Prolongation of Cardiac Allograft Survival by Endometrial Regenerative Cells: Focusing on B-Cell Responses
Bibliographic record
Abstract
Abstract Endometrial regenerative cells (ERCs) have been recently evaluated as an attractive candidate source for emerging stem cell therapies in immunosuppression, but their role in immunoregulation is not fully understood. The present study was designed to investigate their effects, especially on B-cell responses in heart transplantation. In this study, ERCs were noninvasively obtained from menstrual blood. Heart transplantation was performed between C57BL/6 (H-2b) donor mice and BALB/c (H-2d) recipients. B-cell activation and antibody levels were determined using fluorescence-activated cell sorting, enzyme-linked immunosorbent assay and ELISpot. In this study, we demonstrated that ERCs negatively regulated B-cell maturation and activation in vitro without affecting their viability. ERC treatment prolonged cardiac allograft survival in mice, which was correlated with a decrease in IgM and IgG deposition and circulating antidonor antibodies, as well as with reduction in frequencies of antidonor antibody-secreting CD19+ B cells. In addition, upon ex vivo stimulation, B cells from ERC-treated heart transplant recipients had impaired proliferation capacity and produced less IgM and IgG antibody. Moreover, ERC treatment of mice receiving ovalbumin (OVA)-aluminum hydroxide vaccine resulted in significant lower numbers of anti-OVA IgG antibody-secreting splenic B cells and lower anti-OVA antibody titres. Our results indicate that therapeutic effects of ERCs may be attributed at least in part by their B-cell suppression and humoral response inhibition, suggesting the potential use of ERCs for attenuating antibody-mediated allograft 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) | 0.001 | 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".