Systematic review of extracellular vesicle‐based treatments for lung injury: are EVs a potential therapy for COVID‐19?
Bibliographic record
Abstract
Severe COVID-19 infection results in bilateral interstitial pneumonia, often leading to acute respiratory distress syndrome (ARDS) and pulmonary fibrosis in survivors. Most patients with severe COVID-19 infections who died had developed ARDS. Currently, ARDS is treated with supportive measures, but regenerative medicine approaches including extracellular vesicle (EV)-based therapies have shown promise. Herein, we aimed to analyse whether EV-based therapies could be effective in treating severe pulmonary conditions that affect COVID-19 patients and to understand their relevance for an eventual therapeutic application to human patients. Using a defined search strategy, we conducted a systematic review of the literature and found 39 articles (2014-2020) that reported effects of EVs, mainly derived from stem cells, in lung injury models (one large animal study, none in human). EV treatment resulted in: (1) attenuation of inflammation (reduction of pro-inflammatory cytokines and neutrophil infiltration, M2 macrophage polarization); (2) regeneration of alveolar epithelium (decreased apoptosis and stimulation of surfactant production); (3) repair of microvascular permeability (increased endothelial cell junction proteins); (4) prevention of fibrosis (reduced fibrin production). These effects were mediated by the release of EV cargo and identified factors including miRs-126, -30b-3p, -145, -27a-3p, syndecan-1, hepatocyte growth factor and angiopoietin-1. This review indicates that EV-based therapies hold great potential for COVID-19 related lung injuries as they target multiple pathways and enhance tissue regeneration. However, before translating EV therapies into human clinical trials, efforts should be directed at developing good manufacturing practice solutions for EVs and testing optimal dosage and administration route in large animal models.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".