An Analysis of Mesenchymal Stem Cell-Derived Extracellular Vesicles for Preclinical Use
Bibliographic record
Abstract
Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) can reduce inflammation, promote healing, and improve organ function, thereby providing a potential "cell-free" therapy. Prior to clinical translation, it is critical to synthesize existing evidence on preclinical methods and efficacy. To address these issues, we used gold standard systematic review methodology to consolidate information from all published animal studies investigating MSC-EVs as an intervention. A systematic search of MEDLINE and Embase identified 206 studies. Data were extracted in duplicate for methodology, experimental design, interventional traits, modifications, and outcomes. MSC-EVs were used to treat a variety of diseases and demonstrated benefits in 97% of studies. Adverse effects were reported in only three studies, two demonstrating tumor growth. A quarter of articles modified EVs to enhance efficacy, with 72% leading to markedly improved outcomes as compared to unmodified EVs. However, several key methodological concerns were evident. Only 60% of studies used nomenclature consistent with the size definitions of EVs. Ultracentrifugation (70%) and isolation kits (23%) were the most common isolation techniques with noted differences in yield and purity. EVs were inconsistently dosed by protein (68%) or particle concentration (16%). Two-thirds of studies administered xenogeneic EVs, suggesting immunocompatibility. Less than 25% of studies assessed EV biodistribution. Approaches for determining size, protein markers, and morphology were highly heterogeneous, with only 12 and 4 studies meeting the MISEV 2014 and 2018 recommendations, respectively. Knowledge gaps identified from this systematic review highlight important opportunities to improve preclinical design and methodology in the rapidly growing field of EV therapeutics.
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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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".