REGULATORY CD4-CD8- T CELLS PREVENT AUTOIMMUNE DIABETES (143.4)
Bibliographic record
Abstract
Abstract Regulatory T cells show great potential for use in cellular therapy. In particular, CD4-CD8- (DN) T cells, which compose 1 to 3% of total T lymphocytes, exhibit prominent antigen-specific immune tolerance properties in models of allografts and xenografts, as well as in an induced model of autoimmune diabetes. Here, we evaluate the immunoregulatory properties of DN T cells in the autoimmune-prone NOD genetic background. Using the 3A9 TCR transgenic mice, we demonstrate that DN T cells from both autoimmune-resistant and -prone mice are equally effective at eliminating potentially autoreactive B cells in vitro. However, autoimmune-prone mice carry at least three fold fewer DN T cells than autoimmune-resistant mice, in both TCR transgenic and non-transgenic setting. Interestingly, a single transfer of DN T cells is sufficient to prevent autoimmune diabetes onset in autoimmune-prone mice. These results suggest that increasing DN T cell number is sufficient to confer protection from autoimmune diabetes onset. Further functional and genetic characterization of DN T cells in NOD mice highlighted a potential role for both CD172a and IL-10 in the regulation of DN T cell homeostasis. Importantly, genetic polymorphisms in both CD172a and IL-10 are associated with diabetes susceptibility in humans. Taken together, our results strongly suggest a role for DN T cell in peripheral tolerance and the association of genetic defects in DN T cell homeostasis with the development of diabetes.
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 | 0.001 |
| 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".