Haematological Performances of Carbon Coated PTFE by Plasma-Based Deposition
Bibliographic record
Abstract
Materials in contact with tissue and biological fluids affect cell reaction that could eventually lead to clinical complications (i.e. thrombosis, restenosis). Improving the biological performances of the materials used for biomedical applications is the main goal of this study. In particular, cardiovascular devices require excellent haemo- and biocompatibility properties. PTFE is currently the main material used for vascular prostheses. After long contact periods with blood, clinical complications leading to thrombosis and restenosis are often reported. Improving the haematological performances of PTFE could significantly increase its life-time and decrease long-term complications. However, inadequately engineered surfaces could trigger the coagulation cascade with the formation of a clot, the first step towards a thrombosis. Plasma carbon-based coatings with varying nitrogen contents deposited on PTFE have been studied as promising coating to improve the haematological performances of PTFE implants. In this work, several techniques were applied to study the viscoelastic properties of blood after contact with virgin and treated PTFE as well as the presence and the clot morphology eventually formed onto the surfaces. The chemical composition of the surfaces was analysed with XPS and FTIR.
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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 | 0.001 |
| 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".