HCV Diagnosis and Sequencing Using Dried Blood Spots from Patients in Kinshasa (DRC): A Tool to Achieve WHO 2030 Targets is a research paper published in Diagnostics (2021). On theSindex it has a DataRank of 0.717. It has been cited 17 times, with 11 citing works in its 1-hop citation network.
The World Health Organization has established an elimination plan for hepatitis C virus (HCV) by 2030. In Sub-Saharan Africa (SSA) access to diagnostic tools is limited, and a number of genotype 4 subtypes have been shown to be resistant to some direct-acting antivirals (DAAs). This study aims to analyze diagnostic assays for HCV based on dried blood spots (DBS) specimens collected in Kinshasa and to characterize genetic diversity of the virus within a group of mainly HIV positive patients. HCV antibody detection was performed on 107 DBS samples with Vidas® anti-HCV and Elecsys anti-HCV II, and on 31 samples with INNO-LIA HCV. Twenty-six samples were subjected to molecular detection. NS3, NS5A, and NS5B regions from 11 HCV viremic patients were sequenced. HCV seroprevalence was 12.2% (72% with detectable HCV RNA). Both Elecsys Anti-HCV and INNO-LIA HCV were highly sensitive and specific, whereas Vidas® anti-HCV lacked full sensitivity and specificity when DBS sample was used. NS5B/NS5A/NS3 sequencing revealed exclusively GT4 isolates (50% subtype 4r, 30% 4c and 20% 4k). All 4r strains harbored NS5A resistance-associated substitutions (RAS) at positions 28, 30, and 31, but no NS3 RAS was detected. Elecsys Anti-HCV and INNO-LIA HCV are reliable methods to detect HCV antibodies using DBS. HCV subtype 4r was the most prevalent among our patients. RASs found in subtype 4r in NS5A region confer unknown susceptibility to DAA.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.434
From this paper's citation signal
Citation Network Contribution
0.284
From 7 citing papers with measurable signal
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 60% comes from its base citations and 40% from the citation network (7 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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