Highly Diverse Hepatitis C Strains Detected in Sub‐Saharan Africa Have Unknown Susceptibility to Direct‐Acting Antiviral Treatments
Bibliographic record
Abstract
The global plan to eradicate hepatitis C virus (HCV) led by the World Health Organization outlines the use of highly effective direct-acting antiviral drugs (DAAs) to achieve elimination by 2030. Identifying individuals with active disease and investigation of the breadth of diversity of the virus in sub-Saharan Africa (SSA) is essential as genotypes in this region (where very few clinical trials have been carried out) are distinct from those found in other parts of the world. We undertook a population-based, nested case-control study in Uganda and obtained additional samples from the Democratic Republic of Congo (DRC) to estimate the prevalence of HCV, assess strategies for disease detection using serological and molecular techniques, and characterize genetic diversity of the virus. Using next-generation and Sanger sequencing, we aimed to identify strains circulating in East and Central Africa. A total of 7,751 Ugandan patients were initially screened for HCV, and 20 PCR-positive samples were obtained for sequencing. Serological assays were found to vary significantly in specificity for HCV. HCV strains detected in Uganda included genotype (g) 4k, g4p, g4q, and g4s and a newly identified unassigned g7 HCV strain. Two additional unassigned g7 strains were identified in patients originating from DRC (one partial and one full open reading frame sequence). These g4 and g7 strains contain nonstructural (ns) protein 3 and 5A polymorphisms associated with resistance to DAAs in other genotypes. Clinical studies are therefore indicated to investigate treatment response in infected patients. Conclusion: Although HCV prevalence and genotypes have been well characterized in patients in well-resourced countries, clinical trials are urgently required in SSA, where highly diverse g4 and g7 strains circulate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".