Effects of mutations in the connection and RNase H domains of HIV-1 reverse transcriptase on drug susceptibility.
Bibliographic record
Abstract
Despite the success in the development of antiretroviral therapy, the emergence of drug resistance remains an important factor that can undermine the benefits of treatment. The vast majority of well-described resistance-associated mutations are clustered around the binding site for a given inhibitor. However, mutations that are observed at considerable distance from this location can likewise affect drug susceptibility. Treatment-associated mutations in the C-terminal region of HIV-1 reverse transcriptase provide a recently surfaced example in this regard. In this review, we discuss the potential clinical significance of these mutations and underlying molecular mechanisms. Routine resistance testing does not usually include the C-terminal region of HIV-1 reverse transcriptase. However, previous studies have shown that mutations in this region can reduce susceptibility to both nucleoside and nonnucleoside reverse transcriptase inhibitors. The prevalence of some of these mutations can be as high as reported for several classic resistance mutations in HIV-1 reverse transcriptase. Biochemical studies provided plausible mechanisms that help to explain how certain C-terminal mutations can contribute to alterations in drug susceptibility and viral replication capacity. Overall, the available data warrant further investigation on the impact of C-terminal mutations in combination with classic resistance-associated mutations, on changes in viral load, and response to treatment with different classes of reverse transcriptase inhibitors.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".