IL-4 influences the differentiation and the susceptibility to activation-induced cell death of human naive CD8+ T cells
Bibliographic record
Abstract
It is now well established that the cytokine environment influences the activation, differentiation, proliferation and death of T lymphocytes during the primary response to antigen. Using an in vitro model, we investigated the influence of IL-4, added at the onset of TCR stimulation, on phenotypic and functional markers of naive CD8+ T cell activation including the up-regulation of activation markers, proliferation as well as the susceptibility to activation-induced cell death (AICD). We report that IL-4, unlike IL-2 added at the onset of repeated TCR stimulation of naive CD8+ T cells prevents AICD, in part due to its ability to maintain the level of the survival-related protein Bcl-2. Moreover, TCR-triggered activation of naive CD8+ T cells in the presence of IL-4 leads to the development of a CD8+ T cell subset that proliferates normally, but which fails to exhibit characteristic activation parameters such as the up-regulation of CD25 and Granzyme B. Taken together, these results demonstrate that exposure to IL-4 during primary activation influences CD8+ T cell differentiation by inducing the development of a sub-population of AICD-resistant, proliferation-competent cells that do not show some of the typical features of CD8+ T cell activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".