Extracellular vesicles from mesenchymal stromal cells as a promising therapy for ARDS: a systematic review of preclinical studies
Bibliographic record
Abstract
Introduction: Mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) have emerged as a promising cell-free therapeutic strategy for acute respiratory distress syndrome (ARDS), a condition with limited effective treatment options. Methods: This systematic review synthesizes findings from 51 in vivo preclinical studies investigating the efficacy, delivery methods, mechanisms of action, and optimization strategies of MSC-EV interventions in experimental ARDS. Results: Across diverse models and etiologies, MSC-EVs consistently attenuated inflammation, improved gas exchange, and enhanced survival. Mechanistically, these benefits were largely attributed to microRNA-mediated immunomodulation, including promotion of anti-inflammatory macrophage phenotypes and improved bacterial clearance. Factors influencing therapeutic efficacy included the MSC source, EV preconditioning, timing of administration, and route of delivery. Discussion: Despite these encouraging findings, critical methodological heterogeneity limits reproducibility and translational potential. This heterogeneity is particularly evident in dose metrics (e.g., particle number versus protein content), EV quantification methods (e.g., flow cytometry versus nanoparticle tracking analysis), and timing of outcome assessment. This review underscores the growing body of preclinical evidence supporting MSC-EVs in ARDS and identifies key knowledge gaps such as optimal dosing, safety profiling, and scalable manufacturing that must be addressed to enable clinical translation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".