Nanocoatings, degradable metals and surface fonctionnalisation: Towards high-performance cardiovascular biomaterials
Bibliographic record
Abstract
The need for highly performing vascular biomaterials is rapidly increasing with the ageing of population. Vascular diseases are the primary cause of death in the world and at least 1 million patients undergo to surgical operation for prosthesis implantation each year worldwide to face cardiovascular occlusive diseases, aneurysms and acute renal failures. As the major problem still resides in an interfacial mismatch between the synthetic inert graft and the natural living tissue surrounding it, the common approaches consist of modulating the tissue/biomaterial interface by modifying the synthetic graft surface properties, in an attempt to improve their long-term biocompatibility and hemocompatibility. Thus, several coating techniques, including plasma-based treatments, were investigated during the last 20 years to improve clinical performances of cardiovascular devices, including stents and vascular prostheses. Strong binding of selected bio-molecules, including protein-repellent ones, surface patterning, and a number of other strategies has already been investigated in order to obtain biological-like surfaces based on the hypothesis that the human body would positively interact with these biological coated materials. Nevertheless, such coatings did not completely successes clinically as it turned out that the bioactive materials could not play their biological role as well as expected and eventually led to the development of negative interactions and finally to clinical complications. Today, nanotechnology and surface modifications provides a new insight to the current problem of biomaterial failures, and even allows us to envisage strategies for the organ shortage. Advanced tools and new paths towards the development of functional solutions for cardiovascular clinical applications are now available. Within this general framework, this talk will focus on highly-adherent and strongly-cohesive (after deployment) fluorocarbon nano-coatings for intravascular stents, bio-mimicking coatings for vascular prostheses, and degradable metals for temporary devices. The intrinsic goal is to present an extremely personal look at how materials and surface modifications have progressed, from the glory days of their introduction, to the promising future that nanotechnology may or may not hold for improving the quality of the life of millions worldwide.
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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 | 0.001 | 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".