IL-7 Receptor Expression Levels Do Not Identify CD8+ Memory T Lymphocyte Precursors following Peptide Immunization
Bibliographic record
Abstract
Identification of the mechanisms underlying the survival of effector T cells and their differentiation into memory T lymphocytes are critically important to understanding memory development. Because cytokines regulate proliferation, differentiation, and survival of T lymphocytes, we hypothesized that cytokine signaling dictates the fate of effector T cells. To follow cytokine receptor expression during T cell responses, we transferred murine TCR transgenic T cells into naive recipients followed by immunization with peptide emulsified in adjuvant or pulsed on dendritic cells. Our findings did not correlate IL-7R alpha-chain and IL-2R beta-chain expression on effector CD8+ cells with the generation of memory T lymphocytes. However, we could correlate the extent of IL-7R alpha expression down-regulation on effector T cells with the level of inflammation generated by the immunization. Furthermore, our findings showed that the maintenance of a high level of IL-7R expression by effector T cells at the peak of the response does not preclude their death. This suggests that maintenance of IL-7R expression is not sufficient to prevent T cell contraction. Thus, our results indicate that expression of the IL-7R is not always a good marker for identifying precursors of memory T cells among effectors and that selective expression of the IL-7R by effector T cells should not be used to predict the success of vaccination.
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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 | 0.001 |
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".