Discarded Human Thymus Is a Novel Source of Stable and Long-Lived Therapeutic Regulatory T Cells
Bibliographic record
Abstract
Regulatory T cell (Treg)-based therapy is a promising approach to treat many immune-mediated disorders such as autoimmune diseases, organ transplant rejection, and graft-versus-host disease (GVHD). Challenges to successful clinical implementation of adoptive Treg therapy include difficulties isolating homogeneous cell populations and developing expansion protocols that result in adequate numbers of cells that remain stable, even under inflammatory conditions. We investigated the potential of discarded human thymuses, routinely removed during pediatric cardiac surgery, to be used as a novel source of therapeutic Tregs. Here, we show that large numbers of FOXP3(+) Tregs can be isolated and expanded from a single thymus. Expanded thymic Tregs had stable FOXP3 expression and long telomeres, and suppressed proliferation and cytokine production of activated allogeneic T cells in vitro. Moreover, expanded thymic Tregs delayed development of xenogeneic GVHD in vivo more effectively than expanded Tregs isolated based on CD25 expression from peripheral blood. Importantly, in contrast to expanded blood Tregs, expanded thymic Tregs remained stable under inflammatory conditions. Our results demonstrate that discarded pediatric thymuses are an excellent source of therapeutic Tregs, having the potential to overcome limitations currently hindering the use of Tregs derived from peripheral or cord blood.
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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) | 0.001 | 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".