Objective Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China. Methods We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect. Results During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1) 12 and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1) 12 model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1) 12 model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model. Conclusion Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.

BackgroundAccumulating literature has shown the predictive values of inflammation and nutrition-based biomarkers in the prognosis of oesophageal cancer but with inconsistent findings.MethodWe performed a meta-analysis to systematically evaluate the predictive value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), C reactive protein-to-albumin ratio (CAR), systemic inflammation index (SII), prognostic nutritional index (PNI), Glasgow Prognostic Score (GPS) and modified Glasgow Prognostic Score (mGPS) in oesophageal cancer. The outcome indicators include the overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS). We applied pooled HR, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the curve together with 95% CI to estimate the predictive accuracy.ResultsA total of 72 studies, including 22 260 patients, were included in the meta-analysis. Elevated NLR, PLR CAR, SII, GPS, mGPS and decreased LMR and PNI were associated with poor OS of oesophageal cancer. A high level of NLR, PLR and GPS was related to poor DFS. A high level of NLR and GPS was related to poor CSS. The summarised AUC of CAR (0.72, 95% CI: 0.68 to 0.75) and mGPS (0.75, 95% CI: 0.71 to 0.78) surpassed any other indicators.ConclusionsClinical indicators such as NLR, PLR, LMR, PNI, SII, CAR, GPS and mGPS have the moderate predictive ability in OS, DFS and CSS of oesophageal cancer. The pretreatment level of CAR and mGPS showed an outstanding prediction value in 5-year OS for oesophageal cancer.

Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1) 12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1) 12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

BackgroundAs a new anti-diabetic medicine, Liraglutide (LIRA), one of GLP-1 analogues, has been found to have an anti-atherosclerotic effect. Since vascular smooth muscle cells (VSMCs) play pivotal roles in the occurrence of diabetic atherosclerosis, it is important to investigate the role of LIRA in reducing the harmful effects of high-glucose (HG) treatment in cultured VSMCs, and identifying associated molecular mechanisms.MethodsPrimary rat VSMCs were exposed to low or high glucose-containing medium with or without LIRA. They were challenged with HG in the presence of phosphatidylinositol 3-kinase (PI3K), extracellular signal-regulated kinase (ERK)1/2, or glucagon-like peptide receptor (GLP-1R) inhibitors. Cell proliferation and viability was evaluated using a Cell Counting Kit-8. Cell migration was determined by Transwell migration and scratch wound assays. Flow cytometry and Western blotting were used to determine apoptosis and protein expression, respectively.ResultsUnder the HG treatment, VSMCs exhibited increased migration, proliferation, and phosphorylation of protein kinase B (Akt) and ERK1/2, along with reduced apoptosis (all p < 0.01 vs. control). These effects were significantly attenuated with LIRA co-treatment (all p < 0.05 vs. HG alone). Inhibition of PI3K kinase and ERK1/2 similarly attenuated the HG-induced effects (all p < 0.01 vs. HG alone). GLP-1R inhibitors effectively reversed the beneficial effects of LIRA on HG treatment (all p < 0.05).ConclusionsHG treatment may induce abnormal phenotypes in VSMCs via PI3K and ERK1/2 signaling pathways activated by GLP-1R, and LIRA may protect cells from HG damage by acting on these same pathways.

To explore the long-term effect of exposure to ambient air pollution on the risk of active tuberculosis (TB). Methods: We constructed a distributed-lag nonlinear model (DLNM) to evaluate the relatively long-term influence of outdoor PM 2.5 , PM 10 , SO 2 and NO 2 exposure on active TB risk in the city of Lianyungang in Jiangsu Province, China. Results: There were 7,282 TB cases reported in the study area during 2014-2017, with annual median (interquartile range) concentrations of PM 2.5 , PM 10 , SO 2 and NO 2 at 45.86 (34.57-64.14) mg/m 3 , 85.43 (62.86-116.14) mg/m 3 , 22.00 (15.71-30.86) mg/m 3 and 30.00 (23.29-38.57) mg/m 3 , respectively. The single-pollutant model showed that for each 10 mg/m 3 increase in concentration, the cumulative relative risk of TB was 1.12 (lag 0-24 weeks, 95% CI: 1.03-1.22) for PM 2.5 with reference to 35 mg/m 3 ; 1.11 (lag 0-21 weeks, 95% CI: 1.06-1.17) for PM 10 with reference to 70 mg/m 3 ; 1.37 (lag 0-20 weeks, 95% CI: 1.16-1.62) for SO 2 with reference to 60 mg/m 3 ; and 1.29 (lag 0-22 weeks, 95% CI: 1.11-1.49) for NO 2 with reference to 40 mg/m 3. In the multipollutant model considering both PM 10 and NO 2 , the association remained significant. Conclusions: Our results revealed a potential association between outdoor exposure to PM 2.5 , PM 10 , SO 2 , and NO 2 and active TB. Considering that people from developing countries continue to be exposed to both severe outdoor air pollution and high rates of latent TB infection, the association between worsening air pollution and active TB deserves further attention.