Pitfalls of antiretroviral drug resistance genotyping of HIV-1 Group M and Group N from Cameroon by sequenced-based assays
Bibliographic record
Abstract
BACKGROUND: HIV-1 genotyping for antiretroviral drug resistance mutations (DRMs) were developed based basically on subtype B HIV-1 Group M, which represents only 10% of HIV strains worldwide. In sub-Saharan Africa, non-B subtypes HIV-1 largely predominate and HIV-1 genetic diversity could affect the performance of drug resistance genotyping assays. We compared prospectively the performance of the ViroSeq(®) and Trugene(®) genotyping assays to detect DRM in HIV-1-infected adult patients living in Douala, Cameroun. MATERIALS AND METHODS: DRM in protease (P) and reverse transcriptase (RT) genes were assessed in parallel using both ViroSeq(®) and Trugene(®) assays in plasma samples from 45 first-line antiretroviral treatment-experienced patients in Douala, Cameroon. RESULTS: Trugene HIV-1 Genotyping Assay(®) (Siemens Health Care Diagnostics, NY, USA) and ViroSeq HIV-1 Genotyping System(®)(Celera Diagnostics, CA, USA) assessed equivalently antiretroviral DRMs in P and RT genes from non-B HIV-1 Group M in 44 Cameroonian adults in virological failure; Trugene(®) was slightly more sensitive than ViroSeq(®) (100% vs. 91%). One patient infected by HIV-1 Group N was successfully amplified only by the Trugene HIV-1 Genotyping assay(®), while ViroSeq HIV-1 Genotyping System v2.0(®) assay could not. CONCLUSION: Results showed the higher performance of the Trugene(®) system to detected and amplify P and RT genes targeting DRM to the principal antiretroviral drugs used in sub-Saharan Africa. Discrepancies between the results of HIV viral load assays and molecular tests should alert clinicians and virologists to the possibility of infection by an atypical variant virus, especially in Central Africa where very broad HIV-1 genetic diversity exists.
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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 | 0.001 | 0.000 |
| 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".