Fusion peptide <scp>P15‐CSP</scp> shows antibiofilm activity and pro‐osteogenic activity when deposited as a coating on hydrophilic but not hydrophobic surfaces
Bibliographic record
Abstract
In the context of porous bone void filler for oral bone reconstruction, peptides that suppress microbial growth and promote osteoblast function could be used to enhance the performance of a porous bone void filler. We tested the hypothesis that P15-CSP, a novel fusion peptide containing collagen-mimetic osteogenic peptide P15, and competence-stimulating peptide (CSP), a cationic antimicrobial peptide, has emerging properties not shared by P15 or CSP alone. Peptide-coated surfaces were tested for antimicrobial activity toward Streptoccocus mutans, and their ability to promote human mesenchymal stem cell (MSC) attachment, spreading, metabolism, and osteogenesis. In the osteogenesis assay, peptides were coated on tissue culture plastic and on thin films generated by plasma-enhanced chemical vapor deposition to have hydrophilic or hydrophobic character (water contact angles 63°, 42°, and 92°, respectively). S. mutans planktonic growth was specifically inhibited by CSP, whereas biofilm formation was inhibited by P15-CSP. MSC adhesion and actin stress fiber formation was strongly enhanced by CSP, P15-CSP, and fibronectin coatings and modestly enhanced by P15 versus uncoated surfaces. Metabolic assays revealed that CSP was slightly cytotoxic to MSCs. MSCs developed alkaline phosphatase activity on all surfaces, with or without peptide coatings, and consistently deposited the most biomineralized matrix on hydrophilic surfaces coated with P15-CSP. Hydrophobic thin films completely suppressed MSC biomineralization, consistent with previous findings of suppressed osteogenesis on hydrophobic bioplastics. Collective data in this study provide new evidence that P15-CSP has unique dual capacity to suppress biofilm formation, and to enhance osteogenic activity as a coating on hydrophilic surfaces.
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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".