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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".