PubMed Health⌕ Search

PubMed · 9305663

Diagnostic tests for hepatitis C.

Abstract

Diagnostic tests for hepatitis C can be divided into the following two general categories: 1) serological assays that detect antibody to hepatitis C virus (anti-HCV); and 2) molecular assays that detect, quantify, and/or characterize HCV RNA genomes within an infected patient. Serological assays have been subdivided into screening tests for anti-HCV, such as the enzyme immunoassay (EIA), and supplemental tests such as the recombinant immunoblot assay (RIBA). Three generations of anti-HCV tests have been developed, and each generation has resulted in an improvement in the sensitivity of detecting anti-HCV. Supplemental anti-HCV tests are designed to resolve false-positive testing by EIA, and are appropriately used in low-prevalence settings in which false-positive anti-HCV tests remain a problem. Third-generation anti-HCV tests (EIA-3 and RIBA-3, respectively) contain antigens from the HCV core, nonstructural 3, nonstructural 4, and nonstructural 5 genes. Detection of HCV RNA in patient specimens by polymerase chain reaction (PCR) provides evidence of active HCV infection and is potentially useful for confirming the diagnosis and monitoring the antiviral response to therapy. Optimal HCV PCR assays at present have a sensitivity of less than 100 copies of HCV RNA per milliliter of plasma or serum. Standardization and proficiency testing of diagnostic laboratories performing HCV PCR remains an important problem for future study. Two main technologies exist for assessing HCV RNA levels or viral load. Quantitative PCR is the most sensitive test for determining hepatitis C viral load, whereas the branched-chain DNA test appears to be the most precise method. Major limitations of the current tests are inadequate dynamic range and high variability of PCR-based assays, and poor sensitivity of the branched-chain DNA test. Molecular tests have also been developed to classify HCV into distinct genotypes; the clinical importance of HCV genotype determination remains a subject for future investigation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D R Gretch. 1997. Diagnostic tests for hepatitis C.. https://doi.org/10.1002/hep.510260708

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Comparison of methods for analysis of selective genotyping survival data.

Survival traits and selective genotyping datasets are typically not normally distributed, thus common models used to identify QTL may not be statistically appropriate for their analysis. The objective of the present study was to compare models for identification of QTL associated with survival traits, in particular when combined with selective genotyping. Data were simulated to model the survival distribution of a population of chickens challenged with Marek disease virus. Cox proportional hazards (CPH), linear regression (LR), and Weibull models were compared for their appropriateness to analyze the data, ability to identify associations of marker alleles with survival, and estimation of effects when all individuals were genotyped (full genotyping) and when selective genotyping was used. Little difference in power was found between the CPH and the LR model for low censoring cases for both full and selective genotyping. The simulated data were not transformed to follow a Weibull distribution and, as a result, the Weibull model generally resulted in less power than the other two models and overestimated effects. Effect estimates from LR and CPH were unbiased when all individuals were genotyped, but overestimated when selective genotyping was used. Thus, LR is preferred for analyzing survival data when the amount of censoring is low because of ease of implementation and interpretation. Including phenotypic data of non-genotyped individuals in selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate, and therefore the CPH model is preferred for this scenario, although transformation of the data may also make the Weibull model appropriate for this case. The results from the research presented herein are directly applicable to interval mapping analyses.

Genotype↗