Identification of HCV Resistant Variants against Direct Acting Antivirals in Plasma and Liver of Treatment Naïve Patients
Bibliographic record
Abstract
Current standard-of-care treatment of chronically infected hepatitis C virus (HCV) patients involves direct-acting antivirals (DAA). However, concerns exist regarding the emergence of drug -resistant variants and subsequent treatment failure. In this study, we investigate potential natural drug-resistance mutations in the NS5B gene of HCV genotype 1b from treatment-naïve patients. Population-based sequencing and 454 deep sequencing of NS5B gene were performed on plasma and liver samples obtained from 18 treatment- naïve patients. The quasispecies distribution in plasma and liver samples showed a remarkable overlap in each patient. Although unique sequences in plasma or liver were observed, in the majority of cases the most dominant sequences were shown to be identical in both compartments. Neither in plasma nor in the liver codon changes were detected at position 282 that cause resistance to nucleos(t)ide analogues. However, in 10 patients the V321I change conferring resistance to nucleos(t)ide NS5B polymerase inhibitors and in 16 patients the C316N/Y/H non-nucleoside inhibitors were found mainly in liver samples. In conclusion, 454-deep sequencing of liver and plasma compartments in treatment naïve patients provides insight into viral quasispecies and the pre-existence of some drug-resistant variants in the liver, which are not necessarily present in plasma.
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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 | 0.000 | 0.000 |
| 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".