Convolution Neural Networks Using Deep Matrix Factorization for Predicting Circrna-Disease Association.

CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological experiments identify new associations is time-consuming and high-cost. As a result, there is a need of building efficient and achievable computation models to predict potential circRNA-disease associations. In this paper, we design a novel convolution neural networks framework(DMFCNNCD) to learn features from deep matrix factorization to predict circRNA-disease associations. Firstly, we decompose the circRNA-disease association matrix to obtain the original features of the disease and circRNA, and use the mapping module to extract potential nonlinear features. Then, we integrate it with the similarity information to form a training set. Finally, we apply convolution neural networks to predict the unknown association between circRNAs and diseases. The five-fold cross-validation on various experiments shows that our method can predict circRNA-disease association and outperforms state of the art methods.

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

[Expression of hSef and FGF-2 in epithelial ovarian tumor].

OBJECTIVE: To detect the expression of human similar expression to FGF gene(hSef) and fibroblast growth factor-2(FGF-2) and their correlation with epithelial ovarian tumor. METHODS: Immunohistochemical SP staining was used to detect the expression of hSef and FGF-2 proteins in 31 cases of epithelial ovarian carcinoma (EOC), 18 cases of benign epithelial tumor (BET), 10 cases of normal ovarian (NO) tissues collected from July 2007 to May 2008. The expression of hSef mRNA in 24 cases of EOC, BET and NO collected from July 2008 to May 2009 were analyzed by RT-PCR. RESULTS: The results of immunohistochemical study showed that the expression of hSef in the EOC tissues were significantly lower than that in the NO and BET (P < 0.001). However, the expression of FGF-2 was higher (P = 0.002). The expression of hSef had a negative correlation with FGF-2 (r(s) = -0.324, P = 0.012). The RT-PCR results showed that there was a gradually declined trend of expression of hSef in NO, BET to EOC (P < 0.001), but the expression of FGF-2 in NO, BET to EOC was gradually increased (P < 0.001), with a significant negative correlation (NO: r(s) = -0.910, P < 0.001; BET: r(s) = -0.859, P < 0.001; EOC: r(s) = -0.888, P < 0.001). CONCLUSIONS: The expression of hSef is decreased in epithelial ovarian carcinoma tissue, but the expression of FGF-2 is increased. It is likely that low hSef expression is related to the the carcinogenesis and development of epithelial ovarian carcinoma by suppressing the promoting effects of FGF-2 to cell proliferation.

[A new monoclonal antibody selectively distributes on hepatocellular membrane].

OBJECTIVE: Screening monoclonal antibodies selectively distribute on hepatocellular membrane by hybridoma technology and exploring their relationship with liver diseases. METHODS: Plasma membrane vesicles of rat hepatocytes were prepared using density gradient centrifugation and BALB/c mice were immunized with the membrane vesicles. Monoclonal antibodies were made with hybridoma technology. The immunizing valences and monoclonal antibodies were detected and screened by ELISA. Mh7 antigen was identified by immunoprecipitation method. Liver tissues of carbon tetrachloride injured rat models and diabetic rat models were used to detect the pathology value of mh7-antigen. RESULTS: Hepatocellular membrane vesicles were obtained successfully. Several monoclonal antibodies were yielded by hybridoma technology. Immunofluorescence and pre-embedding immunogold-silver cytochemistry confirmed that mh7-antigen was a membrane molecule and with a 200KD molecular weight. Immunohistochemistry results indicated mh7 selectively distributed on hepatocellular membrane. Results with rat models demonstrated that mh7-antigen was dramatically reduced in fatty degenerated hepatocyte of carbon tetrachloride injured rat models and distributed as straps in diabetic rat models. CONCLUSIONS: Mh7 is a new hepatocellular membrane monoclonal antibody and may closely related with liver diseases.

Different exercise therapies for treating heart failure: A protocol for overview of systematic reviews and network meta-analysis.

BACKGROUND: Exercise therapies has been shown to be safe and effective as a non-pharmacological management for treating heart failure, At the same time, many clinical trials, systematic review, and meta-analyses have demonstrated the advantages of exercise therapies in heart failure. However, the methodological quality of these systematic reviews and the differences in efficacy between different exercise modes are unclear. Therefore, this study intends to overview of systematic reviews and network meta-analysis of exercise therapies intervention in heart failure, and finally to rank the effects of exercise therapies in the intervention of heart failure, so as to provide certain reference for clinical decision-making. METHODS: From the seven databases: PubMed, EMBASE.com, Web of Science, the Cochrane Library, Chinese biomedical literature database (CBM), Chinese National Knowledge Infrastructure (CNKI), Wan fang Database, and Chongqing VIP (CQVIP) databases. To search for systematic or meta-analysis of different exercise therapies for heart failure from inception to August 2020. According to the inclusion criteria and exclusion criteria, the two researchers independently selected articles and extracted data. In case of differences, a third party shall be sought for settlement. The AMSTAR2 scale, PRISMA scale and GRADE were used to assess the quality and evidence grade of the literature. The eligible randomized controlled trials (RCTs) were selected from the included systematic reviews and updated RCTs from the above systematic reviews to August 2020. GRADE was used for the risk of bias of the included RCTs. Pairwise meta-analyses were performed using the random-effects model, and network meta-analysis of the included RCTs were performed the frequentist framework. All data analyses were completed in Stata 15.0. RESULTS: Finally, a total of 33 articles related to systematic review and meta-analysis were included, there are 28 articles in Chinese and 5 articles in English. The results of this overview and network meta-analysis will be submitted to a peer-reviewed journal for publication. CONCLUSION: This review will provide a comprehensive overview of existing systematic reviews of exercise therapies interventions for heart failure and provide recommendations for clinical practice or guidelines. PROTOCOL REGISTRATION: INPLASY202080118.

[Pre- and post-chemotherapy expressions of Th1 and Th2 type cytokines and their clinical significance in gastric cancer patients].

OBJECTIVE: To investigate the pre- and post-chemotherapy expression levels of Th1 and Th2 type cytokines in peripheral blood CD4(+) T lymphocytes, the changes of Th1/Th2 ratio and their clinical significance in patients with gastric cancer. METHODS: The levels of specific cytokines in 60 gastric cancer patients were detected by flow cytometry before and after chemotherapy with FOLFOX4. RESULTS: The level of IFN-gamma from peripheral blood CD4(+) T lymphocytes after chemotherapy in the whole group of gastric cancer patients was 11.4% +/- 5.0%, significantly higher than that (9.5% +/- 3.4%) before chemotherapy (P < 0.05). The level of IL-10 after chemotherapy was 3.6% +/- 1.2%, significantly lower than that (4.2% +/- 1.8%) before chemotherapy (P < 0.05). The ratio of Th1/Th2 (IFN-gamma/IL-4) after chemotherapy was 3.4 +/- 1.0 versus 3.4 +/- 1.6 before chemotherapy, without significant difference (P > 0.05). Interestingly, the levels of IFN-gamma and TNF-alpha of peripheral blood CD4(+) T lymphocytes in 15 gastric cancer patients who achieved partial response (PR) after chemotherapy were 14.8% +/- 8.0% and 5.9% +/- 2.0%, respectively, both were higher than that (6.9% +/- 2.5% and 4.2% +/- 1.3%) before chemotherapy (both P < 0.05). Furthermore, the ratio of Th1/Th2 (IFN-gamma/IL-4) after chemotherapy was 4.0 +/- 1.5, significantly higher than 2.5 +/- 1.2 before chemotherapy (P < 0.01). CONCLUSION: Effective chemotherapy may reduce the tumor burden and relieve the shift of Th1/Th2 ratio. Improvement in immune function may be a very important therapeutic measure due to poor response of chemotherapy in gastric cancer patients.