Development of a TaqMan assay for the six major genotypes of hepatitis C virus: Comparison with commercial assays
Bibliographic record
Abstract
A quantitative real-time PCR assay was developed that detects genomic RNA from reference strains representing the six major genotypes of hepatitis C virus (HCV) with equal sensitivity and accurately measured HCV RNA in JFH1 HCV-infected Huh7.5 cells. The method is indirectly calibrated to the first international (WHO 96/790) HCV standard preparation and has a linear dynamic range of 10(2.6)-10(6.5) IU/ml. In addition, the inter- and intra-assay precision were approximately 3% CV and <2% CV, respectively. Comparison with results obtained by commercially available HCV RNA Nucleic Acid Technology kits (Versant HCV RNA 3.0 b-DNA and Amplicor HCV Monitor), that also employ the WHO standard, allowed validation of the TaqMan assay against all major HCV genotypes. Both commercial methods detected HCV RNA over a wide dynamic range, but showed a consistent difference of about 0.3 log10 when evaluating samples of different HCV genotypes. The genome titers obtained with the three methods correlated with the infectivity titers previously determined for the HCV reference strains. TaqMan assays have become an essential tool to follow viral load in clinical samples and cell culture-based experiments and this technology offers significant advantages in linear dynamic range, sensitivity and customization.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".