Three‐dimensionally printed polyetherketoneketone scaffolds with mesenchymal stem cells for the reconstruction of critical‐sized mandibular defects
Bibliographic record
Abstract
OBJECTIVE: Additive manufacturing offers a tailored approach to tissue engineering by providing anatomically precise scaffolds onto which stem cells and growth factors can be supplied. Polyetherketoneketone (PEKK), an ideal candidate biomaterial, is limited by a poor implant-bone interface but can be functionalized with adipose-derived stem cells (ADSC) to promote integration. This in vivo study examined the interaction of a three-dimensional printed PEKK/ADSC implant within the critical-sized mandibular defect in a rabbit model. STUDY DESIGN/METHODS: Trapezoidal porous scaffolds with dimensions of 1.5 × 1.0 × 0.5 cm were printed using selective laser sintering. ADSCs were seeded on the scaffolds that were then implanted in marginal defects created in New Zealand rabbits. Rabbits were euthanized at 10- and 20-week intervals. Microcomputed tomography was used to characterize bone ingrowth and was correlated with histological analysis. Stress testing was performed on the scaffolds before and after implantation. RESULTS: All scaffolds were well integrated into adjacent bone. Bone-to-tissue volume increased from 30.34% ( ± 12.46) to 61.27% ( ± 8.24), and trabecular thickness increased from 0.178 mm ( ± 0.069) to 0.331 mm ( ± 0.0306) in the 10- and 20-week groups, respectively, compared to no bone regrowth on the control side (P < 0.05). Histology confirmed integration at the bone-implant interface. Biomechanical testing revealed a compressive resistance 15 times that of bone alone (P < 0.05) CONCLUSION: 3D-printed PEKK scaffolds combined with ADSCs present a promising solution to improve the bone-implant interface and increase the resistance to forces of mastication after mandibular reconstruction. LEVEL OF EVIDENCE: NA. Laryngoscope, 127:E392-E398, 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".