Bibliographic record
Abstract
PURPOSE OF REVIEW: The application of regulatory T cell (Treg) therapy in organ transplantation is actively being pursued using unmodified, typically polyclonal cells. As the results of these ongoing clinical trials emerge, it is time to plan the next wave of clinical trials of Tregs. Here we will review a key strategy to improve Treg effectiveness and reduce side effects, namely increasing Treg specificity - both in terms of antigen recognition and localization to the allograft. RECENT FINDINGS: Study of chemokine signatures accompanying acute rejection has revealed several chemokines that could be targeted to increase Treg homing. For example, Tregs possessing a Th1-like phenotype and expressing CXCR3 are better able to migrate towards local inflammation. Allografts themselves can be modified to increase Treg-attracting chemokines and Tregs themselves can produce chemokines, facilitating local proximity to their targets of suppression. Finally, tailoring Treg antigen specificity by T-cell or chimeric antigen receptor engineering is another approach to increase the specificity of suppression and optimize localization. SUMMARY: Treg localization to the graft is important, but the important role of lymph node and germinal center homing cannot be overlooked. There is an opportunity to learn from advances made in cancer immunotherapy to optimize Treg therapy for transplantation.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".