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首页> 外文期刊>Journal of Clinical Microbiology >Distinguishing Acute from Chronic Hepatitis C Virus (HCV) Infection Based on Antibody Reactivities to Specific HCV Structural and Nonstructural Proteins
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Distinguishing Acute from Chronic Hepatitis C Virus (HCV) Infection Based on Antibody Reactivities to Specific HCV Structural and Nonstructural Proteins

机译:基于对特定HCV结构和非结构蛋白的抗体反应性,区分急性丙型肝炎病毒(HCV)感染

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Currently available serological assays for detection of antibodies to hepatitis C virus (HCV) cannot reliably discriminate acute from chronic HCV infection. We developed a multiplexed, flow-cytometric microsphere immunoassay to measure anti-HCV-IgG reactivities to the core, NS3, NS4, and NS5 HCV recombinant proteins and applied it to 99 serum samples from 24 anti-HCV seroconverters and 141 anti-HCV-IgG and HCV RNA-positive plasma specimens from chronically infected people. Differences in the geometric means or means of signal/cutoff ratios between the two sample sets were statistically significant for all the antigens tested. A multivariate logistic regression model correctly classified the samples in two groups, with a cross-validation accuracy of 90.8% for the acute group and 97.2% for the chronic group. The immunoassay described has the potential to distinguish acute from chronic HCV infection.
机译:当前可用于检测丙型肝炎病毒(HCV)抗体的血清学检测方法不能可靠地将急性与慢性HCV感染区分开。我们开发了一种多重流式细胞术微球免疫测定法,以测量针对核心,NS3,NS4和NS5 HCV重组蛋白的抗HCV-IgG反应性,并将其应用于来自24个抗HCV血清转化器和141个抗HCV-的99个血清样品中来自慢性感染人群的IgG和HCV RNA阳性血浆标本。对于所有测试的抗原,两个样品组之间的几何平均值或信号/截止比平均值的差异在统计学上是显着的。多元逻辑回归模型正确地将样品分为两组,交叉验证的准确率分别为急性组90.8%和慢性组97.2%。所描述的免疫分析方法有可能区分急性和慢性HCV感染。

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