Mesenchymal Stromal Cells Are More Effective Than Their Extracellular Vesicles at Reducing Lung Injury Regardless of Acute Respiratory Distress Syndrome Etiology
Bibliographic record
Abstract
Although mesenchymal stromal cells (MSCs) have demonstrated beneficial effects on experimental acute respiratory distress syndrome (ARDS), preconditioning may be required to potentiate their therapeutic effects. Additionally, administration of cell-free products, such as extracellular vesicles (EVs) obtained from MSC-conditioned media, might be as effective as MSCs. In this study, we comparatively evaluated the effects of MSCs, preconditioned or not with serum collected from mice with pulmonary or extrapulmonary ARDS (ARDSp and ARDSexp, respectively), and the EVs derived from these cells on lung inflammation and remodeling in ARDSp and ARDSexp mice. Administration of MSCs (preconditioned or not), but not their EVs, reduced static lung elastance, interstitial edema, and collagen fiber content in both ARDSp and ARDSexp. Although MSCs and EVs reduced alveolar collapse and neutrophil cell counts in lung tissue, therapeutic responses were superior in mice receiving MSCs, regardless of preconditioning. Despite higher total cell, macrophage, and neutrophil counts in bronchoalveolar lavage fluid in ARDSp than ARDSexp, MSCs and EVs (preconditioned or not) led to a similar decrease. In ARDSp, both MSCs and EVs, regardless of preconditioning, reduced levels of tumor necrosis factor- (TNF-) α, interleukin-6, keratinocyte chemoattractant (KC), vascular endothelial growth factor (VEGF), and transforming growth factor- (TGF-) β in lung homogenates. In ARDSexp, TNF-α, interleukin-6, and KC levels were reduced by MSCs and EVs, preconditioned or not; only MSCs reduced VEGF levels, while TGF-β levels were similarly increased in ARDSexp treated either with saline, MSCs, or EVs, regardless of preconditioning. In conclusion, MSCs yielded greater overall improvement in ARDS in comparison to EVs derived from the same number of cells and regardless of the preconditioning status. However, the effects of MSCs and EVs differed according to ARDS etiology.
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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 | 0.000 | 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 | 0.000 |
| 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".