Background Subarachnoid hemorrhage (SAH) is an uncommon and serious subtype of stroke, which leads to the loss of the patient's ability to produce and live for many years. Objective To investigate the clinical effect of nimodipine in the treatment of SAH. Methods Electronic databases including China National Knowledge Infrastructure (CNKI), VIP, SinoMed, China Master's Theses Full-text Database (CMFD), China Doctoral Dissertations Full-text Database (CDFD), Cochrane Library, PubMed and Embase were searched from 2010 and 2021. All randomized controlled trials evaluating the efficacy of nimodipine in the treatment of SAH were included in our meta-analysis. The patients were divided into control group and treatment group. Meta-analysis was performed with Stata16.0 software. Results A total of 10 studies were included. Compared with the control group, the treatment group had higher effective rate (OR = 3.21, 95% CI: 2.25, 4.58; p < 0.001), and lower incidence of adverse reactions (OR = 0.35, 95% CI: 0.19, 0.67; p = 0.001). Before treatment, no significant differences were identified in middle cerebral artery blood flow velocity and Glasgow coma scale (GCS) score between the two groups. However, after treatment, the middle cerebral artery blood flow velocity (SMD = −1.36, 95% CI: −2.28, −0.49; p = 0.002) and GCS score (SMD = 1.24, 95% CI: 0.58, 1.89; p < 0.001) in the treatment group were significantly better than those in the control group. Conclusions Nimodipine is effective in the treatment of SAH, lowering incidence of adverse reactions and therefore improving the prognosis of patients.
The microbial fermentation process often involves various biological metabolic reactions and chemical processes. The mixed bacterial culture process of 2-keto-l-gulonic acid has strong nonlinear and time-varying characteristics. In this study, a probabilistic Bayesian deep learning approach is proposed to obtain a highly accurate and robust prediction of product formation. The Bayesian optimized deep neural network (BODNN) is utilized as basic model for prediction, the structural parameters of which are optimized. Then, the training datasets are classified into different categories according to the prior evaluation of prediction error. The final forecasting is a weighted combination of BODNN models based on the Bayesian hybrid method. The weights can be interpreted as Bayesian posterior probabilities and are computed recursively. The validation of 95 industrial batches is carried out, and the average root mean square errors are 1.51 and 2.01% for 4 and 8 h ahead prediction, respectively. The results illustrate that the proposed approach can capture the dynamics of fermentation batches and is suitable for online process monitoring.
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