Peptide grafting on intraosseous transcutaneous amputation prostheses to promote sealing with skin cells: Potential to limit infections
Bibliographic record
Abstract
The long-term success of intraosseous transcutaneous amputation prostheses (ITAPs) mainly relies on dermal attachment of skin cells to the implant. Otherwise, bacteria can easily penetrate through the interface between the implant and the skin. Therefore, infection at this implant/skin interface remains a significant complication in orthopedic surgeries in which these prostheses are required. Two main strategies were investigated to prevent these potential infection problems which consist in either establishing a strong sealing at the skin/implant interface or on eradicating infections by killing bacteria. In this work, two adhesion peptides, either KRGDS or KYIGSR and one antimicrobial peptide, Magainin 2 (Mag 2), were covalently grafted via phosphonate anchor arms to the surface of the Ti6Al4V ELI (extra low interstitials) material, commonly used to manufacture ITAPs. X-ray photoelectron spectroscopy, contact angle, and confocal microscopy analyses enabled to validate the covalent and stable grafting of these three peptides. Dermal fibroblasts cultures on bare Ti6Al4V ELI surfaces and functionalized ones displayed a good cell adhesion and proliferation on all samples. However, cell spreading and viability appeared to be improved on grafted surfaces, especially for those conjugated with KRGDS and Mag 2. Moreover, the dermal sheet attachment, was significantly higher on surfaces functionalized with Mag 2 as compared to the other surfaces. Therefore, the surface functionalization with the antimicrobial peptide Mag 2 seems to be the best approach for the targeted application, as it could play a dual role, inducing a strong skin adhesion while limiting infections on Ti6Al4V ELI materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".