Human endometrial regenerative cells attenuate renal ischemia reperfusion injury in mice
Bibliographic record
Abstract
BACKGROUND: Endometrial regenerative cells (ERCs) is an attractive novel type of adult mesenchymal stem cells that can be non-invasively obtained from menstrual blood and are easily replicated at a large scale without tumorigenesis. We have previously reported that ERCs exhibit unique immunoregulatory properties in experimental studies in vitro and in vivo. In this study, the protective effects of ERCs on renal ischemia-reperfusion injury (IRI) were examined. METHODS: Renal IRI in C57BL/6 mice was induced by clipping bilateral renal pedicles for 30 min, followed by reperfusion for 48 h. ERCs were isolated from healthy female menstrual blood, and were injected (1 million/mouse, i.v.) into mice 2 h prior to IRI induction. Renal function, pathological and immunohistological changes, cell populations and cytokine profiles were evaluated after 48 h of renal reperfusion. RESULTS: Here, we showed that as compared to untreated controls, administration of ERCs effectively prevented renal damage after IRI, indicated by better renal function and less pathological changes, which were associated with increased serum levels of IL-4, but decreased levels of TNF-α, IFN-γ and IL-6. Also, ERC-treated mice displayed significantly less splenic and renal CD4(+) and CD8(+) T cell populations, while the percentage of splenic CD4(+)CD25(+) regulatory T cells and infiltrating M2 macrophages in the kidneys were significantly increased in ERC-treated mice. CONCLUSIONS: This study demonstrates that the novel anti-inflammatory and immunoregulatory effects of ERCs are associated with attenuation of renal IRI, suggesting that the unique features of ERCs may make them a promising candidate for cell therapies in the treatment of ischemic acute kidney injury in patients.
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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 | 0.001 | 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 | 0.001 |
| 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".