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Liver Int ; 32(10): 1527-34, 2012 Nov.
Article in English | MEDLINE | ID: mdl-22863037

ABSTRACT

BACKGROUND: A CD antibody microarray has been previously developed allowing semi-quantitative identification of greater than 80 CD antigens on circulating leucocytes from peripheral blood samples. This assay, which uses a live cell-capture technique, enables an extensive leucocyte immunophenotype determination in a single analysis and to date this has been used successfully to characterise diseases including human leukaemias and HIV infection. AIMS: To determine CD antigen expression profiles for patients with various liver diseases and to look for preserved disease-specific signatures. METHODS: Three liver disease groups including hepatitis C (HCV) (n = 35), non-alcoholic steatohepatitis (NASH) (n = 21) and alcohol-related liver disease (n = 14) were compared with a normal group (n = 23). Hierarchal Clustering (HCL) and Principal Component Analysis (PCA) of the data revealed distinct binding patterns for patients with and without cirrhosis. RESULTS: Patients with cirrhosis and portal hypertension compared with those without cirrhosis had significantly reduced expression of several markers of T-cell function including CD45, CD8, CD28 and TCR α/ß. Disease prediction algorithms based on the expression data were able to discriminate cirrhotics from non-cirrhotics with 71% overall success, which improved to 77% when only patients with HCV were considered. CONCLUSIONS: These results demonstrate disease-specific consensus patterns of expression of CD antigens for patients with chronic liver disease, suggesting that the CD antibody array is a promising tool in the analysis of human liver disease, and with further refinement may have future research and clinical utility.


Subject(s)
Algorithms , Antibodies , Antigens, CD/metabolism , Immunophenotyping/methods , Liver Diseases/diagnosis , Protein Array Analysis/methods , Adult , Aged , Aged, 80 and over , Antibodies/metabolism , Cluster Analysis , Female , Humans , Leukocytes/metabolism , Male , Middle Aged , Principal Component Analysis
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