Stem cell conditioned medium improves acute lung injury in mice: in vivo evidence for stem cell paracrine action
Bibliographic record
Abstract
Mortality and morbidity of acute lung injury and acute respiratory distress syndrome remain high because of the lack of pharmacological therapies to prevent injury or promote repair. Mesenchymal stem cells (MSCs) prevent lung injury in various experimental models, despite a low proportion of donor-derived cell engraftment, suggesting that MSCs exert their beneficial effects via paracrine mechanisms. We hypothesized that soluble factors secreted by MSCs promote the resolution of lung injury in part by modulating alveolar macrophage (AM) function. We tested the therapeutic effect of MSC-derived conditioned medium (CdM) compared with whole MSCs, lung fibroblasts, and fibroblast-CdM. Intratracheal MSCs and MSC-CdM significantly attenuated lipopolysaccharide (LPS)-induced lung neutrophil influx, lung edema, and lung injury as assessed by an established lung injury score. MSC-CdM increased arginase-1 activity and Ym1 expression in LPS-exposed AMs. In vivo, AMs from LPS-MSC and LPS-MSC CdM lungs had enhanced expression of Ym1 and decreased expression of inducible nitric oxide synthase compared with untreated LPS mice. This suggests that MSC-CdM promotes alternative macrophage activation to an M2 "healer" phenotype. Comparative multiplex analysis of MSC- and fibroblast-CdM demonstrated that MSC-CdM contained several factors that may confer therapeutic benefit, including insulin-like growth factor I (IGF-I). Recombinant IGF-I partially reproduced the lung protective effect of MSC-CdM. In summary, MSCs act through a paracrine activity. MSC-CdM promotes the resolution of LPS-induced lung injury by attenuating lung inflammation and promoting a wound healing/anti-inflammatory M2 macrophage phenotype in part via IGF-I.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".