Grafting of a model protein on lactide and caprolactone based biodegradable films for biomedical applications
Bibliographic record
Abstract
Thermoplastic biodegradable polymers displaying elastomeric behavior and mechanical consistency are greatly appreciated for the regeneration of soft tissues and for various medical devices. However, while the selection of a suitable base material is determined by mechanical and biodegradation considerations, it is the surface properties of the biomaterial that are responsible for the biological response. In order to improve the interaction with cells and modulate their behavior, biologically active molecules can be incorporated onto the surface of the material. With this aim, the surface of a lactide and caprolactone based biodegradable elastomeric terpolymer was modified in two stages. First, the biodegradable polymer surface was aminated by atmospheric pressure plasma treatment and second a crosslinker was grafted in order to covalently bind the biomolecule. In this study, albumin was used as a model protein. According to X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM), albumin was efficiently immobilized on the surface of the terpolymer, the degree of albumin surface coverage (ΓBSA) reached ~35%. Moreover, gel permeation chromatography (GPC) studies showed that the hydrolytic degradation kinetic of the synthesized polymer was slightly delayed when albumin was grafted. However, the degradation process in the bulk of the material was unaffected, as demonstrated by Fourier transform infrared (FTIR) analyses. Furthermore, XPS analyses showed that the protein was still present on the surface after 28 days of degradation, meaning that the surface modification was stable, and that there had been enough time for the biological environment to interact with the modified material.
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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".