The efficacy of polycaprolactone/hydroxyapatite scaffold in combination with mesenchymal stem cells for bone tissue engineering
Bibliographic record
Abstract
Major drawbacks of using an autograft are the possibilities of insufficient bony source and patient's morbidity after operation. Bone tissue engineering technology, therefore, has been applied for repairing bony defects. Previous study showed that a novel fabricated 3D-Polycaprolactone/Hydroxyapatite (PCL/HAp) scaffold possessed a good biocompatibility for bone cells. This study aimed to determine the ability of PCL/HAp for supporting cell growth, gene expression, and osteogenic differentiation in three types of mesenchymal stem cells, including bone marrow-derived mesenchymal stem cells (BMSCs), dental pulp stem cells (DPSCs), and adiposed-derived mesenchymal stem cells (ADSCs). These were assessed by cell viability assay (MTT), reverse-transcription polymerase chain reaction (RT-PCR) analysis, alkaline phosphatase activity, and osteogenic differentiation by alizarin red-S staining. The results showed that PCL/HAp scaffold could support growth of all three types of mesenchymal stem cells. In addition, DPSCs with PCL/HAp showed the highest level of calcium deposition compared to other groups. In conclusion, DPSCs exhibited a better compatibility with these scaffolds compared to BMSCs and ADSCs. However, the PCL/HAp could be a good candidate scaffold for all tested mesenchymal stem cells in bone tissue engineering.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".