Functionalization of Electrospun Polycaprolactone Scaffolds with Matrix-Binding Osteocyte-Derived Extracellular Vesicles Promotes Osteoblastic Differentiation and Mineralization
Bibliographic record
Abstract
Synthetic polymeric materials have demonstrated great promise for bone tissue engineering based on their compatibility with a wide array of scaffold-manufacturing techniques, but are limited in terms of the bioactivity when compared to naturally occurring materials. To enhance the regenerative properties of these materials, they are commonly functionalised with bioactive factors to guide growth within the developing tissue. Extracellular matrix vesicles (EVs) play an important role in facilitating endochondral ossification during long bone development and have recently emerged as important mediators of cell-cell communication coordinating bone regeneration, and thus represent an ideal target to enhance the regenerative properties of synthetic scaffolds. Therefore, in this paper we developed tools and protocols to enable the attachment of MLO-Y4 osteocyte-derived EVs onto electrospun polycaprolactone (PCL) scaffolds for bone repair. Initially, we optimize a method for the functionalization of PCL materials with collagen type-1 and fibronectin, inspired by the behaviour of matrix vesicles during endochondral ossification, and demonstrate that this is an effective method for the adhesion of EVs to the material surface. We then used this functionalization process to attach osteogenic EVs, collected from mechanically stimulated MLO-Y4 osteocytes, to collagen-coated electrospun PCL scaffolds. The EV-functionalized scaffold promoted osteogenic differentiation (measured by increased ALP activity) and mineralization of the matrix. In particular, EV-functionalised scaffolds exhibited significant increases in matrix mineralization particularly at earlier time points compared to uncoated and collagen-coated controls. This approach to matrix-based adhesion of EVs provides a mechanism for incorporating vesicle signalling into polyester scaffolds and demonstrates the potential of osteocyte derived EVs to enhance the rate of bone tissue regeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".