Mesenchymal stem cells regulate airway contractile tissue remodeling in murine experimental asthma
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stem cells may offer therapeutic potential for asthma due to their immunomodulatory properties and host tolerability, yet prior evidence suggests that bloodborne progenitor cells may participate in airway remodeling. Here, we tested whether mesenchymal stem cells administered as anti-inflammatory therapy may favor airway remodeling and therefore be detrimental. METHODS: Adipose tissue-derived mesenchymal stem cells were retrovirally transduced to express green fluorescent protein and intravenously injected into mice with established experimental asthma induced by repeat intranasal house dust mite extract. Controls were house dust mite-instilled animals receiving intravenous vehicle or phosphate-buffered saline-instilled animals receiving mesenchymal stem cells. Data on lung function, airway inflammation, and remodeling were collected at 72 h after injection or after 2 weeks of additional intranasal challenge. RESULTS: The mesenchymal stem cells homed to the lungs and rapidly downregulated airway inflammation in association with raised T-helper-1 lung cytokines, but such effect declined under sustained allergen challenge despite a persistent presence of mesenchymal stem cells. Conversely, airway hyperresponsiveness and contractile tissue underwent a late reduction regardless of continuous pathogenic stimuli and inflammatory rebound. Tracking of green fluorescent protein did not show mesenchymal stem cell integration or differentiation in airway wall tissues. CONCLUSIONS: Therapeutic mesenchymal stem cell infusion in murine experimental asthma is free of unwanted pro-remodeling effects and ameliorates airway hyper-responsiveness and contractile tissue remodeling. These outcomes support furthering the development of mesenchymal stem cell-based asthma therapies, although caution and solid preclinical data building are warranted.
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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) | 0.000 | 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".