IL‐27 increases the proliferation and effector functions of human naïve CD8<sup>+</sup> T lymphocytes and promotes their development into Tc1 cells
Bibliographic record
Abstract
IL-27 has been shown to exhibit both pro- and anti-inflammatory properties; it favors mouse naïve CD4(+) T-cell differentiation into Th1 cells to the detriment of Th17 and Th2 skewing and regulates IL-10 and IL-17 production by human CD4(+) T cells. Moreover, IL-27 promotes proliferation and cytotoxic functions of mouse CD8(+) T lymphocytes, but no data are available on human CD8(+) T cells. We investigated the impact of IL-27 on human CD8(+) T cells. In contrast to mouse T cells, the IL-27 receptor (IL-27R), composed of T cell cytokine receptor (TCCR) and gp130, was detected on a greater percentage of human CD8(+) than CD4(+) T cells and these proportions increased upon polyclonal activation. IL-27 induced rapid STAT1 and STAT3 signaling, enhanced STAT1 protein levels, and induced SOCS1 and SOCS3 expression in a STAT1-dependent manner by human CD8(+) T cells. Addition of IL-27 to α-CD3-activated naïve CD8(+) T cells significantly increased T-box transcription factor expression levels, cell proliferation, and IFN-γ and granzyme B production leading to increased CD8(+) T-cell-mediated cytotoxicity. These results demonstrate that IL-27, a rapidly produced cytokine by activated APC, has a profound impact on human naïve CD8(+) T cells, driving them to become highly efficient Tc1 cells.
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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) | 0.003 | 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".