A Canadian Survey of Research on HIV-1 Latency—Where Are We Now and Where Are We Heading?
Bibliographic record
Abstract
Worldwide, almost 40 million people are currently living with HIV-1. The implementation of cART inhibits HIV-1 replication and reduces viremia but fails to eliminate HIV-1 from latently infected cells. These cells are considered viral reservoirs from which HIV-1 rebounds if cART is interrupted. Several efforts have been made to identify these cells and their niches. There has been little success in diminishing the pool of latently infected cells, underscoring the urgency to continue efforts to fully understand how HIV-1 establishes and maintains a latent state. Reactivating HIV-1 expression in these cells using latency-reversing agents (LRAs) has been successful, but only in vitro. This review aims to provide a broad view of HIV-1 latency, highlighting Canadian contributions toward these aims. We will summarize the research efforts conducted in Canadian labs to understand the establishment of latently infected cells and how this informs curative strategies, by reviewing how HIV latency is established, which cells are latently infected, what methodologies have been developed to characterize them, how new compounds are discovered and evaluated as potential LRAs, and what clinical trials aim to reverse latency in people living with HIV (PLWH).
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".