Bone‐Conditioned Medium Inhibits Osteogenic and Adipogenic Differentiation of Mesenchymal Cells In Vitro
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Autografts are used for bone reconstruction in regenerative medicine including oral and maxillofacial surgery. Bone grafts release paracrine signals that can reach mesenchymal cells at defect sites. The impact of the paracrine signals on osteogenic, adipogenic, and chondrogenic differentiation of mesenchymal cells has remained unclear. MATERIAL AND METHODS: Osteogenesis, adipogenesis, and chondrogenesis were studied with murine ST2 osteoblast progenitors, 3T3-L1 preadipocytes, and ATDC5 prechondrogenic cells, respectively. Primary periodontal fibroblasts from the gingiva, from the periodontal ligament, and from bone were also included in the analysis. Cells were exposed to bone-conditioned medium (BCM) that was prepared from porcine cortical bone chips. RESULTS: BCM inhibited osteogenic and adipogenic differentiation of ST2 and 3T3-L1 cells, respectively, as shown by histological staining and gene expression. No substantial changes in the expression of chondrogenic genes were observed in ATDC5 cells. Primary periodontal fibroblasts also showed a robust decrease in alkaline phosphatase and peroxisome proliferator-activated receptor gamma (PPARγ) expression when exposed to BCM. BCM also increased collagen type 10 expression. Pharmacologic blocking of transforming growth factor (TGF)-β receptor type I kinase with SB431542 and the smad-3 inhibitor SIS3 at least partially reversed the effect of BCM on PPARγ and collagen type 10 expression. In support of BCM having TGF-β activity, the respective target genes were increasingly expressed in periodontal fibroblasts. CONCLUSIONS: The present work is a pioneer study on the paracrine activity of bone grafts. The findings suggest that cortical bone chips release soluble signals that can modulate differentiation of mesenchymal cells in vitro at least partially involving TGF-β signaling.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".