Effect of BMP-2 and BMP-7 homodimers and a mixture of BMP-2/BMP-7 homodimers on osteoblast adhesion and growth following culture on a collagen scaffold
Bibliographic record
Abstract
In the present study, we studied the involvement of BMP-2 and BMP-7 as homodimers and as a mixture of homodimers in bone regeneration using an engineered bone model. The engineered bone model consisted of a collagen scaffold populated with osteoblasts that acted as a carrier for the BMPs. BMP-2, BMP-7 and a mixture of BMP-2/BMP-7 were used at final concentrations of 10 and 100 ng ml(-1). Osteoblasts seeded onto a collagen scaffold were cultured for 24 h before being stimulated with the BMPs. Four days later, osteoblast adhesion to and growth on the scaffold were assessed. Osteocalcin, IL-6, metalloproteinase (MMP-2 and MMP-9) and protease inhibitor (TIMP-1 and TIMP-2) mRNA and protein levels were measured. Our results showed that the BMP-2, BMP-7 and a mixture of BMP-2/BMP-7 all promoted osteoblast growth on the collagen scaffold, with the mixture of BMP-2/BMP-7 enhancing the most growth. BMP-2 and the mixture of BMP-2/BMP-7 enhanced osteocalcin (an osteoblast differentiation marker) mRNA expression and protein secretion, likely via the IL-6 pathway given that IL-6 secretion was upregulated by BMP-7 and a mixture of BMP-2/BMP-7. BMPs promote extracellular matrix production by inhibiting MMP-2 mRNA and increasing TIMP-1 and TIMP-2 mRNA expressions and protein secretions. BMP-2, BMP-7 and the mixture of BMP-2/BMP-7 could promote bone regeneration via different mechanisms involving IL-6 and MMP inhibitors.
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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 | 0.001 | 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) | 0.001 | 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".