Dynamics of Endothelial Cell Responses to Laminar Shear Stress on Surfaces Functionalized with Fibronectin-Derived Peptides
Bibliographic record
Abstract
Surface endothelialization could improve the long-term performance of vascular grafts and stents. We previously demonstrated that aerosol-generated fibronectin-derived peptide micropatterns consisting of GRGDS spots over a WQPPRARI background increase endothelial cell yields in static cultures. We developed a novel fluorophore-tagged RGD peptide (RGD-TAMRA) to visualize cell–surface interactions in real-time. Here, we studied the dynamics of endothelial cell response to laminar flow on these peptide-functionalized surfaces. Endothelial cells were exposed to 22 dyn/cm 2 wall shear stress while acquiring time-lapse images. Cell surface coverage and cell alignment were quantified by undecimated wavelet transform multivariate image analysis. Similar to gelatin-coated surfaces, surfaces with uniform RGD-TAMRA distribution led to cell retention and rapid cell alignment (∼63% of the final cell alignment was reached within 1.5 h), contrary to the micropatterned surfaces. The RGD-TAMRA peptide is a promising candidate for endothelial cell retention under flow, and the spray-based micropatterned surfaces are more promising for static cultures.
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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".