Three-Dimensional Printing of Calcium Carbonate/Hydroxyapatite Scaffolds at Low Temperature for Bone Tissue Engineering
Bibliographic record
Abstract
Three-dimensional (3D) printing technology has been applied to fabricate bone tissue engineering scaffolds for a wide range of materials with precisely control over scaffold structures. Coral is a potential bone repair and bone replacement material. Due to the natural source limitation of coral, we developed a fabrication protocol for 3D printing of calcium carbonate (CaCO 3 ) nanoparticles for coral replacement in the application of bone tissue engineering. Up to 80% of CaCO 3 nanoparticles can be printed with high resolution using poly- l -lactide as a blender. The scaffolds were subjected to a controlled hydrothermal process for incomplete conversion of carbonate to phosphate to produce CaCO 3 scaffold covered by hydroxyapatite (HA) to modify the biocompatibility and degradation of CaCO 3 /HA scaffolds. X-ray diffraction and Fourier transform infrared spectroscopy showed that HA was converted and attached to the surface of the scaffold, and the surface morphology and microstructure were studied using a scanning electron microscope. To confirm the bone regeneration performance of the scaffold, cell proliferation and osteogenic differentiation of MC3T3 cells on the scaffold were evaluated. In addition, in vivo experiments showed that CaCO 3 /HA scaffolds can promote bone growth and repairing process and has high potential in bone tissue engineering. ClinicalTrials.gov ID: SH9H-2020-A603
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".