Objective To systematically review the relationship between the expression of Survivin mRNA and ovarian cancer. Methods PubMed, The Cochrane Library (Issue 11, 2016), CBM, CNKI, VIP and WanFang Data databases were searched to identify case-control studies concerning the association between the expression of Survivin mRNA and ovarian cancer up to November 2016. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.2 software. Results A total of 10 studies were included. The positive of Survivin mRNA in ovarian cancer group was significantly higher than that in control group (OR=24.63, 95% CI 13.44 to 45.15,P<0.000 01). The positive of Survivin in low differentiated group was significantly higher than that in high differentiation group (OR=3.69, 95% CI 2.29 to 5.93,P<0.000 01). The positive of Survivin in clinical stage of Ⅲ-Ⅳ was significantly higher than that in clinical stage of Ⅰ-Ⅱ (OR=4.76, 95% CI 2.99 to 7.57,P<0.000 01), respectively. However, the expression of Survivin mRNA was not associated with lymph node metastasis, ascites and histological type. Conclusion The current evidence indicates that the expression of Survivin mRNA is significantly correlated with ovarian cancer and its clinicopathologic features. Due to the limited quantity and quality of includes studies, the above conclusions are needed to be verified by more high quality studies.
ObjectiveTo conduct a scoping review of studies on the application of knowledge mapping in the field of rare diseases at home and abroad, in order to clarify the content and status of application and provide references for future research in this field. MethodsRelevant studies in PubMed, Web of Science, Embase, MEDLINE, CNKI, WanFang Data, VIP, and CBM databases were searched, using the Joanna Briggs Institute Scoping Review Guidelines in Australia as the methodological framework, and the search time frame was from the establishment of the database to June 1, 2023. ResultsTwenty-five papers were included, and the main applications of knowledge graphs in the field of rare diseases were knowledge management, assisted diagnosis, drug repositioning and decision support, involving techniques such as knowledge representation, knowledge extraction, knowledge reasoning, knowledge fusion and knowledge storage.ConclusionKnowledge graphs have shown positive results in fusing and exploiting multi-source information, aiding disease prediction and diagnosis and drug development, but further technical improvements are needed.