Retinoids: novel potential therapeutics in the pursuit of HIV-1 cure
Bibliographic record
Abstract
Human immunodeficiency virus (HIV) infection remains a global epidemic. While antiretroviral therapy (ART) suppresses viral replication, cessation of ART results in viral rebound necessitating lifelong treatment. This is a result of a reservoir of latently infected cells, resistant to clearance by ART and the major obstacle in curing HIV. HIV cure strategies have focused on reactivating this latent reservoir with latency reversal agents (LRAs) along with enhancement of anti-HIV immunity to eliminate reactivated HIV. Retinoic acid (RA) derivatives are promising therapeutics that may promote clearance HIV latent reservoir allowing for definitive cure. In addition to plausible mechanisms for depleting the latent reservoir with LRA activityviathe p300 acetyl transferase pathway, countering HIV-mediated suppression of RIG-I and IRF-3, and proposed induction of selective apoptosis of HIV-infected cellsviaRIG-I, RA may also limit HIV spread by augmenting cellular traffickingviaCCR7 and CCR9 and induce accumulation of high-affinity effector CD8+ T cells that aid immune clearance of HIV-infected cells. Furthermore, due to their specificity for HIV-infected cells, retinoids are attractive agents to form the basis of multidrug regimens. Altogether, retinoids have many compelling properties as potential novel therapeutics in the cure of HIV.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".