CD4<sup>+</sup> T‐regulatory cells: toward therapy for human diseases
Bibliographic record
Abstract
SUMMARY: T-regulatory cells (Tregs) have a fundamental role in the establishment and maintenance of peripheral tolerance. There is now compelling evidence that deficits in the numbers and/or function of different types of Tregs can lead to autoimmunity, allergy, and graft rejection, whereas an over-abundance of Tregs can inhibit anti-tumor and anti-pathogen immunity. Experimental models in mice have demonstrated that manipulating the numbers and/or function of Tregs can decrease pathology in a wide range of contexts, including transplantation, autoimmunity, and cancer, and it is widely assumed that similar approaches will be possible in humans. Research into how Tregs can be manipulated therapeutically in humans is most advanced for two main types of CD4(+) Tregs: forkhead box protein 3 (FOXP3)(+) Tregs and interleukin-10-producing type 1 Tregs (Tr1 cells). The aim of this review is to highlight current information on the characteristics of human FOXP3(+) Tregs and Tr1 cells that make them an attractive therapeutic target. We discuss the progress and limitations that must be overcome to develop methods to enhance Tregs in vivo, expand or induce them in vitro for adoptive transfer, and/or inhibit their function in vivo. Although many technical and theoretical challenges remain, the next decade will see the first clinical trials testing whether Treg-based therapies are effective in humans.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".