Mesenchymal stromal cells reduce inflammation and improve lung function in a mouse model of cystic fibrosis lung disease
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are multipotent adult stem cells which possess immunomodulatory and repair capabilities. In this study, we investigated whether MSC therapy could modulate inflammation and lung damage in the lungs of Scnn1b-transgenic mice overexpressing the β-subunit of the epithelial sodium channel (β-ENaC), a model with features of Cystic Fibrosis lung disease. Human bone marrow derived MSC cells were intravenously delivered to mice, prior to collection of bronchoalveolar lavage (BALF) and tissue. BALF analysis revealed a significant reduction in inflammatory cells after MSC administration, with both monocytic cells and neutrophils significantly reduced. Pro-inflammatory cytokines keratinocyte-derived chemokine (KC) and osteopontin were also significantly reduced. Histological tissue analysis revealed a reduction in emphysema in Scnn1b-TG mice treated with MSCs and consistent with these findings, improvements in lung function after MSC therapy were observed. Furthermore, MSCs enhanced Ki67 staining in alveolar cells, which may indicate regeneration of the destroyed parenchyma. Mechanistically, restoration of peroxisome proliferator-activated receptor-γ (PPARγ) expression and its transcriptional program were identified after MSC treatment. Our data demonstrate that MSC therapy can reduce inflammation, damage, and lung function decline in the chronically inflamed lung of Scnn1b-Tg mice, suggesting that MSCs may provide an effective tool in the treatment of muco-obstructive diseases such as cystic fibrosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".