Evaluation of Two Commercial Kits for the Detection of Genotypic Drug Resistance on a Panel of HIV Type 1 Subtypes A Through J
Bibliographic record
Abstract
We compared the two commercially available sequencing kits for HIV-1 drug resistance testing, the ViroSeq Genotyping System (Applied Biosystems, Foster City, CA, U.S.A.) and the TRUGENE HIV-1 Genotyping Kit (Visible Genetics, Inc., Toronto, Ontario, Canada), with our in-house genotyping system. Fifteen viral isolates from African patients (6 treated and 9 untreated) covering a panel of HIV-1 subtypes A through J and 7 plasma samples from Belgian and African patients (2 treated and 5 untreated) were tested. All the samples could be amplified and sequenced by the three systems; however, for all systems, alternative amplification/sequencing primers had to be used for some samples belonging to subtype B as well as to other subtypes. The consensus sequence was partially derived from only one strand for the in-house system and for the ViroSeq Genotyping System. The TRUGENE HIV-1 Genotyping Kit scored the highest number of ambiguities, followed by the ViroSeq Genotyping System and the in-house system. For 11 samples, these differences in reporting mixtures affected 14 resistance-related positions, which altered the interpretation toward protease inhibitors for 2 samples when using version 1.2 RetroGram software (Virology Networks, Utrecht, The Netherlands). All three systems were able to sequence diluted samples with a viral load down to 10 3 or 10 4 RNA copies/ml. Our data therefore suggest that the performance of amplification and sequencing primers must be improved to allow fast and reliable resistance testing for all HIV-1 subtypes.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".