Identification of 19 Novel Hepatitis C Virus Subtypes—Further Expanding HCV Classification
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) is currently classified into 8 genotypes and 86 subtypes. The objective of this study was to characterize novel HCV subtypes and to investigate the impact of subtypes on treatment outcome. METHODS: Full-genome sequencing was performed on HCV plasma samples with <85% sequence homology of NS3, NS5A, and/or NS5B to HCV genotype (GT) 1-8 reference strains. RESULTS: A total of 14 653 patients with GT1-6 HCV infection were enrolled in clinical studies of sofosbuvir-based regimens. For the majority of the patients, a specific subtype could be assigned based on a close genetic relationship to previously described subtypes. However, for 19 patients, novel subtypes were identified with <85% homology compared with previously described subtypes. These novel subtypes had the following genotypes: 9 in GT2, 5 in GT4, 2 in GT6, and 1 each in GT1, GT3, and GT5. Despite the presence of polymorphisms at resistance-associated substitution positions, 18 of the 19 patients treated with sofosbuvir-containing therapy achieved SVR12. CONCLUSIONS: Nineteen novel HCV subtypes were identified, suggesting an even greater genetic diversity of HCV subtypes than previously recognized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".