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BackgroundA prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources.MethodsThe autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated.ResultsA total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)12 model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model.Discussion and ConclusionsThe gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China.

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Diabetes is considered as a risk for cognitive decline, which is characterized by neurodegenerative alteration and innate immunity activation. Recently, complement 3 (C3), the critical central component of complement system, has been reported to play a key role in neurodegenerative alterations under pathological condition. Receptor for advanced glycation end products (RAGE) activation is confirmed to mediate several inflammatory cytokines production. However, whether C3 activation participates in the diabetic neuropathology and whether this process is regulated by RAGE activation remains unknown. The present study aimed to investigate the role of C3 in streptozotocin‐induced diabetic mice and high glucose‐induced primary astrocytes and the underlying modulatory mechanisms. The decreased synaptophysin density and increased C3 deposition at synapses were observed in the diabetic brain compared to the control brain. Furthermore, the elevated C3 was co‐localized with GFAP‐positive astrocytes in the diabetic brain slice in vivo and high glucose‐induced astrocytes culture in vitro. Diabetes/high glucose‐induced up‐regulation of C3 expression at gene, protein and secretion levels, which were attenuated by pre‐treatment with RAGE, p38MAPK and NF‐κB inhibitors separately. These results demonstrate that high glucose induces C3 up‐regulation via RAGE‐ p38MAPK‐NF‐κB signalling in vivo and in vitro, which might be associated with synaptic protein loss.