Interleukin‐10‐secreting type 1 regulatory T cells in rodents and humans
Bibliographic record
Abstract
Interleukin-10 (IL-10)-secreting T regulatory type 1 (Tr1) cells are defined by their specific cytokine production profile, which includes the secretion of high levels of IL-10 and transforming growth factor-beta(TGF-beta), and by their ability to suppress antigen-specific effector T-cell responses via a cytokine-dependent mechanism. In contrast to the naturally occurring CD4+ CD25+ T regulatory cells (Tregs) that emerge directly from the thymus, Tr1 cells are induced by antigen stimulation via an IL-10-dependent process in vitro and in vivo. Specialized IL-10-producing dendritic cells, such as those in an immature state or those modulated by tolerogenic stimuli, play a key role in this process. We propose to use the term Tr1 cells for all IL-10-producing T-cell populations that are induced by IL-10 and have regulatory activity. The full biological characterization of Tr1 cells has been hampered by the difficulty in generating these cells in vitro and by the lack of specific marker molecules. However, it is clear that Tr1 cells play a key role in regulating adaptive immune responses both in mice and in humans. Further work to delineate the specific molecular signature of Tr1 cells, to determine their relationship with CD4+ CD25+ Tregs, and to elucidate their respective role in maintaining peripheral tolerance is crucial to advance our knowledge on this Treg subset. Furthermore, results from clinical protocols using Tr1 cells to modulate immune responses in vivo in autoimmunity, transplantation, and chronic inflammatory diseases will undoubtedly prove the biological relevance of these cells in immunotolerance.
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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 | 0.001 | 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 | 0.001 |
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".