Double negative regulatory T cells in transplantation and autoimmunity: recent progress and future directions
Bibliographic record
Abstract
T lymphocytes bearing the αβ T cell receptor (TCR) but lacking CD4, CD8, and markers of natural killer (NK) cell differentiation are known as 'double-negative' (DN) T cells and have been described in both humans and rodent models. We and others have shown that DN T cells can act as regulatory T cells (Tregs) that are able to prevent allograft rejection, graft-versus-host disease, and autoimmune diabetes. In the last few years, new data have revealed evidence of DN Treg function in vivo in rodents and humans. Moreover, significant advances have been made in the mechanisms by which DN Tregs target antigen-specific T cells. One major limitation of the field is the lack of a specific marker that can be used to distinguish truly regulatory DN T cells (DN Tregs) from non-regulatory ones, and this is the central challenge in the coming years. Here, we review recent progress on the role of DN Tregs in transplantation and autoimmunity, and their mechanisms of action. We also provide some perspectives on how DN Tregs compare with Foxp3(+) Tregs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".