Bioinspired Blood Compatible Surface Having Combined Fibrinolytic and Vascular Endothelium‐Like Properties via a Sequential Coimmobilization Strategy
Bibliographic record
Abstract
Developing surfaces with antithrombotic properties is of great interest for the applications of blood‐contacting biomaterials and medical devices. It is promising to coimmobilize two or more biomolecules with different and complementary functions to improve blood compatibility. However, the general one‐pot strategy usually adopted by previous studies suffers the problems of inevitable competition between diverse biomolecules and uncontrollability of the relative quantities of the immobilized biomolecules. To solve these problems, a new sequential coimmobilization strategy is proposed and applied to fabricate a blood compatible surface. Polyurethane surface is modified with a copolymer, poly(2‐hydroxyethyl methacrylate‐co‐1‐adamantan‐1‐ylmethyl methacrylate), which serves as a linker‐spacer for sequential attachment of two functional molecules, a hexapeptide containing REDV (Arg‐Glu‐Asp‐Val) sequence, and a modified cyclodextrin bearing 7 lysine ligands, through covalent bonding and host–guest interaction, respectively. The resulting surface combines the antithrombogenic properties of the vascular endothelium and the clot lysing properties of the fibrinolytic system. Importantly, neither of the two functions of REDV peptide and lysine is compromised by the presence of the other, suggesting the enhanced blood compatibility. These results suggest a new strategy to engineer multifunctional surfaces by coimmobilization of bioactive molecules having unique functionalities.
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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) | 0.001 | 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) | 0.000 | 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 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".