Graft-Specific Regulatory T Cells for Long-Lasting, Local Tolerance Induction
Bibliographic record
Abstract
BACKGROUND: Solid organ transplantation is hindered by immune-mediated chronic graft dysfunction and the side effects of immunosuppressive therapy. Regulatory T cells (Tregs) are crucial for modulating immune responses post-transplantation; however, the transfer of polyspecific Tregs alone is insufficient to induce allotolerance in rodent models. METHODS: To enhance the efficacy of adoptive Treg therapy, we investigated different immune interventions in the recipients. By utilizing an immunogenic skin transplant model and existing transplantation medicine reagents, we facilitated the clinical translation of our findings. Specifically, antigen-specific Tregs were used. RESULTS: Our study demonstrated that combining the available induction therapies with drug-induced T-cell proliferation due to lymphopenia effectively increased the Treg/T effector ratios. This results in significant Treg accumulation within the graft, leading to long-term tolerance after the transfer of antigen-specific Tregs. Importantly, all the animals achieved operational tolerance, which boosted the presence of adoptively transferred Tregs within the graft. CONCLUSIONS: This protocol offers a means to establish tolerance by utilizing antigen-specific Tregs. These results have promising implications for future trials involving adoptive Treg therapy in organ 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".