In vitro Biological Performances of Phosphorylcholine‐Grafted ePTFE Prostheses through RFGD Plasma Techniques
Bibliographic record
Abstract
Arterial prostheses made of microporous Teflon (ePTFE) are currently used in vascular surgery as bypasses for small and medium vessels. However, several clinical complications, such as thrombosis, frequently occur in these prostheses when implanted in humans. In this work, an original strategy was developed to improve the hemocompatibility of ePTFE prostheses, based on glow-discharge surface modification followed by chemical grafting of phosphorylcholine, known for its hemocompatible properties. This procedure leads to a covalent attachment of the molecules, therefore preventing their removal by shear stress induced by blood flow at the implant wall. The improvement of the blood compatibility properties of the modified ePTFE arterial prostheses have been investigated by in vitro tests such as thromboelastography, neutrophil adsorption, platelet aggregation, and cell cultures. These in vitro tests put in evidence that thrombogenicity index, platelet aggregation, and neutrophil adhesion were decreased by the molecule grafted on the prostheses. Moreover, the cell growth on the surface of the PRC-grafted prostheses was greatly enhanced in comparison to the virgin prosthesis. Based on these results, it could be concluded that PRC grafting on ePTFE prostheses permit to improve in vitro hemocompatibility and biocompatibility in comparison with their virgin counterpart.
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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".