Fluoropassivation and gelatin sealing of polyester arterial prostheses to skip preclotting and constrain the chronic inflammatory response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it