Platelet-Derived Extracellular Vesicles Promote Tenogenic Differentiation of Stem Cells on Bioengineered Living Fibers
Bibliographic record
Abstract
Tendon mimetic scaffolds that recreate the tendon hierarchical structure and niche have increasing potential to fully restore tendon functionality. However, most scaffolds lack biofunctionality to boost the tenogenic differentiation of stem cells. In this study, we assessed the role of platelet-derived extracellular vesicles (EVs) in stem cells' tenogenic commitment using a 3D bioengineered in vitro tendon model. First, we relied on fibrous scaffolds coated with collagen hydrogels encapsulating human adipose-derived stem cells (hASCs) to bioengineer our composite living fibers. We found that the hASCs in our fibers showed high elongation and cytoskeleton anisotropic organization, typical of tenocytes. Moreover, acting as biological cues, platelet-derived EVs boosted the hASCs' tenogenic commitment, prevented phenotypic drift, enhanced the deposition of the tendon-like extracellular matrix, and induced lower collagen matrix contraction. In conclusion, our living fibers provided an in vitro system for tendon tissue engineering, allowing us to study not only the tendon microenvironment but also the influence of biochemical cues on stem cell behavior. More importantly, we showed that platelet-derived EVs are a promising biochemical tool for tissue engineering and regenerative medicine applications that are worthy of further exploration, as paracrine signaling might potentiate tendon repair and regeneration.
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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) | 0.001 | 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".