ReviewVirusdisease2020
Major mutations in the NS3 gene region of hepatitis C virus related to the resistance to direct acting antiviral drugs: a systematic review.
Review in Virusdisease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
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Abstract
Hepatitis C virus (HCV) remains a global public health problem with high prevalence rates and chronicity of infection. Present work aimed to describe the main mutations in the NS3 region of the HCV genome related to the resistance of patients to the currently available direct-acting antivirals (DAAs). To guide the study description, the preferred items in the PRISMA protocol for systematic review were used. The data collected were HCV genotypes and subtypes and mutations in HCV NS3, general and stratified by continent. The 10 papers selected for this systematic review reported studies in seven countries, on three continents, and generated data of 2937 patients. The most frequent HCV subtype was 1a. Prevalence of genotypes suggested that there were few demographic regions reached by the studies, since there were regional variations in the type of genotypes reported in the available bibliographies. Of the total study population, 35.3% (n = 1037) had mutations in the NS3 gene region of HCV, suggesting a high rate of resistance to DAAs and a low sustained virologic response among those who used some therapeutic option. Ten major mutations were identified: Q80K, V170I, S122G, V36L, T54S, D168Q, A156S, Q80G, S122R, and V55A. The Q80K mutation was the highlight of the study, appearing not only with greater representativity (61.6%) but also as the only one described in the three continents analyzed. This systematic review reinforces the need to carry out more studies of detection of these mutations to fill in all information gaps that might help in optimization of treatment.
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