the ADNEX model was significantly less accurate and specific for staging borderline tumor than MRI examination, although it had significantly higher sensitivity (P < 0.05). For tumors at other stages, there were no diagnostic differences between the methods (P > 0.05). Conclusion: ADNEX risk model has certain diagnostic and predictive value to distinguish benign from malignant ovarian tumors. It is useful to detect and exclude ovarian tumor. However, for early diagnosis, it is not accurate enough and further study is needed to validate this usefulness.