Background:Ankylosing spondylitis (AS) is a chronic immune-mediated disease affecting the sacroiliac joints and the spine, manifesting with new bone formation and osteopenia. Five tumor necrosis factor-alpha (TNF-α) inhibitors (infliximab, etanercept, adalimumab, certolizumab, and golimumab) are available for the treatment of AS, however, the results for the safety of TNF-α inhibitors in the treatment of AS are not consistent.Methods:In this study, we conducted a meta-analysis to determine the safety of TNF-α inhibitors compared with placebo in reducing pain, swelling, and inflammation of AS patients. Eight relevant articles including 2049 patients were included for this meta-analysis study. We observed that the incidence of adverse events (RR = 1.22, 95% CI: 1.12–1.33; P = .501, I2 = 0%) and injection-site reaction (RR = 2.93, 95% CI: 2.02–4.23; P = .691, I2 = 0%) in AS patients’ treatment with TNF-α inhibitors was significantly higher than that with placebo.Results:However, there was no significant difference in the incidence of serious adverse event, infection, serious infection, and discontinuations due to adverse event. TNF-α inhibitors may be a promising treatment for AS, but carries an increased incidence rate of adverse events and injection-site reaction.Conclusion:Due to the existence of the unstable factors, further studies need to be done to verify the result of this study.
Forecasting the short-term metro ridership is an important issue for operation management of metro systems. However, it cannot be solved well by the single long short-term memory (LSTM) neural network alone for the irregular fluctuation caused by various factors. This paper proposes a hybrid algorithm (STL-LSTM) which combines the addition mode of Seasonal-Trend decomposition based on Loess (STL) and the LSTM neural network to mitigate the influences of irregular fluctuation and improve the performance of short-term metro ridership prediction. First, the original series is decomposed into three sub-series by the addition mode of STL. Then, the LSTM neural network is employed to predict each decomposed series. Finally, all the predicted outputs are merged as the overall output. The results show that the STL-LSTM model can achieve higher accuracy than the single LSTM model, support vector regression (SVR), and the EMD-LSTM model which combines the empirical mode decomposition and the LSTM neural network. INDEX TERMS Short-term metro ridership prediction, seasonal-trend decomposition based on loess (STL), long short-term memory (LSTM) neural network.
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