Use of Sequence Analysis of the NS5B Region for Routine Genotyping of Hepatitis C Virus with Reference to C/E1 and 5′ Untranslated Region Sequences
Why this work is in the frame
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Bibliographic record
Abstract
Nucleotide sequence analysis of the NS5B region was performed to identify genotypes of 8,479 hepatitis C virus (HCV) RNA-positive patient samples collected in the Canadian province of Quebec. Genotypes could be determined for 97.3% of patients. Genotypes 1 to 6 were found in 59.4, 9.0, 25.7, 3.6, 0.6, and 1.8% of patients, respectively. Two isolates did not classify within the six genotypes. The subtype 1 distribution was 76.7% 1a, 22.6% 1b, and 0.7% others, while the subtype 2 distribution was 31.8% 2a, 47.6% 2b, 10.9% 2c, 4.1% 2i, and 5.6% others. Subtype 3a accounted for 99.1% of genotype 3 strains, while all genotype 5 samples were of subtype 5a. The subtype 4 distribution was 39.2% 4a, 15.4% 4k, 11.6% 4d, 10.2% 4r, and 23.6% others. The subtype 6 distribution was 40.4% 6e, 20.5% 6a, and 39.1% others. The 5' untranslated region (5'UTR) sequences of subtype 6e were indistinguishable from those of genotype 1. All samples that did not classify within the established subtypes were also sequenced in C/E1 and 5'UTR. C/E1 phylogenetic reconstructions were analogous to those of NS5B. The sequences identified in this study allowed the provisional assignments of subtypes 1j, 1k, 2m, 2r, 3i, 4q, 6q, 6r, and 6s. Sixty-four (0.8%) isolates classifying within genotypes 1 to 6 could not be assigned to one of the recognized subtypes. Our results show that genotyping of HCV by nucleotide sequence analysis of NS5B is efficient, allows the accurate discrimination of subtypes, and is an effective tool for studying the molecular epidemiology of HCV.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it