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BackgroundTianshu capsule (TSC), a formula of traditional Chinese medicine, has been widely used in clinical practice for prophylactic treatment of headaches in China. However, former clinical trials of TSC were small, and lack of a standard set of diagnostic criteria to enroll patients. The study was conducted to re-evaluate the efficacy and safety of TSC post-marketing in an extending number of migraineurs who have diagnosed migraine with the International Classification of Headache Disorders, 3rd edition (beta version, ICHD-3β).MethodsThe study was a double-blind, randomized, placebo-controlled clinical trial that conducted at 20 clinical centers in China. At enrollment, patients between 18 and 65 years of age diagnosed with migraine were assigned to receive either TSC (4.08 g, three times daily) or a matched placebo according to a randomization protocol. The primary endpoint was a relative reduction of 50% or more in the frequency of headache attacks. The secondary outcomes included a reduction in the incidence of headache, the visual analogue scale of headache attacks, days of acute analgesic usage, and percentage of patients with a decrease of 50% or more in headache severity. Accompanying symptoms were also assessed.ResultsOne thousand migraine patients were initially enrolled in the study, and 919 of them completed the trial. Following the 12-week treatment, significant improvement was observed in the TSC group concerning both primary and secondary outcomes. After therapy discontinuation, the gap between the TSC group and the placebo group in efficacy outcomes continued to increase. There were no severe adverse effects.ConclusionsTSC is an effective, well-tolerated medicine for prophylactic treatment of migraine, and still have prophylactic effect after medicine discontinuation.Trial registrationClinicalTrials.gov Identifier: NCT02035111; Data of registration: 2014-01-10.

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Thickness of tectonically deformed coal (TDC) has positive correlations with the susceptible gas outbursts in coal mines. To predict the TDC thickness of the coalbed, we proposed a prediction method using seismic attributes based on the deep belief network (DBN) and dimensionality reduction. Firstly, we built a DBN prediction model using the extracted attributes from a synthetic seismic section. Next, we transformed the possibly correlated seismic attributes into principal components through principal components analysis. Then, we compared the true TDC thickness with the predicted TDC thicknesses to evaluate the prediction accuracy of different models, i.e., a DBN model, a support vector machine model, and an extreme learning machine model. Finally, we used the DBN model to predict the TDC thickness of coalbed No. 8 in an operational coal mine based on synthetic experiments. Our studies showed that the predicted distribution of TDC thickness followed the regional characteristics of TDC development well and was positively correlated with the burial depth, coalbed thickness, and tectonic development. In summary, the proposed DBN model provided a reliable method for predicting TDC thickness and reducing gas outbursts in coal mine operations.

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