HIV‐1 Reverse Transcriptase Connection Domain Mutations: Dynamics of Emergence and Implications for Success of Combination Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Factors promoting the emergence of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) connection domain mutations and their effect on antiretroviral therapy (ART) are still largely undetermined. We investigated this matter by analyzing genotypic resistance tests covering 400 amino acid positions in the RT of HIV-1 subtype B viruses and corresponding treatment histories and laboratory measurements. METHODS: The emergence of connection domain mutations was studied in 334 patients receiving monotherapy or dual therapy with thymidine analogues at the time of the genotypic resistance test. Response to subsequent combination ART (cART) was analyzed using Cox regression for 291 patients receiving unboosted protease inhibitors. Response was defined by ever reaching an HIV RNA level <50 copies/mL during the first cART. RESULTS: The connection domain mutations N348I, R356K, R358K, A360V, and A371V were more frequently observed in ART-exposed than ART-naive patients, of which only N348I and A360V were nonpolymorphic (with a prevalence of <1.5% in untreated patients). N348I correlated with M184V and predominantly occurred in patients receiving lamivudine and zidovudine concomitantly. A360V was not associated with specific drug combinations and was found to emerge later than M184V or thymidine analogue mutations. Nonpolymorphic connection domain mutations were rarely detected in the absence of established drug resistance mutations in ART-exposed individuals (prevalence, <1%). None of the 5 connection domain mutations associated with treatment showed a statistically significant effect on response to cART. CONCLUSIONS: Despite their frequent emergence, connection domain mutations did not show large detrimental effects on response to cART. Currently, routine implementation of connection domain sequencing seems unnecessary for developed health care settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".