405.6: Ex vivo delivery of autologous regulatory T cells during normothermic machine perfusion in porcine kidney transplantation.
Bibliographic record
Abstract
Introduction: Kidney transplantation is the optimal treatment for end-stage kidney disease. However, ischemia-reperfusion injury (IRI) remains a significant challenge, particularly in marginal grafts, as it contributes to delayed graft function and antibody-mediated rejection. Regulatory T cells (Tregs), a subset of CD4+CD25+ T cells, play a central role in modulating immune responses and have been shown to ameliorate ischemic acute kidney injury. Method: Normothermic ex vivo kidney perfusion (NEVKP) is a promising platform for organ preservation and therapeutic intervention, allowing for targeted delivery of Tregs directly to the kidney. In this study, we isolated and expanded Tregs from porcine peripheral blood and administered them during NEVKP in a porcine autotransplantation model.Results: Tregs administered during perfusion showed potential to suppress local immune responses without systemic immunosuppression. Foxp3-positive cells increased in the graft tissue, while effector T cells were suppressed immediately post-transplant. Although histological changes were not statistically significant, there was a trend toward reduced tubular injury. These findings suggest that Tregs delivered during NEVKP could mitigate IRI and enhance graft preservation. Conclusion: This study provides the first evidence in a large animal model supporting the feasibility and potential of Tregs for localized immunomodulation during kidney transplantation, paving the way for future studies targeting rejection.
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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 | 0.000 |
| 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".