Peripheral human CD4+CD8+ T lymphocytes exhibit a memory phenotype and enhanced responses to IL-2, IL-7 and IL-15
Bibliographic record
Abstract
Abstract CD4+CD8+T lymphocytes account for 1–2% of circulating human T lymphocytes, but their frequency is augmented in several diseases. The phenotypic and functional properties of these T lymphocytes are still ill-defined. We performed anex vivocharacterization of CD4+CD8+T lymphocytes from the blood of healthy individuals. We observed that CD4+CD8+T lymphocytes exhibit several characteristics associated with memory T lymphocytes including the expression of chemokine receptors (e.g. CCR7, CXCR3, CCR6) and activation markers (e.g. CD57, CD95). Moreover, we showed that a greater proportion of CD4+CD8+T lymphocytes have an enhanced capacity to produce cytokines (IFNγ, TNFα, IL-2, IL-4, IL-17A) and lytic enzymes (perforin, granzyme B) compared to CD4+and/or CD8+T lymphocytes. Finally, we assessed the impact of three key cytokines in T cell biology on these cells. We observed that IL-2, IL-7 and IL-15 triggered STAT5 phosphorylation in a greater proportion of CD4+CD8+T lymphocytes compared to CD4 and CD8 counterparts. We demonstrate that CD4+CD8+T lymphocytes from healthy donors exhibit a phenotypic profile associated with memory T lymphocytes, an increased capacity to produce cytokines and lytic enzymes, and a higher proportion of cells responding to key cytokines implicated in T cell survival, homeostasis and activation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".