Fluoropassivation and gelatin sealing of polyester arterial prostheses to skip preclotting and constrain the chronic inflammatory response
Bibliographic record
Abstract
Fluoropassivation and gelatin coating have been applied to polyethylene terephthalate (PET) vascular prosthesis to combine the advantages of both polytetrafluoroethylene (PTFE) and PET materials, and to eliminate the preclotting procedure. The morphological, chemical, physical, and mechanical properties of such prostheses were investigated and compared with its original model. Fluoropassivation introduced -OCF(3), -CF(3), and -CFCF(2)- structures onto the surface of the polyester fibers. However, the surface fluorine content was only 28-32% compared to the 66% in expanded PTFE (ePTFE) grafts. The fluoropassivation decreased the hydrophilicity, slightly increased the water permeability, and marginally lowered the melting point and the crystallinity of the PET fibers. After gelatin coating, the fluoropassivated and nonfluoropassivated prostheses showed similar surface morphology and chemistry. While gelatin coating eliminated preclotting, it also renders the prostheses slightly stiffer. The original prosthesis had the highest bursting strength (275 N), with the fluoropassivated and gelatin-sealed devices showing similar bursting strength between 210 and 230 N. Fluoropassivation and gelatin coating lowered the retention strength by 23 and 30% on average, respectively. In vitro enzymatic incubation had only marginal effect on the surface fluorine content of the nongelatin-sealed prostheses. However, the gelatin-sealed ones significantly lost their surface fluorine after in vitro enzymatic incubation (by 69-85%) or in vivo 6-month implantation (by 51-60%), showing the lability of the fluoropolymer layer under the hostile biological environment.
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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".