Endothelial Cell Responses Towards Surface-modified Expanded Polytetrafluoroethylene Fibers
Bibliographic record
Abstract
The effect of various surface modifications, in conjunction with the intrinsic surface features of expanded polytetrafluoroethylene (ePTFE) fibers on endothelialization, were investigated. A multi-step surface modification strategy was applied to commercial ePTFE sutures and then the effect of surface topography and surface chemistry involving cell adhesive and cell-resistant molecules was evaluated towards endothelial cell adhesion and its spreading. N-hepthylamine plasma polymer (HApp) was deposited onto the ePTFE fiber surface and then carboxy-methyl-dextran (CMD) was covalently attached. Subsequently, GRGDS and GRGES peptides were covalently grafted onto the CMD graft layer. The micrometric and nanometric features of ePTFE were qualitatively examined by atomic force microscopy, and scanning electron microscopy. Human umbilical vein endothelial cells (HUVECs) were used to evaluate the in vitro cell adhesion on nonmodified fibers and the multi-step surface coatings. Cell adhesive molecules (HApp and RGD) enhanced the cell adhesion while cell-resistant molecules (CMD and RGE) and nonmodified fibers resisted cell adhesion. Therefore, only surface features have no effect on the HUVEC adhesion and that the surface chemistry is dominant in modulating HUVEC adhesion on ePTFE fibers.
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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".