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a b s t r a c tBackground: Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends. Methods: Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. Findings: ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014-2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%). Interpretation: Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

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Two strains of porcine reproductive and respiratory syndrome virus (PRRSV) were isolated, designated GDQJ and GDBY1. Experimental inoculation showed that GDBY1, caused 100% morbidity and 67% mortality, while GDQJ, caused 100% morbidity but no death. Full-length genomes were sequenced. Homologic and phylogenetic analyses indicated that these two strains were closely related to Chinese highly pathogenic PRRSV strains. Surprisingly, identical 30 amino acids (aa) deletion in the NSP2-coding region, a presumed high virulence marker, was present in low virulent strain GDQJ. Further comprehensive analysis of GDQJ genome in comparison with Chinese highly pathogenic PRRSV strains revealed multiple genomic variations, distributing in 5' UTR, NSP1b, NSP2, NSP3, NSP5, NSP7, NSP9, NSP10, GP5, and N regions. Data present in this article confirm that the 30 aa deletion in the NSP2-coding region alone is not a reliable genomic indicator for the high virulence of PRRSV strains emerged in China. The genomic variations of GDQJ strain provided the basis for further studies of virulence determinants for PRRSVs.

The retinol-binding protein 4 (RBP4) has been postulated to play a role in glucose homeostasis, insulin resistance, and diabetes mellitus in human and animal studies. The aim of the present study was to evaluate the role of RBP4 in Chinese patients with type 2 diabetes mellitus with and without diabetic retinopathy (DR). Plasma RBP4 concentrations were tested in 287 patients with type 2 diabetes. At baseline, demographic and clinical information including presence of DR and vision-threatening DR (VTDR) was collected. The relationship between RBP4 and DR (VTDR) was investigated using logistic regression. Patients with DR or VTDR had significantly higher plasma levels of RBP4 on admission (P<0.0001). Receiver operating characteristics (ROCs) to predict DR and VDTR demonstrated areas under the curve for RBP4 of 0.79 (95% confidence interval (CI): 0.73–0.85) and 0.90 (95% CI: 0.85–0.94), respectively, which were superior to other factors. For each 1 μg/ml increase in plasma level of RBP4, the unadjusted and adjusted risk of DR would be increased by 8% (with the odds ratio (OR) of 1.08 (95% CI: 1.05–1.13), P<0.001) and 5% (1.05 (1.02–1.11), P=0.001), respectively. It was 12% (with the OR of 1.12 (95% CI: 1.07–1.18), P<0.001) and 9% (1.09 (1.05–1.15), P<0.001) for VTDR. The present study shows that elevated plasma levels of RBP4 were associated with DR and VDTR in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of DR complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with DR.