New antiretroviral inhibitors and HIV-1 drug resistance: more focus on 90% HIV-1 isolates?
Bibliographic record
Abstract
Combined HIV antiretroviral therapy (cART) has been effective except if drug resistance emerges. As cART has been rolled out in low-income countries, drug resistance has emerged at higher rates than observed in high income countries due to factors including initial use of these less tolerated cART regimens, intermittent disruptions in drug supply, and insufficient treatment monitoring. These socioeconomic factors impacting drug resistance are compounded by viral mechanistic differences by divergent HIV-1 non-B subtypes compared to HIV-1 subtype B that largely infects the high-income countries (just 10% of 37 million infected). This review compares the inhibition and resistance of diverse HIV-1 subtypes and strains to the various approved drugs as well as novel inhibitors in clinical trials. Initial sequence variations and differences in replicative fitness between HIV-1 subtypes pushes strains through different fitness landscapes to escape from drug selective pressure. The discussions here provide insight to patient care givers and policy makers on how best to use currently approved ART options and reduce the emergence of drug resistance in ∼33 million individuals infected with HIV-1 subtype A, C, D, G, and recombinants forms. Unfortunately, over 98% of the literature on cART resistance relates to HIV-1 subtype B.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".