Electrospun Nanofiber Scaffolds and Plasma Polymerization: A Promising Combination Towards Complete, Stable Endothelial Lining for Vascular Grafts
Bibliographic record
Abstract
In the quest to reduce risk of thrombosis in vascular grafts, it is essential to provide a surface with morphological and mechanical properties close to those of the extracellular matrix beneath the luminal endothelium, and to favor the growth of a confluent, stable monolayer of endothelial cells. This is accomplished here by combining electrospun poly(ethylene terephthalate) (PET) mats with an amine-rich thin plasma-polymerized coating, designated "L-PPE:N." Its deposition does not modify the open, highly porous mats and leads only to small changes in mechanical properties. L-PPE:N significantly improves the adhesion and growth of human umbilical vein endothelial cells (HUVEC) and their resistance to flow-induced shear stress. These properties favor the formation of desired confluent HUVEC monolayers on the topmost surface, unlike conventional vascular grafts (ePTFE or woven PET), where cells migrate inside the material. This combination is therefore highly advantageous for the pre-endothelialization of the luminal side of small-diameter vascular prostheses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".