Cellular Activation, Differentiation, and Proliferation Influence the Dynamics of Genetically Intact Proviruses Over Time
Bibliographic record
Abstract
Human immunodeficiency virus (HIV) persists in cells despite antiretroviral therapy; however, the influence of cellular mechanisms such as activation, differentiation, and proliferation upon the distribution of proviruses over time is unclear. To address this, we used full-length sequencing to examine proviruses within memory CD4+ T-cell subsets longitudinally in 8 participants. Over time, the odds of identifying a provirus increased in effector and decreased in transitional memory cells. In all subsets, more activated (HLA-DR-expressing) cells contained a higher frequency of intact provirus, as did more differentiated cells such as transitional and effector memory subsets. The proportion of genetically identical proviruses increased over time, indicating that cellular proliferation was maintaining the persistent reservoir; however, the number of genetically identical proviral clusters in each subset was stable. As such, key biological processes of activation, differentiation, and proliferation influence the dynamics of the HIV reservoir and must be considered during the development of any immune intervention.
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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 | 0.003 |
| 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 | 0.001 | 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".