Coating of cobalt chrome substrates with thin films of polar/hydrophobic/ionic polyurethanes: Characterization and interaction with human immunoglobulin G and fibronectin
Bibliographic record
Abstract
Biomaterial implants often lead to specific tissue reactions that could compromise their bio-integration and/or optimal cellular interactions. Polyurethanes (PU) are of particular interest as coatings to mask CoCr's bioactivity, since they are generally more biocompatible than metal substrates, present a broad range of chemistry, and have highly tunable-mechanical properties. In the current work, complex polyvinyl-urethanes (referred to as D-PHI materials) are studied for their surface phase structures: specifically, an original D-PHI polymer (O-D-PHI) and a differential formulation with relatively higher hydrophobic and ionic content (HHHI) are of interest. The PUs are diluted in tetrahydrofuran (THF) to generate thin films which differentially influence the physical and chemical properties of the D-PHI coatings. AFM images over time show the gradual appearance of domains exhibiting crystalline organisation, and whose shape and size were dependent on D-PHI thickness (thin films vs non-solvent cast resin materials). After three weeks, a complete stabilization of the crystal state is observed. The thin coatings are stable in an aqueous and 37 °C environment. The adsorption of two human plasmatic proteins Immunoglobulin G (IgG) and Fibronectin (Fn), involved in inflammation and coagulation, was studied. The exposure of specific protein sequences (IgG-Fab, Fn-Cell Binding Domain and Fn-N-terminal domain) were dramatically reduced on both D-PHI materials when compared to bare metal CoCr. The implications of these findings would be relevant to defining coating strategies used to improve the blood clotting and immune cell reactivity to CoCr implant materials.
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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".