Harnessing Advances in T Regulatory Cell Biology for Cellular Therapy in Transplantation
Bibliographic record
Abstract
Cellular therapy with CD4FOXP3 T regulatory (Treg) cells is a promising strategy to induce tolerance after solid-organ transplantation or prevent graft-versus-host disease after transfer of hematopoietic stem cells. Treg cells currently used in clinical trials are either polyclonal, donor- or antigen-specific. Aside from variations in isolation and expansion protocols, however, most therapeutic Treg cell-based products are much alike. Ongoing basic science work has provided considerable new insight into multiple facets of Treg cell biology, including their stability, homing, and functional specialization; integrating these basic science discoveries with clinical efforts will support the development of next-generation therapeutic Treg cells with enhanced efficacy. In this review, we summarize recent advances in knowledge of how Treg cells home to lymphoid and peripheral tissues, and control antibody production and tissue repair. We also discuss newly appreciated pathways that modulate context-specific Treg cell function and stability. Strategies to improve and tailor Treg cells for cell therapy to induce transplantation tolerance are highlighted.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".