Enhancement of Osteoblast Proliferation In Vitro by Selective Enrichment of Demineralized Freeze-dried Bone Allograft With Specific Growth Factors
Bibliographic record
Abstract
Decalcified freeze-dried bone allograft (DFDBA), believed to serve as a matrix for new bone growth and to contain various bone-inducing growth factors, is currently used to regenerate periodontal defects and to restore and maintain dental alveolar ridges. Growth factors within DFDBA are extracted during the demineralization process, thus rendering the allograft incapable of spontaneous osteogenesis; however, exogenous growth factor addition to DFDBA may enhance the osteogenic capacity of native osteoblasts. This study's purpose is to evaluate murine osteoblast proliferation in the presence of various exogenous soluble growth factors as measured by fluorescence units. Osteoblasts harvested from mouse pup calvaria were cultured with 2% residual calcium-DFDBA and supplemented by one of the following growth factors or combinations of these factors: transforming growth factor-beta (TGF-beta), insulin-like growth factor-I (IGF-I), platelet-derived growth factor (PDGF), fibroblast growth factors basic (bFGF), or vascular endothelial growth factors (VEGF). Osteoblast proliferation rates indicate that the in vitro supplementation of 2% residual calcium-DFDBA with the combination of IGF and TGF-beta, IGF and PDGF, and PDGF and TGF-beta significantly (P < or = .05) enhance murine osteoblast activity and proliferation at 7 days compared with the control containing no exogenous growth factors.
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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 | 0.000 | 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".