Mesenchymal stromal cell extracellular vesicles as therapy for acute and chronic respiratory diseases: A meta‐analysis
Bibliographic record
Abstract
Preclinical studies suggest mesenchymal stromal cell extracellular vesicles (MSC-EVs) reduce inflammation and improve organ function in lung diseases; however, an objective analysis of all available data is needed prior to translation. Using rigorous meta-research methods, we determined the effectiveness of MSC-EVs for preclinical respiratory diseases and identified experimental conditions that may further refine this therapy. A systematic search of MEDLINE/Embase identified 1167 records. After screening, 52 articles were included for data extraction and evaluated for risk of bias and quality of reporting in study design. A random effects meta-analysis was conducted for acute lung injury (ALI; N = 23), bronchopulmonary dysplasia (BPD; N = 8) and pulmonary arterial hypertension (PAH; N = 7). Subgroup analyses identified EV methods/characteristics that may be associated with improved efficacy. Data is presented as standardized mean differences (SMD) or risk ratios (RR) with 95% confidence intervals (CI). For ALI, MSC-EVs markedly reduced lung injury (SMD -4.33, CI -5.73 to -2.92), vascular permeability (SMD -2.43, CI -3.05 to -1.82), and mortality (RR 0.39, CI 0.22 to 0.68). Small EVs were more consistently effective than large EVs whereas no differences were observed between tissue sources, immunocompatibility or isolation techniques. For BPD, alveolarization was improved by MSC-EVs (SMD -1.45, CI -2.08 to -0.82) with small EVs more consistently beneficial then small/large EVs. In PAH, right ventricular systolic pressure (SMD -4.16, CI -5.68 to -2.64) and hypertrophy (SMD -2.80, CI -3.68 to -1.91) were significantly attenuated by EVs. In BPD and PAH, EVs isolated by ultracentrifugation demonstrated therapeutic benefit whereas tangential flow filtration (N = 2) displayed minimal efficacy. Lastly, risk of bias and quality of reporting for experimental design were consistently unclear across all studies. Our findings demonstrate clear potential of MSC-EVs to be developed as therapy for acute and chronic lung diseases. However, greater transparency in research design and direct comparisons of isolation technique and EV subtypes are needed to generate robust evidence to guide clinical translation. Protocol Registration: PROSPERO CRD42020145334.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.056 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".