Murine Preosteoblast Differentiation Induced by a Peptide Derived from Bone Morphogenetic Proteins-9
Bibliographic record
Abstract
Bone morphogenetic proteins (BMPs) increase the differentiation of osteoblasts implicated in bone formation and repair. In a previous study, we demonstrated that a peptide derived from BMP-9 (pBMP-9) at 400 ng/mL inhibited murine preosteoblasts MC3T3-E1 proliferation. Here, we compared the effects of equimolar concentrations of BMP-2 (50 ng/mL), BMP-9 (42.3 ng/mL), and pBMP-9 (4.52 ng/mL) on the differentiation of MC3T3-E1 in a serum-free medium. Like BMP-2, BMP-9 and pBMP-9 activated the Smad pathway. In contrary to BMP-2, the Smad phosphorylation induced by BMP-9 and pBMP-9 is not prevented by noggin, an extracellular antagonist of BMP-2. Further, BMP-9 and pBMP-9 increased, dose dependently, alkaline phosphatase activity, an early marker of osteoblast differentiation, after 1 day. Quantitative real-time polymerase chain reaction analysis demonstrated that BMP-2, BMP-9, and pBMP-9 (4.52 or 400 ng/mL) all activated the transcription of Runx2, Osterix, type I collagen alpha1 chain, and Osteocalcin genes within day 6. Alizarin red S quantification demonstrated that pBMP-9 (400 ng/mL) and pBMP-9 (4.52 ng/mL) allowed a slight deposition of Ca(2+) in the extracellular matrix of cells within 12 and 18 days, respectively. Therefore, pBMP-9 might be a promising replacement for costly BMP in tissue engineering applications that require a well-defined serum-free medium.
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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 | 0.000 |
| 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 | 0.001 |
| 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".