Bone-mimicking scaffold based on silk fibroin incorporated with hydroxyapatite and titanium oxide as enhanced osteo-conductive material for bone tissue formation: fabrication, characterization, properties, and <i>in vitro</i> testing
Bibliographic record
Abstract
Abstract Bone-mimicking scaffolds based on silk fibroin (SF) mixed with hydroxyapatite nanoparticles (HA NPs) and titanium oxide (TiO2) nanoparticles were created as materials for bone formation. Six scaffold groups were fabricated: S1 (SF), S2 (Silk + (HA: TiO2; 100: 0)), S3 (Silk, (HA: TiO2; 70: 30)), S4 (Silk + (HA NPs: TiO2; 50: 50)), S5 (Silk + (HA: TiO2; 30: 70)), and S6 (Silk + (HA NPs: TiO2; 0:100)). Scaffolds were characterized for molecular formation, structure, and morphology by Fourier transform infrared spectroscopy, element analysis, and X-ray diffraction. They were tested for physical swelling and compressive modulus. Scaffolds were cultured with MC3T3 and tested in vitro to evaluate their biological performance. The results showed that scaffolds with HA and TiO2 demonstrated molecular interaction via amide I and phosphate groups. These scaffolds had smaller pore sizes than those without HA and TiO2. They showed more swelling and higher compressive modulus than the scaffolds without HA and TiO2. They exhibited better biological performance: cell adhesion, viability, proliferation, alkaline phosphatase activity, and calcium content than the scaffolds without HA and TiO2. Their porous walls acted as templates for cell aggregation and supported synthesis of calcium secreted from cells. S3 were the most suitable scaffolds. With their enhanced osteo-conductive function, they are promising for bone augmentation for oral and maxillofacial surgery.
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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".