Prevalence of HIV type-1 drug-associated mutations in pre-therapy patients in the Free State, South Africa
Bibliographic record
Abstract
BACKGROUND: We aimed to characterize the molecular epidemiology of HIV type-1 (HIV-1) and the prevalence of drug-associated mutations prior to initiating highly active antiretroviral therapy (HAART) in the Free State province, South Africa. The Free State has a population of 3 million, an antenatal HIV prevalence of approximately 34% and a well established infrastucture for antiretroviral (ARV) provision. METHODS: HIV-1 polymerase genes were sequenced from 425 HAART-naive HIV-1-positive patients at voluntary primary healthcare HIV testing centres, who were subsequently attending district centres for assessment for commencing ARVs. Patients (>18 years) were sampled randomly with no exclusion for gender or clinical criteria. Sequences were analysed according to phylogeny and drug resistance. RESULTS: Phylogenetic clustering within the cohort was suggestive of multiple introductions of subtype C virus into the region. Drug resistance mutations (according to the International AIDS Society-USA classification) were distributed randomly across the cohort phylogeny with an overall prevalence of 2.3% in the sampled patients. When stratified according to CD4(+) T-cell count, the prevalence of resistance was 3.6%, 0.9% and 1.2% for CD4(+) T-cell counts <100, 200-350 and >500 cells/microl, respectively, and was most common for non-nucleoside reverse transcriptase inhibitor resistance (3.1% in patients with CD4(+) T-cell count <100 cells/microl). We surveyed all drug-selected mutations and found further significant clustering among patients with low CD4(+) T-cell counts (P=0.003), suggesting unrecognized exposure to ARVs. CONCLUSIONS: In the Free State population, there was a statistical association between low CD4(+) T-cell counts and drug-associated viral polymorphisms. Our data advocate the benefit of detailed history taking from patients starting HAART at low CD4(+) T-cell counts with close follow-up of the virological response.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".