Development of an Antimicrobial Peptide SAAP‐148‐Functionalized Supramolecular Coating on Titanium to Prevent Biomaterial‐Associated Infections
Bibliographic record
Abstract
Abstract Titanium implants are widely used in medicine but have a risk of biomaterial‐associated infection (BAI), of which traditional antibiotic‐based treatment is affected by resistance. Antimicrobial peptides (AMPs) are used to successfully kill antibiotic‐resistant bacteria. Herein, a supramolecular coating for titanium implants is developed which presents the synthetic antimicrobial and antibiofilm peptide SAAP‐148 via supramolecular interactions using ureido‐pyrimidinone supramolecular units (UPy‐SAAP‐148GG). Material characterization of dropcast coatings shows the presence of UPy‐SAAP‐148GG at the surface. The supramolecular immobilized peptide remains antimicrobially active in dropcast polymer films and can successfully kill (antibiotic‐resistant) Staphylococcus aureus, Acinetobacter baumannii, and Escherichia coli. Minor toxicity for human dermal fibroblasts is observed, with a reduced cell attachment after 24 h. Subsequently, a dipcoat coating on titanium implants is developed and tested in vivo in a subcutaneous implant infection mouse model with S. aureus administered locally on the implant before implantation to mimic contamination during surgery. The supramolecular coating containing 5 mol% of UPy‐SAAP‐148GG significantly prevents colonization of the implant surface as well as of the surrounding tissue, with no signs of toxicity. This shows that supramolecular AMP coatings on titanium are eminently suitable to prevent BAI.
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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".