Background: Cyclooxygenase-2 (COX-2) plays an important role in the monocyte-platelet aggregate (MPA)-medicated inflammatory response and possible coronary artery disease (CAD). This study aimed to assess the predicting significance of COX-2 expression in peripheral blood monocyte for CAD.Methods: A total of 66 patients with CAD including stable angina (SA) and unstable angina (UA) were enrolled. The inflammatory indexes including white blood cell (WBC) count, high-sensitive C reactive protein (hs-CRP), serum monocyte chemoattractant protein-1 (MCP-1) and MPA levels were measured.The western-blotting assay and reverse transcription-polymerase chain reaction (RT-PCR) analysis were used to detect the COX-2 expression in peripheral blood monocytes. Furthermore, the correlation between COX-2 expression and MPA levels, and the association of COX-2 expression with CAD risk were assessed. Results:The UA patients demonstrated higher levels of inflammatory indexes than the SA patients (P<0.001). Simultaneously, higher MPA levels and enhanced COX-2 expression were observed in the UA patients (P<0.01). The patients with enhanced COX-2 expression exhibited higher MPA than those without (P<0.01), and patients with increased MPA also demonstrated enhanced COX-2 expression (P<0.001).Moreover, the levels of COX-2 protein expression was positively related to the MPA formation rates (R 2 =0.4933, P<0.01), and enhanced COX-2 expression was independently associated with CAD risk [odds ratio (OR): 6.322, 95% confidence interval (CI): 4.544-8.978 ]. Conclusions:The COX-2 expression of peripheral blood monocytes can be used as an independent predictor for CAD.

Through the integration of global and local classifiers can be better to image normal and abnormal (including cancer and hyperplasia) of the classification, But early cancer and hyperplasia in shape have only a very slight difference. Therefore, we also need the new features to design a new classifier to realize the classification of cancer and hyperplasia. In this paper, we use the improved peA + LDA dimension reduction method to overcome the traditional peA + LDA method, using which the test samples have poor generalization ability . at the same time ,We have done a related experiment to achieve the cancerous cells and proliferation of cells in the classification problem. comparedWith the traditional peA + LDA classification methods ,we found the improved peA + LDA to achieve a better recognition effect.

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