A new strategy to prepare n-HA/CS composite scaffolds with surface loading of CS microspheres
Bibliographic record
Abstract
A nano-hydroxyapatite/chitosan (n-HA/CS) scaffold loading with CS microspheres was developed in this study. First, CS microspheres with an average diameter of ∼57 μm were prepared by cross-linking, and a three-dimensional (3D) printing technology was used to fabricate n-HA/CS porous scaffolds with an average pore size and porosity of 454 ± 51 μm and 60.71%, respectively. Then, the n-HA/CS scaffolds were immersed in a solution of CS microspheres and cross-linked by vanillin. The structure, porosity, composition, and cellular behavior of the CS-microsphere-loaded scaffolds were investigated. In vitro cell experiments showed that the CS microspheres on the scaffolds could effectively promote the adhesion and growth of cells on the scaffolds, suggesting that the CS microspheres and the scaffolds have good cytocompatibility. The composite scaffolds were implanted in the back muscles of rabbits for 3, 6, and 9 weeks, and the results of subsequent SEM and H&E staining analyses demonstrated that both the n-HA/CS scaffolds and the loaded CS microspheres had good biocompatibility and gradually degraded over time, where the microspheres were gradually released from the scaffolds. The strategy developed in this study for loading microspheres on scaffolds has promising prospects for future clinical applications of enhancing bone defect repair or implant integration.
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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) | 0.001 | 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 | 0.001 |
| 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".