Interleukin-7 promotes HIV persistence during antiretroviral therapy
Bibliographic record
Abstract
HIV persists in latently infected memory CD4(+) T cells during antiretroviral therapy (ART). When administered to HIV-infected subjects receiving suppressive ART, interleukin-7 (IL-7) increases the number of CD4(+) T cells by promoting their survival and proliferation. However, little is known about the impact of IL-7 on HIV persistence during ART. By isolating large numbers of CD4(+) T cells from HIV-infected subjects, we demonstrate that IL-7 enhances viral production in productively infected cells but does not disrupt viral latency in latently infected cells. When administered to virally suppressed subjects, IL-7 led to the rapid proliferation of memory CD4(+) T cells, which resulted in a 70% increase in the absolute number of circulating CD4(+) T cells harboring integrated HIV DNA 4 weeks after therapy. The genetic diversity of the viral reservoir increased transiently in the majority of the subjects studied before returning to baseline values. Altogether, our results indicate that IL-7 promotes the mechanisms of HIV persistence during ART by enhancing residual levels of viral production and inducing proliferation of latently infected cells, and suggest that IL-7 does not represent a suitable candidate therapeutic strategy for HIV eradication. This trial was registered at www.clinicaltrials.gov as #NCT00099671 (AIDS Clinical Trials Group protocol 5214).
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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 | 0.000 | 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 | 0.001 |
| 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".