The Role of Cytokines which Signal through the Common γ Chain Cytokine Receptor in the Reversal of HIV Specific CD4+ and CD8+ T Cell Anergy
Bibliographic record
Abstract
BACKGROUND: HIV specific T cells are putatively anergic in vivo. IL-2, a member of a class of cytokines that binds to receptors containing the common gamma chain (gammac) has been shown to reverse anergy. We examined the role of gammac cytokines in reversing HIV specific T cell anergy. METHODS: PBMC from untreated HIV-infected individuals were briefly exposed to a panel of gammac cytokines, and frequencies of gag specific T cells were enumerated by intracellular IFN-gamma flow cytometry. RESULTS: Of the gammac cytokines, brief exposure to IL-2, IL-15, or combined IL-15/IL-7 significantly enhanced (range 2-7 fold) the CD4(+) and CD8(+) T cell IFN-gamma responses to HIV gag, with IL-15 giving the greatest enhancement. The effects of cytokines were not due to enhanced proliferation of pre-existing antigen specific cells, but were due to a combination of enhanced cytokine production from antigen specific T cells plus activation of non-epitope specific T cells. CONCLUSIONS: These observations support the notion that a significant number of HIV specific T cells are circulating in an anergic state. IL-2, IL-7 and particularly IL-15 as an immune modulator to reverse HIV-1 specific T cell anergy should be investigated, with the caveat that non-specific activation of T cells may also be induced.
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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".