Bone Marrow Cells from Normal and Ovariectomized Rats Respond Differently to Basic Fibroblast Growth Factor and Bone Morphogenetic Protein 2 Treatment <i>in Vitro</i>
Bibliographic record
Abstract
The protein growth factors basic fibroblast growth factor (bFGF) and bone morphogenetic protein 2 (BMP-2) are being actively pursued for bone tissue engineering. Although both proteins are capable of stimulating osteogenic activity of bone marrow cells (BMCs), no studies have addressed the effect of estrogen deficiency on the growth factor responsiveness of BMCs. This study investigated the osteogenic response of BMCs from normal and ovariectomized (OVX) rats to bFGF and BMP- 2. In the absence of growth factors, a higher number of total colony-forming units (t-CFU) and alkaline phosphatase-expressing CFU (ALP-CFU) were obtained with BMCs derived from OVX rats. The percentage of ALP-CFU, however, was not significantly different between BMCs from the two groups of rats. Whereas BMP-2 did not influence the t-CFU and percentage of ALP-CFU, bFGF decreased t-CFU in BMCs derived from OVX rats and reduced the percentage of ALP-CFU in BMCs from both types of rats. Consistent with the higher t-CFU, the number of mineralized colonies (min-CFU) was also higher for BMCs derived from OVX rats. The number of min-CFU was not influenced by BMP-2 treatment, but was reduced with bFGF treatment. Comparison of the growth factor effects on a per-cell (DNA) basis confirmed the expected stimulatory effect of BMP-2 on ALP activity and mineralization in BMCs from normal rats, but these two parameters were not unequivocally stimulated in BMCs from OVX rats. We conclude that BMCs derived from normal and OVX rats exhibited significant differences in their osteogenic response to bFGF and BMP-2 treatment.
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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 | 0.000 |
| 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".