Rapid Genotyping of Hepatitis C Virus by Primer-Specific Extension Analysis
Bibliographic record
Abstract
Quick and accurate genotyping of hepatitis C virus (HCV) is becoming increasingly important for clinical management of chronic infection and as an epidemiological marker. Furthermore, the incidence of HCV infection with mixed genotypes has clinical significance that is not addressed by most genotyping methods. We have developed a fluorescence-based genotyping assay called primer-specific extension analysis (PSEA) for the most prevalent HCV genotypes and have demonstrated the capacity of PSEA-HCV for detecting mixed-genotype HCV infections. PSEA-HCV detects genotype-specific sequence differences in the 5' untranslated region of HCV in products amplified by the COBAS AMPLICOR HCV Test, v2.0. Simulated mixed HCV infection of plasma with RNase-resistant RNA controls demonstrates that PSEA-HCV can detect as many as five genotypes in one specimen. Furthermore, in dual-genotype simulations, PSEA-HCV can unequivocally detect both genotypes, with one genotype representing only 3.1% of the mixture (313/10,000 IU in starting plasma). Compared to INNO-LiPA HCV II, both assays determined the same genotype for 191/199 (96%) patient specimens (175 subtype and 16 genotype-only identifications). Following the initial evaluation, PSEA-HCV was used routinely to genotype HCV from patient specimens submitted to our laboratory (n=312). Seventeen (5.4%) mixed infections were identified. The distribution of single-infection HCV genotypes in our population was 60.9% type 1 (n=190), 12.8% type 2 (n=40), 20.2% type 3 (n=63), 0.3% type 4 (n=1), and 0.3% other (n=1). In conclusion, PSEA-HCV provides an inexpensive, high-throughput screening tool for rapid genotyping of HCV while reliably identifying mixed HCV infections.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".