Human saphenous vein endothelial cell adhesion and expansion on micropatterned polytetrafluoroethylene
Bibliographic record
Abstract
Intimal hyperplasia and thrombosis are responsible for the poor patency rates of small-diameter vascular grafts. These complications could be avoided by a rapid and strong adhesion of endothelial cells to the prosthetic surfaces, which typically consist of expanded polytetrafluoroethylene (PTFE) for small-diameter vessels. We have previously described two peptide micropatterning strategies that increase the endothelialization rates of PTFE. The micropatterns were generated either by inkjet printing 300 μm squares or by spraying 10.1 ± 0.1 μm diameter droplets of the CGRGDS cell adhesion peptide, while the remaining surface was functionalized using the CWQPPRARI cell migration peptide. We now directly compare these two micropatterning strategies and examine the effect of hydrodynamic stress on human saphenous vein endothelial cells grown on the patterned surfaces. No significant differences in cell adhesion were observed between the two micropatterning methods. When compared to unpatterned surfaces treated with a uniform mixture of the two peptides, the cell expansion was significantly higher on sprayed or printed surfaces after 9 days of static cell culture. In addition, after 6 h of exposure to hydrodynamic stress, the cell retention and cell cytoskeleton reorganization on the patterned surfaces was improved when compared to untreated or random treated surfaces. These results indicate that micropatterned surfaces lead to improved rates of PTFE endothelialization with higher resistance to hydrodynamic stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".