Naturally occurring protease inhibitor resistance mutations and their frequencies in HIV proviral sequences of drug-naïve sex workers in Nairobi, Kenya
Bibliographic record
Abstract
Sub-Saharan Africa accounts for 69% of the people living with HIV globally. An estimated 1,600,000 Kenyans are living with HIV-1. Antiretroviral therapy (ART) has saved 9 million life-years in Sub-Saharan Africa. However, drug resistance mutations reduce the effectiveness of ART, and need to be monitored for effective ART. Naturally occurring primary antiretroviral drug resistance mutations have not been well analyzed in ART nave HIV+ patients in Kenya. Here we have examined protease inhibitor (PI) resistance mutations in ART nave HIV-1 seropositive women in Pumwani sex worker cohort established in Nairobi, Kenya, wherein HIV-1 infection is predominantly caused by subtypes A and D viruses. We have analyzed consensus sequences of HIV protease from 109 drugnave patients, as a part of HIV-1 whole-genome sequencing using 454 sequencing methodology. Analysis using HIVdb program revealed a prevalence of 22% (24/109) PI resistance mutations among the study subjects. D30N (3.7%), M46I (0.9%) and V82F (0.9%) are the major mutations observed. D30N mutation is known to confer high-level resistance to nelfinavir. M46I and V82F confer resistance to indinavir, lopinavir, fosamprenavir and nelfinavir. In addition, many minor mutations were found at seven different drug resistance sites. It is important to study the implications of these mutations to the effectiveness of specific PI drug treatment. This study provides valuable data pertaining to primary drug resistance in Kenyan HIV-1 infected patients before ART became available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".