Fibronectin grafting to enhance skin sealing around transcutaneous titanium implant
Bibliographic record
Abstract
Intraosseous transcutaneous amputation prosthesis is a new approach in orthopedic implants that overcomes socket prosthesis problems. Its long-term performance requires a tight skin-implant seal to prevent infections. In this study, fibronectin (Fn), a widely used adhesion protein, was adsorbed or grafted onto titanium alloy. Fn grafting was performed using two different linking arms, dopamine/glutaric anhydride or phosphonate. The characterization of Fn-modified surfaces showed that Fn grating via phosphonate has led to the highest amount of Fn cell-binding site (RGD, arginine, glycine, and aspartate) available on the surface. Interestingly, cell culture studies revealed a strong correlation between the amount of available RGD ligands and cellular behavior, since enhanced proliferation and spreading of fibroblasts were noticed on Fn-grafted surfaces via phosphonate. In addition, an original in vitro mechanical test, inspired from the real situation, to better predict clinical outcomes after implant insertion, has been developed. Tensile test data showed that the adhesion strength of a bio-engineered dermal tissue was significantly higher around Fn-grafted surfaces via phosphonate, as compared to untreated surfaces. This study sheds light on the importance of an appropriate selection of the linking arm to tightly control the spatial conformation of biomolecules on the material surface, and consequently cell interactions at the interface tissue/implant.
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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) | 0.000 | 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".