Ultrafiltration Segregates Tissue Regenerative Stimuli Harboured Within and Independent of Extracellular Vesicles
Bibliographic record
Abstract
Abstract The release of extracellular vesicles (EVs) from human multipotent stromal cells (MSC) has been proposed as a mechanism by which MSC mediate regenerative functions in vivo . Our recent work has characterized MSC derived from human pancreatic tissues (Panc-MSC) that generated a tissue regenerative secretome. Despite these advancements, it remains unknown whether regenerative stimuli are released independent or within extracellular vesicles. Herein, this study demonstrates ultrafiltration is a simple method to enrich for EVs which can be injected in murine models of tissue regeneration. The enrichment of EVs from Panc-MSC conditioned media (CM) was validated using nanoscale flow cytometry and atomic force microscopy; in addition to the exclusive detection of classical EV-markers CD9, CD81, CD63 using label-free mass spectrometry. Additionally, we identified several pro-regenerative stimuli, such as WNT5A or ANGPT1, exclusive to EV-enriched CM. Endothelial cell tubule formation was enhanced in response to both Panc-MSC CM fractions in vitro yet only intramuscular injection of EV-enriched CM demonstrated vascular regenerative functions in NOD/SCID mice with unilateral hind-limb ischemia (*
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".