The role and mechanisms of double negative regulatory T cells in the suppression of immune responses.
Bibliographic record
Abstract
Accumulating evidence has demonstrated that regulatory T (Treg) cells play an important role in the maintenance of immunologic self-tolerance and in down-regulating various immune responses. Thus, there has recently been an increasing interest in studying the biology of Treg cells as well as their potential application in treating immune diseases. Many types of Treg cell subsets have been reported in a variety of disease models. Among these subsets, alpha beta TCR(+)CD3(+)CD4(-)CD8(-) double negative (DN) Treg cells are defined by their capability of inhibiting immune responses via directly killing effector T cells in an antigen specific fashion. Furthermore, DN Treg cells have been shown to develop regulatory activity after encountering specific antigens, partially mediated by the acquisition of MHC-peptide complexes from antigen presenting cells (APCs). The presentation of acquired alloantigens on DN T cells allows for the specific interaction between DN Treg cells and alloantigen reactive effector T cells. Once the DN Treg and target cells have come into contact, killing is then mediated by Fas/Fas-ligand interactions, and perhaps through other unidentified pathways. Further characterization of the functions, molecular expression and mechanisms of activation of DN Treg cells will help in the development of novel therapies to induce antigen specific tolerance to self and foreign antigens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".