High Level of Pre-Treatment HIV-1 Drug Resistance and Its Association with HLA Class I-Mediated Restriction in the Pumwani Sex Worker Cohort
Bibliographic record
Abstract
Background: We analyzed the prevalence of pre-antiretroviral therapy (ART) drug resistance mutations (DRMs) in a Kenyan population. We also examined whether host HLA class I genes influence the development of pre-ART DRMs. Methods: The HIV-1 proviral DNAs were amplified from blood samples of 266 ART-naïve women from the Pumwani Sex Worker cohort of Nairobi, Kenya using a nested PCR method. The amplified HIV genomes were sequenced using next-generation sequencing technology. The prevalence of pre-ART DRMs was investigated. Correlation studies were performed between HLA class I alleles and HIV-1 DRMs. Results: Ninety-eight percent of participants had at least one DRM, while 38% had at least one WHO surveillance DRM. M184I was the most prevalent clinically important variant, seen in 37% of participants. The DRMs conferring resistance to one or more integrase strand transfer inhibitors were also found in up to 10% of participants. Eighteen potentially relevant (p < 0.05) positive correlations were found between HLA class 1 alleles and HIV drug-resistant variants. Conclusions: High levels of HIV drug resistance were found in all classes of antiretroviral drugs included in the current first-line ART regimens in Africa. The development of DRMs may be influenced by host HLA class I-restricted immunity.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".