Comparison and Application of a Novel Genotyping Method, Semiautomated Primer-Specific and Mispair Extension Analysis, and Four Other Genotyping Assays for Detection of Hepatitis C Virus Mixed-Genotype Infections
Bibliographic record
Abstract
To date the true prevalence of hepatitis C virus (HCV) mixed-genotype infections has not been established mainly because currently available methods are not suitable for the detection of mixed genotypes in a viral population. A novel semiautomated genotyping method, primer-specific and mispair extension analysis (S-PSMEA), which is more reliable than other genotyping assays was developed for detection of HCV mixed-genotype infections. A genotype present at levels as low as 0.8% in a defined mix of HCV genotypes was detected, showing a 20-fold increase in sensitivity over that of direct DNA sequencing. A total of 434 HCV isolates were genotyped and analyzed for a comparative study of the accuracy between S-PSMEA and four current genotyping methods. The results showed that viruses in approximately 40% of the samples from this group determined to be infected with mixed genotypes by S-PSMEA were undetected by direct DNA sequencing due to its low sensitivity. Type-specific PCR, line probe assay, and restriction fragment length polymorphism analysis performed poorly, being able to identify only 38.5, 16.1, and 15.4% of mixed-genotype infections, respectively, that were detected by direct DNA sequencing. The prevalence of mixed-genotype infections detected by S-PSMEA was 7.9% (12 of 152 donors) among HCV-infected blood donors, 14.3% (15 of 105) among patients with chronic hepatitis C, and 17.1% (6 of 36) among thalassemia patients who had received multiple transfusions. The data lead us to conclude that HCV mixed-genotype infections are more common than previously estimated and that S-PSMEA may be the method of choice when detection of genotypes present at low levels in mixed-genotype infections is required due to its higher level of sensitivity.
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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 | 0.005 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".