Silica dust mainly attacks alveolar macrophages (AMs) and increases the apoptosis of AMs in silicosis patients. However, it is still unclear whether autophagy is affected. Autophagy mainly has defensive functions in response to stress, contributing to cell survival in adverse conditions, and conversely it has also been implicated in cell death. Lipopolysaccharide (LPS) induces autophagy and apoptosis in macrophages. The role of LPS in autophagy and apoptosis in AMs of silicosis patients is unknown. In this study, we collected AMs from 53 male workers exposed to silica and divided them into an observer (control) group, and stage I, II and III patient groups. We found increased levels of LC3B, SQSTM1/p62 and BECN1，whereas the phosphorylation of MTOR，and levels of LAMP2, TLR4, MYD88, TICAM1, as well as the number of lysosomes decreased with the development of silicosis. LPS stimulation triggered autophagy and increased levels of SQSTM1 in AMs. The autophagy inhibitor, 3-methyladenine (3MA), inhibited LPS-induced apoptosis in the AMs of silicosis patients. Moreover, 3MA reversed the LPS-induced decrease in BCL2 and the increase in BAX and CASP3 levels in AMs. These results suggest that autophagosomes accumulate in AMs during silicosis progression. LPS can induce the formation of autophagosomes through a TLR4-dependent pathway, and LPS may exacerbate the apoptosis in AMs. Blockade of the formation of autophagosomes may inhibit LPS-induced apoptosis via the intrinsic apoptotic pathway in AMs. These findings describe novel mechanisms that may lead to new preventive and therapeutic strategies for pulmonary fibrosis.

BackgroundIt is a daunting task to discontinue pertussis completely in China owing to its growing increase in the incidence. While basic to any formulation of prevention and control measures is early response for future epidemic trends. Discrete wavelet transform(DWT) has been emerged as a powerful tool in decomposing time series into different constituents, which facilitates better improvement in prediction accuracy. Thus we aim to integrate modeling approaches as a decision-making supportive tool for formulating health resources.MethodsWe constructed a novel hybrid method based on the pertussis morbidity cases from January 2004 to May 2018 in China, where the approximations and details decomposed by DWT were forecasted by a seasonal autoregressive integrated moving average (SARIMA) and nonlinear autoregressive network (NAR), respectively. Then, the obtained values were aggregated as the final results predicted by the combined model. Finally, the performance was compared with the SARIMA, NAR and traditional SARIMA-NAR techniques.ResultsThe hybrid technique at level 2 of db2 wavelet including a SARIMA(0,1,3)(1,0,0)12modelfor the approximation-forecasting and NAR model with 12 hidden units and 4 delays for the detail d1-forecasting, along with another NAR model with 11 hidden units and 5 delays for the detail d2-forecasting notably outperformed other wavelets, SARIMA, NAR and traditional SARIMA-NAR techniques in terms of the mean square error, root mean square error, mean absolute error and mean absolute percentage error. Descriptive statistics exhibited that a substantial rise was observed in the notifications from 2013 to 2018, and there was an apparent seasonality with summer peak. Moreover, the trend was projected to continue upwards in the near future.ConclusionsThis hybrid approach has an outstanding ability to improve the prediction accuracy relative to the others, which can be of great help in the prevention of pertussis. Besides, under current trend of pertussis morbidity, it is required to urgently address strategically within the proper policy adopted.

We examined the effect and relative contributions of different types of stress on the risk of hypertension. Using cluster sampling, 5,976 community-dwelling individuals aged 40–60 were selected. Hypertension was defined according to the Seventh Report of the Joint National Committee, and general psychological stress was defined as experiencing stress at work or home. Information on known risk factors of hypertension (e.g., physical activity levels, food intake, smoking behavior) was collected from participants. Logistic regression analysis was used to determine the associations between psychological stress and hypertension, calculating population-attributable risks and 95% confidence intervals (CIs). General stress was significantly related to hypertension (odds ratio [OR] = 1.247, 95% CI [1.076, 1.446]). Additionally, after adjustment for all other risk factors, women showed a greater risk of hypertension if they had either stress at work or at home: OR = 1.285, 95% CI (1.027, 1.609) and OR = 1.231, 95% CI (1.001, 1.514), respectively. However, this increased risk for hypertension by stress was not found in men. General stress contributed approximately 9.1% (95% CI [3.1, 15.0]) to the risk for hypertension. Thus, psychological stress was associated with an increased risk for hypertension, although this increased risk was not consistent across gender.

The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.

With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.