Mesenchymal Stem Cells and Three-Dimensional-Osteoconductive Scaffold Regenerate Calvarial Bone in Critical Size Defects in Swine
Bibliographic record
Abstract
Abstract Craniofacial bones protect vital organs, perform important physiological functions, and shape facial identity. Critical-size defects (CSDs) in calvarial bones, which will not heal spontaneously, are caused by trauma, congenital defects, or tumor resections. They pose a great challenge for patients and physicians, and significantly compromise quality of life. Currently, calvarial CSDs are treated either by allogenic or autologous grafts, metal or other synthetic plates that are associated with considerable complications. While previous studies have explored tissue regeneration for calvarial defects, most have been done in small animal models with limited translational value. Here we define a swine calvarial CSD model and show a novel approach to regenerate high-quality bone in these defects by combining mesenchymal stem cells (MSCs) with a three-dimensional (3D)-printed osteoconductive HA/TCP scaffold. Specifically, we have compared the performance of dental pulp neural crest MSCs (DPNCCs) to bone marrow aspirate (BMA) combined with a 3D-printed HA/TCP scaffold to regenerate bone in a calvarial CSD (>7.0 cm2). Both DPNCCs and BMA loaded onto the 3D-printed osteoconductive scaffold support the regeneration of calvarial bone with density, compression strength, and trabecular structures similar to native bone. Our study demonstrates a novel application of an original scaffold design combined with DPNCCs or BMA to support regeneration of high-quality bone in a newly defined and clinically relevant swine calvarial CSD model. This discovery may have important impact on bone regeneration beyond the craniofacial region and will ultimately benefit patients who suffer from debilitating CSDs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".