Mesenchymal stromal cell-derived extracellular vesicles for regenerative therapy and immune modulation: Progress and challenges toward clinical application
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) have emerged as a promising form of regenerative therapy and immune modulation. Fundamental advances in our understanding of MSCs and EVs have allowed these fields to merge and create potential cell-free therapy options that are cell-based. EVs contain active cargo including proteins, microRNA, and mRNA species that can impact signaling responses in target cells to modify inflammatory and repair responses. Increasing numbers of preclinical studies in animals with various types of injury models have been published that demonstrate the potential impact of MSC-EV therapy. Although the emergence of registered clinical protocols suggests translation to clinical application has already begun, several barriers to more widespread clinical adoption remain. In this review, we highlight the progress made in MSC-derived small EV-based therapy by summarizing aspects pertaining to the starting material for MSC expansion, EV production, and isolation methods, studies from preclinical models that have established a foundation of knowledge to support translation into the patient setting, and potential barriers to overcome on the path to clinical application. Significance statement Mesenchymal stromal cell-derived extracellular vesicles are a promising cell-free therapy for regenerative medicine and immune modulation with growing evidence from preclinical animal studies. Bioactive cargo in extracellular vesicles, including proteins, microRNA, and mRNA species, can impact signaling responses in target cells to modify inflammatory and repair responses. Although translation to clinical application has already begun, several barriers to more widespread clinical adoption remain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".