Regulatory T-cell dysfunction and its implication for cell therapy
Bibliographic record
Abstract
Regulatory T cells (Tregs) are a subtype of CD4+ T cells that can mediate immune tolerance by a multitude of immunomodulatory mechanisms. Treg-based adoptive immunotherapy is currently being tested in multiple phases I and II clinical trials in transplantation and autoimmune diseases. We have learned from the work done on conventional T cells that distinct mechanistic states can define their dysfunctions, such as exhaustion, senescence, and anergy. All three can negatively impact the therapeutic effectiveness of T-cell-based therapies. However, whether Tregs are susceptible to such dysfunctional states is not well studied, and results are sometimes found to be controversial. In addition, Treg instability and loss of FOXP3 expression is another Treg-specific dysfunction that can decreasein their suppressive potential. A better understanding of Treg biology and pathological states will be needed to compare and interpret the results of the different clinical and preclinical trials. We will review herein Tregs' mechanisms of action, describe different T-cell dysfunction subtypes and how and if they apply to Tregs (exhaustion, senescence, anergy, and instability), and finally how this knowledge should be taken into consideration when designing and interpreting Treg adoptive immunotherapy trials.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".