MMP‐2 sensitive, VEGF‐bearing bioactive hydrogels for promotion of vascular healing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We sought to develop bioactive hydrogels to facilitate arterial healing, e.g., after balloon angioplasty. Toward this end, we developed a new class of proteolytically sensitive, biologically active polyethylene glycol (PEG)-peptide hydrogels that can be formed in situ to temporarily protect the arterial injury from blood contact. Furthermore, we incorporated endothelial cell-specific biological signals with the goal of enhancing arterial reendothelialization. Here we demonstrate efficient endothelial cell anchorage and activation on PEG hydrogel matrices modified by conjugation with both the cell adhesive peptide motif RGD and an engineered variant of vascular endothelial growth factor (VEGF). By crosslinking peptide sequences for cleavage by MMP-2 into the polymer backbone, the hydrogels became sensitive to proteolytic degradation by cell-derived matrix metalloproteinases (MMPs). Analysis of molecular hallmarks associated with endothelial cell activation by VEGF-RGD hydrogel matrices revealed a 70% increase in production of the latent MMP-2 zymogen compared with PEG-peptide hydrogels lacking VEGF. By additional provision of transforming growth factor beta1 (TGF-beta1) within the PEG-peptide hydrogel, conversion of the latent MMP zymogen into its active form was demonstrated. As a result of MMP-2 activation, strongly enhanced hydrogel degradation by activated endothelial cells was observed. Our data illustrate the critical importance of growth factor activities for remodeling of synthetic biomaterials into native tissue, as it is desired in many applications of regenerative medicine. Functionalized PEG-peptide hydrogels could help restore the native vessel wall and improve the performance of angioplasty procedures.
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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 | 0.003 | 0.001 |
| 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 it