HIV Proviral Burden, Genetic Diversity, and Dynamics in Viremic Controllers Who Subsequently Initiated Suppressive Antiretroviral Therapy
Bibliographic record
Abstract
HIV therapy is lifelong because integrated, replication-competent viral copies persist within long-lived cells. To cure HIV, we need to understand when these viral reservoirs form, how large and genetically diverse they are, and how long they endure. Elite controllers-individuals who naturally suppress HIV to undetectable levels-are being intensely studied as models of HIV remission, but viremic controllers, individuals who naturally suppress HIV to low levels, remain understudied even though they too may hold valuable insights. We combined phylogenetics and mathematical modeling to reconstruct proviral seeding and decay from infection to therapy-mediated suppression in four viremic controllers. We recovered diverse proviruses persisting during therapy that broadly reflected HIV's within-host evolutionary history, where the estimated half-lives of the persistent proviral pool during untreated infection ranged from <1 year to negligible. Cure strategies will need to contend with proviral diversity and between-host heterogeneity, even in individuals who naturally control HIV.
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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".