HUMAN REGULATORY T CELLS PREVENT ISLET ALLOGRAFT REJECTION
Bibliographic record
Abstract
Background: Type 1 diabetes mellitus represents a significant burden on global healthcare. Pancreatic islet transplantation offers an effective means of controlling the disease, but shortage of donor tissue, graft thrombosis, and immunological rejection after transplantation remain obstacles that need to be overcome. Our aim was to assess the ability of ex vivo expanded human regulatory T cells (Treg) in modulating the rejection response against a human islet allograft in a clinically relevant model of human pancreatic islet transplantation. Methods: We studied the rejection response against allogeneic human islets in acohort of 32 immunodeficient mice which had been reconstituted with a functional human immune system. Thirteen subjects were transplanted with human islets without further immunological modification; graft survival was compared with that of thirteen subjects treated additionally with human regulatory T cells. Six controls were given a human islet transplant, but not reconstituted with human immune cells to demonstrate the functionality of the islet graft in the absence of immunological rejection. Graft function was assessed with serial blood glucose measurements, immunohistochemistry,immunoflourescence, and flow cytometry. Findings: Human islet allografts were rapidly rejected in subjects that did notreceive Treg. With Treg treatment, however, human islet allograft rejection was prevented (median survival time (MST) of > 45 days with Treg, as opposed to an MST of 23 days without Treg). Ex vivo expanded Treg homed to the lymphoid tissue draining the graft site where they suppressed the priming, activation, proliferation, and effector cytokine production of alloreactive T cells. Interpretation: These findings in a clinically relevant model of human pancreatic islet transplantation demonstrate the ability of ex vivo expanded human Treg to attenuate acute islet allograft rejection, and provide further support for their use in cellular immunotherapy.
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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".