Nebulised mesenchymal stem cell derived extracellular vesicles ameliorate E. coli induced pneumonia in a rodent model
Bibliographic record
Abstract
Abstract Background Mesenchymal stem cell (MSC) derived extracellular vesicles (EVs) have been proposed as an alternative to cell therapy, creating new possible delivery modalities such as nebulisation. We wished to investigate the therapeutic potential of directly nebulised MSC-EVs in the mitigation of Escherichia coli-induced pneumonia. Methods EV size, surface markers and miRNA content were assessed pre- and post-nebulisation. BEAS2B and A459 lung cells were exposed to lipopolysaccharide (LPS) and treated with nebulised bone marrow (BM) or umbilical cord (UC) MSC-EVs. Viability assays (MTT) and inflammatory cytokine assays were performed. THP-1 monocytes were stimulated with LPS and nebulised BM- or UC-EVs and phagocytosis activity was measured. For in vivo experiments, mice received LPS intratracheally (IT) followed by BM- or UC-EVs intravenously (IV) and injury markers assessed at 24 h. Rats were instilled with E. coli bacteria IT and BM- or UC-EVs delivered IV or by direct nebulisation. At 48 h, lung damage was assessed by physiological parameters, histology and inflammatory marker presence. Results MSC-EVs retained their immunomodulatory and wound healing capacity after nebulisation in vitro. EV integrity and content were also preserved. Therapy with IV or nebulised MSC-EVs reduced the severity of LPS-induced lung injury and E. coli-induced pneumonia by reducing bacterial load and oedema, increasing blood oxygenation and improving lung histological scores. MSC-EV treated animals also showed lower levels of inflammatory cytokines and inflammatory-related markers. Conclusions MSC-EVs given IV attenuated LPS-induced lung injury, and nebulisation of MSC-EVs did not affect their capacity to attenuate lung injury caused by E. coli pneumonia, as evidenced by reduction in bacterial load and improved lung physiology.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".