Literature DB >> 27664028

A simple real-time polymerase chain reaction assay using SYBR Green for hepatitis C virus genotyping.

Hosam Zaghloul1, A A El Morsi2, H E Soweha2, Ashraf Elsayed2, S Seif3, Hossam El-Sharawy2.   

Abstract

Detection of the hepatitis C virus (HCV) genome is crucial for diagnosis of HCV infection and for monitoring the efficacy of HCV treatment. Thus, we aimed to develop a convenient screening test for common HCV genotypes based on melting curve analysis with PCR. Serum samples were drawn from 124 patients with known HCV infection confirmed to be antibody and HCV RNA positive. A characteristic melting curve was obtained by monitoring the fluorescence as the temperature increased through the melting point of the PCR product. Results were compared with those obtained by the restriction fragment length polymorphism (RFLP) genotyping method. The melting curve analysis indicated that the different genotypes had discrete melting points (P < 0.0001): 90.43 +/- 0.065 degrees for genotype 1 (n = 35), 90.21 +/- 0.064 degrees for genotype 2 (n = 18), 90.62 +/- 0.045 degrees for genotype 3 (n = 29) and 90.84 +/- 0.130 degrees for genotype 4 (n = 42). The genotype was determined for all samples using the newly developed method as well as RFLP, and the two systems produced concordant results. The sensitivity of the assay was 91.4 % for genotype 1, 83.3 % for genotype 2, 93.1 % for genotype 3, and 85.7 % for genotype 4. Genotypes detected by melting curve analysis significantly correlated with those detected by RFLP (r = 0.946, P < 0.0001) with a strong linear relationship (r 2 = 0.895). This melting curve analysis is a rapid, convenient and low-cost screening test for differentiation of HCV genotypes 1-4.

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Year:  2016        PMID: 27664028     DOI: 10.1007/s00705-016-3059-5

Source DB:  PubMed          Journal:  Arch Virol        ISSN: 0304-8608            Impact factor:   2.574


  1 in total

1.  Downregulation of HOXA3 in lung adenocarcinoma and its relevant molecular mechanism analysed by RT-qPCR, TCGA and in silico analysis.

Authors:  Bin-Liang Gan; Rong-Quan He; Yu Zhang; Dan-Ming Wei; Xiao-Hua Hu; Gang Chen
Journal:  Int J Oncol       Date:  2018-07-30       Impact factor: 5.650

  1 in total

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