Stromal Cell-Derived Factor-1 Mediates Cardiac Allograft Tolerance Induced by Human Endometrial Regenerative Cell-Based Therapy
Bibliographic record
Abstract
Abstract Endometrial regenerative cells (ERCs) are mesenchymal-like stromal cells, and their therapeutic potential has been tested in the prevention of renal ischemic reperfusion injury, acute liver injury, ulcerative colitis, and immunosuppression. However, their potential in the induction of transplant tolerance has not been investigated. The present study was undertaken to investigate the efficacy of ERCs in inducing cardiac allograft tolerance and the function of stromal cell-derived factor-1 (SDF-1) in the ERC-mediated immunoregulation. The inhibitory efficacy of human ERCs in the presence or absence of rapamycin was examined in both mouse cardiac allograft models between BALB/c (H-2d) donors and C57BL/6 (H-2b) recipients and in vitro cocultured splenocytes. AMD3100 was used to inhibit the function of SDF-1. Intragraft antibody (IgG and IgM) deposition and immune cell (CD4+ and CD8+) infiltration were measured by immunohistochemical staining, and splenocyte phenotypes were determined by fluorescence-activated cell sorting analysis. The results showed that ERC-based therapy induced donor-specific allograft tolerance, and functionally inhibiting SDF-1 resulted in severe allograft rejection. The negative effects of inhibiting SDF-1 on allograft survival were correlated with increased levels of intragraft antibodies and infiltrating immune cells, and also with reduced levels of regulatory immune cells including MHC class IIlowCD86lowCD40lowdendritic cells, CD68+CD206+macrophages, CD4+CD25+Foxp3+T cells, and CD1dhighCD5highCD83lowIL-10highB cells both in vivo and in vitro. These data showed that human ERC-based therapy induces cardiac allograft tolerance in mice, which is associated with SDF-1 activity, suggesting that SDF-1 mediates the immunosuppression of ERC-based therapy for the induction of transplant tolerance.
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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".