目的评价沙利度胺及其类似物在炎症性肠病(IBD)治疗中的有效性以及安全性。方法检索PubMed、Cochrane Center Register of Controlled Trails、Embase、Web of Science、中国期刊全文数据库、万方数据库、维普数据库,系统性评价沙利度...目的评价沙利度胺及其类似物在炎症性肠病(IBD)治疗中的有效性以及安全性。方法检索PubMed、Cochrane Center Register of Controlled Trails、Embase、Web of Science、中国期刊全文数据库、万方数据库、维普数据库,系统性评价沙利度胺及其类似物治疗IBD的有效性及安全性,就诱导缓解率、维持缓解率、不良反应事件进行总结分析。结果共纳入16篇文献(3篇RCT,13篇病例系列研究),333例IBD患者。2篇RCT显示在克罗恩病(Crohn’s disease,CD)和溃疡性结肠炎(ulcerative colitis,UC)患者中,沙利度胺均能够显著提高诱导缓解率(46.4%vs 11.5%,P=0.010;83.3%vs 18.8%,P=0.005)。病例系列研究显示沙利度胺能够诱导CD缓解,合并缓解率为50%(95%CI=36%~65%)。沙利度胺不良反应中神经系统不良反应最为常见(207/316,65.5%),周围神经病变是患者停药的最常见原因。结论沙利度胺治疗IBD疗效理想,在CD诱导缓解治疗中效果尤为显著。展开更多
In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great po...In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.展开更多
文摘目的评价沙利度胺及其类似物在炎症性肠病(IBD)治疗中的有效性以及安全性。方法检索PubMed、Cochrane Center Register of Controlled Trails、Embase、Web of Science、中国期刊全文数据库、万方数据库、维普数据库,系统性评价沙利度胺及其类似物治疗IBD的有效性及安全性,就诱导缓解率、维持缓解率、不良反应事件进行总结分析。结果共纳入16篇文献(3篇RCT,13篇病例系列研究),333例IBD患者。2篇RCT显示在克罗恩病(Crohn’s disease,CD)和溃疡性结肠炎(ulcerative colitis,UC)患者中,沙利度胺均能够显著提高诱导缓解率(46.4%vs 11.5%,P=0.010;83.3%vs 18.8%,P=0.005)。病例系列研究显示沙利度胺能够诱导CD缓解,合并缓解率为50%(95%CI=36%~65%)。沙利度胺不良反应中神经系统不良反应最为常见(207/316,65.5%),周围神经病变是患者停药的最常见原因。结论沙利度胺治疗IBD疗效理想,在CD诱导缓解治疗中效果尤为显著。
基金Project (No. 5959438) supported by Microsoft (China) Co., Ltd
文摘In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.
