Biocompatibility and dimensional stability through the use of <scp>3D</scp>‐printed scaffolds made by polycaprolactone and bioglass‐7: An in vitro and in vivo study
Bibliographic record
Abstract
PURPOSE: This experiment aimed to observe the differences in biological properties by producing BGS-7 + PCL scaffolds with different weight fractions of BGS-7 through 3D printing and to confirm whether using the scaffold for vertical bone augmentation is effective. MATERIALS AND METHODS: Cube-shaped bioglass (BGS-7) and polycaprolactone (PCL) scaffolds with different weight fractions (PCL alone, PCL with 15% and 30% BGS-7) are produced using 3D printing. The surface hydroxyapatite (HA) apposition, the pH change, proliferation and attachment assays, and various gene expression levels are assessed. After a 7-mm implant was inserted 3 mm into the rabbit calvaria, vertical bone augmentation is performed around the implant and inside the scaffold in four ways: scaffold only, scaffold+bone graft, bone graft only, and no graft. Sacrifice is performed at 6, 12, and 24 weeks, and the various parameters are compared radiographically and histologically. RESULTS: HA apposition, cell proliferation, cell attachment, and expression of osteogenic genes increase as the proportion of BGS-7 increase. In the in vivo test, a higher bone-implant contact ratio, bone volume ratio, bone mineral density, and new bone area are observed when the scaffold and bone grafts were used together. CONCLUSION: The 3D-printed scaffold, a mixture of BGS-7 and PCL, exhibit higher biological compatibility as the proportion of BGS-7 increase. Additionally, the use of scaffold is effective for vertical bone augmentation.
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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".