Combination of polyetherketoneketone scaffold and human mesenchymal stem cells from temporomandibular joint synovial fluid enhances bone regeneration
Bibliographic record
Abstract
Therapies using human mesenchymal stem cells (MSCs) combined with three-dimensional (3D) printed scaffolds are a promising strategy for bone grafting. But the harvest of MSCs still remains invasive for patients. Human synovial fluid MSCs (hSF-MSCs), which can be obtained by a minimally invasive needle-aspiration procedure, have been used for cartilage repair. However, little is known of hSF-MSCs in bone regeneration. Polyetherketoneketone (PEKK) is an attractive bone scaffold due to its mechanical properties comparable to bone. In this study, 3D-printed PEKK scaffolds were fabricated using laser sintering technique. hSF-MSCs were characterized and cultured on PEKK to evaluate their cell attachment, proliferation, and osteogenic potential. Rabbit calvarial critical-sized bone defects were created to test the bone regenerative effect of PEKK with hSF-MSCs. In vitro results showed that hSF-MSCs attached, proliferated, and were osteogenic on PEKK. In vivo results indicated that PEKK seeded with hSF-MSCs regenerated twice the amount of newly formed bone when compared to PEKK seeded with osteogenically-induced hSF-MSCs or PEKK scaffolds alone. These results suggested that there was no need to induce hSF-MSCs into osteoblasts prior to their transplantations in vivo. In conclusion, the combined use of PEKK and hSF-MSCs was effective in regenerating critical-sized bone defects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".