Cationic osteogenic peptide P15‐CSP coatings promote 3‐D osteogenesis in poly(epsilon‐caprolactone) scaffolds of distinct pore size
Bibliographic record
Abstract
P15-CSP is a biomimetic cationic fusion peptide that stimulates osteogenesis and inhibits bacterial biofilm formation when coated on 2-D surfaces. This study tested the hypothesis that P15-CSP coatings enhance 3-D osteogenesis in a porous but otherwise hydrophobic poly-(ɛ-caprolactone) (PCL) scaffold. Scaffolds of 84 µm and 141 µm average pore size were coated or not with Layer-by-Layer polyelectrolytes followed by P15-CSP, seeded with adult primary human mesenchymal stem cells (MSCs), and cultured 10 days in proliferation medium, then 21 days in osteogenic medium. Atomic analyses showed that P15-CSP was successfully captured by LbL. After 2 days of culture, MSCs adhered and spread more on P15-CSP coated pores than PCL-only. At day 10, all constructs contained nonmineralized tissue. At day 31, all constructs became enveloped in a "skin" of tissue that, like 2-D cultures, underwent sporadic mineralization in areas of high cell density that extended into some 141 µm edge pores. By quantitative histomorphometry, 2.5-fold more tissue and biomineral accumulated in edge pores versus inner pores. P15-CSP specifically promoted tissue-scaffold integration, fourfold higher overall biomineralization, and more mineral deposits in the outer 84 µm and inner 141 µm pores than PCL-only (p < 0.05). 3-D Micro-CT revealed asymmetric mineral deposition consistent with histological calcium staining. This study provides proof-of-concept that P15-CSP coatings are osteoconductive in PCL pore surfaces with 3-D topography. Biomineralization deeper than 150 µm from the scaffold edge was optimally attained with the larger 141 µm peptide-coated pores. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2171-2181, 2017.
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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".