Interleukin‐7 enhances memory CD8<sup>+</sup> T‐cell recall responses in health but its activity is impaired in human immunodeficiency virus infection
Bibliographic record
Abstract
Memory CD8(+) T cells regain function during a recall response, but the requirement of signals in addition to antigen during a secondary immune response is unknown. In this study, the ability of interleukin-7 (IL-7) to enhance memory CD8(+ ) CD45RA(- ) CD127(+) T-cell responses in health and in human immunodeficiency virus (HIV) infection was investigated. CD8(+) T-cell-depleted peripheral blood mononuclear cells (PBMCs) from HIV(-) and untreated HIV(+) donors were pulsed with a cytomegalovirus/Epstein-Barr virus/influenza (CEF) peptide pool, and co-cultured with autologous memory CD8(+) T cells in the presence of IL-7. Cell survival and the function of memory CD8(+) T-cell subsets were then evaluated. Memory CD8(+) T-cell proliferation and interferon-γ (IFN-γ) production was enhanced by the presence of antigen, and the addition of IL-7 further enhanced antigen-induced proliferation. In HIV(+) individuals, the presence of antigen enhanced IFN-γ production to a small degree but did not enhance proliferation. Lastly, IL-7 did not enhance antigen-mediated proliferation of memory CD8(+) T cells from HIV(+) individuals. IL-7 therefore appears to have a role in secondary immune responses and its activity is impaired in memory CD8(+) T cells from HIV(+) individuals. These results further our understanding of the signals involved in secondary immune responses, and provide new insight into the loss of CD8(+) T-cell function in HIV infection.
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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 | 0.000 |
| 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 | 0.001 |
| 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".