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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".