Analysis of resistance‐associated substitutions in acute hepatitis C virus infection by deep sequencing across six genotypes and three continents
Bibliographic record
Abstract
Several direct-acting antivirals (DAAs) have been approved for the treatment of chronic hepatitis C virus (HCV) infections, opening the door to highly effective interferon-free treatment regimens. Resistance-associated substitutions (RASs) have been reported both in treatment-naïve patients and following treatment with protease (NS3), phosphoprotein (NS5A) and polymerase (NS5B) inhibitors. The prevalence of naturally occurring RASs in untreated HCV-infected individuals has mostly been analysed in those infected with genotype 1 (GT1), in the late phase of infection, and only within limited regions of the genome. Furthermore, the geographic distribution of RASs remains poorly characterized. In this study, we used next-generation sequencing to analyse full-length HCV genomes for the prevalence of RASs in acute HCV infections identified in nine international prospective cohorts. RASs were analysed in 179 participants infected with all six major HCV genotypes (GT1-GT6), and the geographic distribution of RASs was assessed in 107 GT1a and GT3a samples. While RASs were detected at varied frequencies across the three genomic regions, and between genotypes, RASs relevant to multiple DAAs in the leading IFN-free regimens were rarely detected in combination. Low-frequency RASs (<10% of the viral population) were also shown to have a GT-specific distribution. The main RASs with geographic associations were NS3 Q80K in GT1a samples and NS5B N142T in GT3a. These data provide the backdrop for prospective surveillance of RASs during DAA treatment scale-up.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".