Development of a New Bone‐Mimetic Surface Treatment Platform: Nanoneedle Hydroxyapatite (nnHA) Coating
Bibliographic record
Abstract
The hierarchical structure of bone plays pivotal roles in driving cell behavior and tissue regeneration and must be considered when designing materials for orthopedic applications. Herein, it is aimed to recapitulate the native bone environment by using melt electrowriting to fabricate fibrous microarchitectures which are modified with plate-shaped (pHA) or novel nanoneedle-shaped (nnHA) crystals. Nuclear magnetic resonance spectroscopy, scanning electron microscopy, transmission electron microscopy, and X-ray diffraction demonstrate that these coatings replicate the nanostructure and composition of native bone. Human mesenchymal stem/stromal cell (MSC) mineralization is significantly increased fivefold with pHA scaffolds and 14-fold with nnHA scaffolds. Given the protein stabilizing properties of mineral, these materials are further functionalized with bone morphogenetic protein 2 (BMP2). nnHA treatment facilitates controlled release of BMP2 which further enhance MSC mineral deposition. Finally, the versatility of this nnHA treatment method, which may be used to coat different architectures/materials including fused deposition modeling (FDM) scaffolds and Ti6Al4V titanium, is demonstrated. This study thus outlines a method for fabricating scaffolds with precise fibrous microarchitectures and bone-mimetic nnHA extrafibrillar coatings which significantly enhance MSC osteogenesis and therapeutic protein delivery, and leverages these results to show how this surface treatment method may be applied to a wider field for multiple orthopedic applications.
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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 | 0.001 | 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".