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- 저자 : ( Xiaoqin Ouyang ) (검색결과 2 건)
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Fatty Acid-Binding Protein 4 in Patients with and without Diabetic Retinopathy
Ping Huang,Xiaoqin Zhao,Yi Sun,Xinlei Wang,Rong Ouyang,Yanqiu Jiang,Xiaoquan Zhang,Renyue Hu,Zhuqi Tang,Yunjuan Gu 대한당뇨병학회 2022 Diabetes and Metabolism Journal Vol.46 No.4
Background: Fatty acid-binding protein 4 (FABP4) has been demonstrated to be a predictor of early diabetic nephropathy. However, little is known about the relationship between FABP4 and diabetic retinopathy (DR). This study explored the value of FABP4 as a biomarker of DR in patients with type 2 diabetes mellitus (T2DM).Methods: A total of 238 subjects were enrolled, including 20 healthy controls and 218 T2DM patients. Serum FABP4 levels were measured using a sandwich enzyme-linked immunosorbent assay. The grade of DR was determined using fundus fluorescence angiography. Based on the international classification of DR, all T2DM patients were classified into the following three subgroups: non-DR group, non-proliferative diabetic retinopathy (NPDR) group, and proliferative diabetic retinopathy (PDR) group. Multivariate logistic regression analyses were employed to assess the correlation between FABP4 levels and DR severity.Results: FABP4 correlated positively with DR severity (<i>r</i>=0.225, <i>P</i>=0.001). Receiver operating characteristic curve analysis was used to assess the diagnostic potential of FABP4 in identifying DR, with an area under the curve of 0.624 (37% sensitivity, 83.6% specificity) and an optimum cut-off value of 76.4 μg/L. Multivariate logistic regression model including FABP4 as a categorized binary variable using the cut-off value of 76.4 μg/L showed that the concentration of FABP4 above the cut-off value increased the risk of NPDR (odds ratio [OR], 3.231; 95% confidence interval [CI], 1.574 to 6.632; <i>P</i>=0.001) and PDR (OR, 3.689; 95% CI, 1.306 to 10.424; <i>P</i>=0.014).Conclusion: FABP4 may be used as a serum biomarker for the diagnosis of DR.
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( Qiuhua Wang ),( Xiaoqin Ouyang ),( Jiacheng Zhan ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.
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