Chitosan coatings with distinct innate immune bioactivities differentially stimulate angiogenesis, osteogenesis and chondrogenesis in poly-caprolactone scaffolds with controlled interconnecting pore size
Bibliographic record
Abstract
This study tested whether osseous integration into poly (ε-caprolactone) (PCL) bioplastic scaffolds with fully-interconnecting 155 ± 8 μm pores is enhanced by an adhesive, non-inflammatory 99% degree of deacetylation (DDA) chitosan coating (99-PCL), or further incorporation of pro-inflammatory 83% DDA chitosan microparticles (83-99-PCL) to accelerate angiogenesis. New Zealand White rabbit osteochondral knee defects were press-fit with PCL, 99-PCL, 83-99-PCL, or allowed to bleed (drill-only). Between day 1 and 6 weeks of repair, drill-only defects repaired by endochondral ossification, with an 8-fold higher bone volume fraction (BVF) versus initial defects, compared to a 2-fold (99-PCL), 1.1-fold (PCL), or 0.4-fold (83-99-PCL) change in BVF. Hematoma innate immune cells swarmed to 83-99-PCL, elicited angiogenesis throughout the pores and induced slight bone resorption. PCL and 99-PCL pores were variably filled with cartilage or avascular mesenchyme near the bone plate, or angiogenic mesenchyme into which repairing trabecular bone infiltrated up to 1 mm deep. More repair cartilage covered the 99-PCL scaffold (65%) than PCL (18%) or 83-99-PCL (0%) (p < 0.005). We report the novel finding that non-inflammatory chitosan coatings promoted cartilage infiltration into and over a bioplastic scaffold, and were compatible with trabecular bone integration. This study also revealed that in vitro osteogenesis assays have limited ability to predict osseous integration into porous scaffolds, because (1) in vivo, woven bone integrates from the leading edge of regenerating trabecular bone and not from mesenchymal cells adhering to scaffold surfaces, and (2) bioactive coatings that attract inflammatory cells induce bone resorption.
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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".