Bioactive micropatterning of biomaterials for induction of endothelial progenitor cell differentiation: Acceleration of in situ endothelialization
Bibliographic record
Abstract
Synthetic grafts do not provide an appealing surface for endothelial cells to adhere and colonize the inner surface. To promote in situ endothelialization the following aspect has to be taken into account, endothelial progenitor cells (EPCs) needs to be mobilized on the surface of the graft. The surface of the graft has to be sufficiently biocompatible to create a prone environment for the EPCs to adhere, proliferate and, differentiate to form a layer and subsequently improve graft patency. In this work, two active molecules GRGDS and sitagliptin, were chosen for their abilities to recruit, enhance adhesion and induce differentiation of endothelial progenitor cells. They were grafted on PET surfaces in order to provide restrained cues triggering cell alignment and evaluate the influence of such structuration on EPCs fate. We then analyze cell behavior onto functionalized biomaterials. Their abilities to control EPCs fate were demonstrated via RT-qPCR, immunofluorescence, and enzymatic tests. The GRGDS/sitagliptin 100 × 10 surface enables to reduce the stemness phenotype on EPCs and induce the expression of endothelial lineage markers. These results highlight the importance of spatial patterning cues in guiding EPCs organization and function, which may have clinical relevance in the development of vascular grafts that promote patency.
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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".