BackgroundTo assess the efficacy and safety of oral Guanxinshutong (GXST) capsules in Chinese patients with stable angina pectoris (SAP) in a prospective, multicenter, double-Blind, placebo-controlled, randomized clinical trial (clinicaltrials.gov Identifier: NCT02280850).MethodsEligible patients were randomized 1:1 to the GXST or placebo group. Current standard antianginal treatment except for nitrate drugs was continued in both groups, who received an additional 4-week treatment of GXST capsule or placebo. Primary endpoint was the change from baseline in angina attack frequency after the 4-week treatment. Secondary endpoints included the reduction of nitroglycerin dose, score of Seatntle Agina Questionnaire, exercise tolerance test defined as time to onset of chest pain and ST-segment depression at least 1 mm greater than the resting one.ResultsA total of 300 SAP patients from 12 centers in China were enrolled between January 2013 and October 2015, and they were randomly divided into the GXST group and the placebo group (150 patients in each group). Of whom, 287 patients completed the study (143 patients in the GXST group, 144 patients in the placebo group). The baseline characteristics of the two groups were comparable. After 4-week treatment with GXST capsules, the number of angina attacks and the consumption of short-acting nitrates were significantly reduced. In addition, the quality of life of patients were also substantially improved in the GXST group. No significant differences in the time of onset of angina and 1-mm ST segment depression were noted between the two groups. 7 patients (4.1%) in the GXST group and 3 patients (2.1%) in the placebo group reported at least one adverse event, respectively.ConclusionsGXST capsules are beneficial for the treatment of SAP patients.
Modern cloud computing systems contain hundreds to thousands of computing and storage servers. Such a scale, combined with ever-growing system complexity, is causing a key challenge to failure and resource management for dependable cloud computing. Autonomic failure detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect failures, we need to monitor the cloud execution and collect runtime performance data. These data are usually unlabeled, and thus a prior failure history is not always available in production clouds. In this paper, we present a self-evolving anomaly detection (SEAD) framework for cloud dependability assurance. Our framework self-evolves by recursively exploring newly verified anomaly records and continuously updating the anomaly detector online. As an distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies and their decisions are leveraged to update the detector. Thus, the detector evolves following the upgrade of system hardware, update of software stack, and change of user workloads. Moreover, we design two types of detectors, one for general anomaly detection and the other for type-specific anomaly detection. With the help of self-evolving technique, our detectors can achieve 88.94% in sensitivity and 94.60% in specificity on average, which makes them suitable for real-world deployment.
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