Transmission of HIV-1 drug resistance in Benin could jeopardise future treatment options
Bibliographic record
Abstract
OBJECTIVES: As access to antiretrovirals (ARV) increases in developing countries, the identification of optimal therapeutic regimens and prevention strategies requires the identification of resistance pathways in non-B subtypes as well as the surveillance of drug mutation resistance (SDMR) including the trafficking of viral strains between high-risk groups such as commercial sex workers (CSW) and the general population (GP). In this study, the authors evaluated the rate of primary resistance mutations and the epidemiological link between isolates from GP and CSW from Bénin. METHODS: Plasma samples were obtained from 129 HIV-1-infected treatment-naïve individuals. Drug resistance mutations were identified using SDMR list and compared with other resistance algorithms. RESULTS: No nucleoside reverse transcriptase inhibitor resistance mutations were found. Four patients had non-nucleoside reverse transcriptase inhibitor resistance (K103N, G190A). One patient exhibited protease inhibitors resistance mutation, F53Y. Using the SDMR list, the authors obtained a rate of 3.9% of primary resistance. Nevertheless, the authors observed several mutations not on SDMR list but included in others resistance database, taking those mutations into account, the authors obtained a rate of 15.5%. CONCLUSIONS: Although our results show a low rate of SDMR, this algorithm may underestimate resistance mutations that may impact treatment options in developing countries. Primary resistance rates were similar in CSW and in the GP. Our phylogenetic analysis confirmed the genetic exchange between groups.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".