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find Keyword "Pattern" 3 results
  • Huo Xiang Zhengqi Dropping Pill in Treating Wind Cold and Dampness Stagnation Pattern of Common Cold: A Randomized Controlled Trial

    Objective To evaluate the efficacy and safety of Huo Xiang Zhengqi dropping pill in treating wind cold and dampness stagnation pattern of common cold. Methods A multicenter, randomlyized, double blind, double dummy, controlled trial was conducted. A total of 480 patients with common cold were randomly divided into two groups: a trial group (360 patients) were treated with Huo Xiang Zhengqi Dropping Pill and Huo Xiang Summer-heat Eliminating Soft Capsule analogue, while a control group (120 patients) were treated with Huo Xiang Summer-heat Eliminating Soft Capsule and Huo Xiang Zhengqi Dropping Pill analogue. The therapeutic course of both groups was 3 days. Results The therapeutic effectiveness of diarrhea as the main symptom: the marked effective rate and total effective rate of the trial group were 86.1% and 96.1%, respectively, while those of the control group were 69.2% and 84.6%, respectively; the therapeutic effectiveness of traditional Chinese medicine (TCM) pattern: the marked effective rate and total effective rate of the trial group were 87.5% and 98.5%, respectively, while those of the control group were 69.2% and 91.5%, respectively. There were significant differences between the two groups in terms of the above two indicators (Plt;0.05), which indicated Huo Xiang Zhengqi Dropping Pill was superior to Huo Xiang Summer-heat Eliminating Soft Capsule in treating wind cold and dampness stagnation pattern of common cold. No adverse effects were found in the trial group. Conclusion Huo Xiang Zhengqi Dropping Pill is effective and safe in treating wind cold and dampness stagnation pattern of common cold.

    Release date:2016-09-07 10:58 Export PDF Favorites Scan
  • Study of the Patterns and Predictors of Recurrent Stroke among Patients with Initial Ischemic Stroke

    ObjectiveTo analyze the patterns of recurrent strokes among patients with initial ischemic stroke and investigate the possible predictors of recurrent ischemic stroke. MethodPatients with recurrent strokes after initial ischemic strokes hospitalized in our hospital between January 2008 and December 2012 were included in the study, and the data of general demographic information, life history, past medical history and laboratory test results were all retrospectively analyzed. The patterns of recurrent strokes in patients with initial ischemic stroke were analyzed, and multivariate logistic regression analysis was used to investigate the independent risk factors for recurrent ischemic stroke. ResultsA total of 237 patients were included in this study, including 198 patients with recurrent ischemic stroke and 39 patients with recurrent hemorrhagic stroke. Among patients with recurrent ischemic stroke, there were 137 patients with anterior circulation stroke, 52 with posterior circulation stroke and 9 with multiple infarction. Multivariate logistic regression analysis showed that older age at initial stroke onset[OR=1.968, 95%CI (1.533, 2.152), P=0.009], frequent mood swings[OR=1.345, 95%CI (1.121, 1.783), P=0.011], hyperlipidemia[OR=1.436, 95%CI (1.216, 1.732), P=0.018] and atrial fibrillation[OR=3.417, 95%CI (2.927, 4.897), P=0.005] were independent risk factors for recurrent ischemic stroke. ConclusionsIschemic stroke is the most common pattern of recurrent strokes; and aging, frequent mood swings, hyperlipidemia and atrial fibrillation are possible predictors of recurrent ischemic stroke after the initial ischemic stroke.

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  • A review of researches on decoding algorithms of steady-state visual evoked potentials

    Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories—trained and non-trained—based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.

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