Determination of reverse transcriptase inhibitor resistance mutations in HIV-1 infected children in Côte d’Ivoire
Bibliographic record
Abstract
Treatment scale-up is leading to a progressive increase in HIV resistance to antiretrovirals, especially in children. To assess resistance to reverse transcriptase inhibitors (RTIs) in HIV-1 infected children in Côte d'Ivoire, genotypic resistance tests were performed and interpreted using the ANRS algorithm (www.hivfrenchresistance.org). Phylogenetic trees were created using BioEdit v7 and Mega7 software. The frequency of resistance to at least one RTI was 79%. It was 88% for nucleoside reverse transcriptase inhibitors (NRTIs), 71% for non-nucleoside reverse transcriptase inhibitors (NNRTIs), and 63% for both classes (NRTI + NNRTI). The frequency of resistance was 50% for the ZDV + 3TC + EFV combination, 42% for the ABC + 3TC + EFV combination, and 8% for the TDF + 3TC + EFV combination. Frequently encountered resistance mutations were for NRTIs: M184V (88%), TAMs (67%), T215F/I/V/Y (33%), and L74I/V (24%); for NNRTIs: K103N/S (74%), P225H (26%), and G190A/E/Q (24%). The synthesis of phylogenetic analyses showed the predominance of the viral subtype CRF02_AG (85%). These results show a high prevalence of resistance to RTIs in children infected with HIV-1. Hence the interest of a more accessible monitoring of viral load and genotypic resistance tests in HIV-1 infected children undergoing treatment in Côte d'Ivoire.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".