Résumé
The human scavenger receptor B1 [hSR-B1/CLA] is a HDL receptor with various anti-atherogenic functions which is expressed on the platelet surface. The anti thrombotic function of HDL includes the modulation of platelet reactivity, coagulation, and endothelial function. The goal of this study is to detect the level of HDL receptor on platelets of CAD patients with atherosclerotic disease. Expressions of the hSRB1 receptor on platelets of 31 CAD patients with atherosclerotic plaque and 20 healthy controls was detected using flow cytometry. Moreover, the lipid panel tests were assayed by Chemistry auto analyzer and compared with healthy controls. Our findings show that abundance of hSR-B1/CLA-1 was significantly reduced on the surface of platelets from CAD patients with atherosclerotic disease compared with healthy control [6/8 % vs. 13/6 %], [P <0,001]. The HDL receptor [hSR-B1/CLA-1] expression on platelets inversely correlates with the risk of coronary heart disease. Our findings suggest that determining the level of hSR-B1/CLA-1 on the platelets may be a useful laboratory marker for CAD investigation
Résumé
The aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of gene expression data using artificial neural networks
The studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in Durham in 2009. Of all the studied patients, 66 patients were suffering from thromboembolic syndrome and 51 people were enrolled in the study as the control group
The gene expression level of 22277 was measured for all the samples and was entered into the model as the main variable. Due to the high number of variables, principal components analysis and auto-encoder neural network methods were used in order to reduce the dimension of data
The results showed that when using auto-encoder networks, the classification accuracy was 93.12. When using the PCA method to reduce the size of the data, the obtained accuracy was 78.26, and hence a significant difference in the accuracy of classification was observed. If auto-encoder network method is used, the sensitivity and specificity will be 92.58 and 93.68 and when PCA method is used, they will be 0.77 and 0.78 respectively. The results suggested that auto-encoder networks, compared with the PCA method, had a higher level of accuracy for the classification of thromboembolic syndrome status
Résumé
AITP mostly occur in children accompanied by variable clinical sings including petechiae, purpura, ecchymosis and severe bleeding. This study has determined and characterized the anti-platelet glycoproteins in children with ITP. The aim of this study was to determinate anti-platelet glycoproteins [GPs] using MAIPA method. During 18 months 38 children with clinical signs of AITP were studied in Mofid children hospital. To determine anti-platelet antibodies by ELISA technique, washed O negative platelets were used as a source of platelet antigens. MAIPA method was used to detect antibodies against individual platelet membrane glycoprotein. The anti-platelet antibodies level above mean+ 3SD of control group was assumed as positive. The results indicated that the platelet count ranges was between 2×109/L and 95×109/L. 63.5 % out of 38 patients were anti-platelet antibodies positive with ELISA method. The correlation between the above patients with anti-platelet antibody positive and clinical signs was 0.4. Results for determination of antibody against platelet GPIIb/IIIa, GPIb/IX and GPIa/IIa using MAIPA method were 44%, 51% and 25% respectively. In conclusion the preference of MAIPA method is the detection of very small amount of antibody. Since MAIPA is the specific method for the detection of antibody against glycoprotein antigens, it has the advantage of differentiating immune and non-immune thrombocytopenia