Infusion of Mesenchymal Stem Cells and Rapamycin Synergize to Attenuate Alloimmune Responses and Promote Cardiac Allograft Tolerance
Bibliographic record
Abstract
The inherent immunosuppressive properties and low immunogenicity of mesenchymal stems cells (MSCs) suggested their therapeutic potential in transplantation. We investigated whether MSCs could prolong allograft survival. Treatment involving infusion of MSCs into BALB/c recipients 24 hours after receiving a heart allograft from a C57BL/6 donor significantly abated rejection and doubled graft mean survival time compared to untreated recipients. Furthermore, combination therapy of MSCs and low-dose Rapamycin (Rapa) achieved long-term heart graft survival (>100 days) with normal histology. The treated recipients readily accepted donor skin grafts but rejected third-party skin grafts, indicating the establishment of tolerance. Tolerant recipients exhibited neither intragraft nor circulating antidonor antibodies, but demonstrated significantly high frequencies of both tolerogenic dendritic cells (Tol-DCs) and CD4(+)CD25(+)Foxp3(+)T cells in the spleens. Infusion of GFP(+)C57BL/6-MSCs in combination with Rapa revealed that the GFP-MSCs accumulated in the lymphoid organs and grafts of tolerant recipients. Thus, engraftment of infused MSCs within the recipient's lymphoid organs and allograft appeared to be instrumental in the induction of allograft-specific tolerance when administered in combination with a subtherapeutic dose of Rapamycin. This study supports the clinical applicability of MSCs in 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".