Human Endometrial Regenerative Cells Attenuate Bleomycin-Induced Pulmonary Fibrosis in Mice
Bibliographic record
Abstract
Endometrial regenerative cells (ERCs) have been recently evaluated as an attractive novel type of stem cell therapy. Previous studies have demonstrated that most ERCs accumulated in the lung after injection and are successfully used to treat diseases such as cardiac fibrosis. However, relevant studies of ERCs in idiopathic pulmonary fibrosis (IPF) have not been reported. The present study was designed to examine the effects of ERCs on bleomycin-induced pulmonary fibrosis. All IPF models in C57BL/6 mice were induced by administrating 5 mg/kg bleomycin in PBS intratracheally. ERCs were isolated from healthy female menstrual blood and were injected (1 million/mouse, i.v.) 24 hours after induction. Wet/dry weight ratio assay, hydroxyproline content, pathological and immunohistological changes, MDA content, T-SOD activity, cytokine profiles, and RT-qPCR analysis were assessed 2 weeks after disease induction. The results showed that ERC treatment significantly decreased the wet/dry ratio and reduced collagen deposition. Histological analyses, Masson staining, and hydroxyproline content analysis indicated that ERCs could reduce collagen fiber production. Immunohistochemical staining revealed lower expression of TGF-βafter ERC treatment. Furthermore, mice treated with ERCs had lower levels of IL-1βand TNF-α, but a higher level of IL-10 in both the lung and serum. Gene expression analysis demonstrated that ERCs potently suppressed the proapoptotic gene Bax, while increasing the antiapoptotic gene Bcl-2 and antifibrosis genes HGF and MMP-9. Our results indicate that human ERCs protected the lung from pulmonary fibrosis in mice through immunosuppressive and antifibrosis effects. Moreover, these findings formed a foundation for the further use of ERCs in clinical treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".