Genetic Diversity, Compartmentalization, and Age of HIV Proviruses Persisting in CD4 <sup>+</sup> T Cell Subsets during Long-Term Combination Antiretroviral Therapy
Bibliographic record
Abstract
The main barrier to HIV cure is the ability of a genetically diverse pool of proviruses, integrated into the genomes of infected CD4 + T cells, to persist despite long-term suppressive combination antiretroviral therapy (cART). CD4 + T cells, however, constitute a heterogeneous population due to their maturation across a developmental continuum, and the genetic “landscapes” of latent proviruses archived within them remains incompletely understood. We applied phylogenetic techniques, largely novel to HIV persistence research, to reconstruct within-host HIV evolutionary history and characterize proviral diversity in CD4 + T cell subsets in five individuals on long-term cART. Participants varied widely in terms of proviral burden, genetic diversity, and age distribution between CD4 + T cell subsets, revealing that proviral landscapes can differ between individuals and between infected cell types within an individual. Our findings expose each within-host latent reservoir as unique in its genetic complexity and support personalized strategies for HIV eradication.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".