Resistance and viral subtypes: how important are the differences and why do they occur?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The global HIV-1 pandemic has evolved to include 11 subtypes and 34 circulating recombinant forms. Our knowledge of HIV-1 response to antiretroviral drugs and emergent drug resistance has, however, been limited to subtype B infections circulating in Europe and North America, with little comparative information on non-B subtypes representing approximately 90% of worldwide epidemics. This review summarizes publications in the past year that highlight intersubtype differences influencing viral susceptibility to antiretroviral drugs and emergent drug resistance. RECENT FINDINGS: Cumulative findings from clinical studies suggest that antiretroviral therapy will be of benefit in the overall treatment of non-B subtype infections, and result in drug-resistance profiles comparable to those observed for subtype B infections. Nevertheless, the 10-15% sequence diversity in the Pol region contributes to intersubtype differences in response to particular nucleoside and non-nucleoside analogues, as well as protease inhibitors. Distinct signature mutations and mutational pathways are identified for specific non-B subtypes. The implications of subtype on clinical outcome and interpretative algorithms are described. SUMMARY: Understanding intersubtype differences in drug resistance is important in optimizing treatment strategies in resource-poor settings. Hopefully, this may assist in the design of prophylactic approaches to prevent HIV-1 horizontal and vertical HIV-1 transmission.
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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 | 0.000 |
| 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) | 0.000 | 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".