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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".