Impact of Bone Harvesting Techniques on Cell Viability and the Release of Growth Factors of Autografts
Bibliographic record
Abstract
BACKGROUND: Autogenous bone grafts obtained by different harvesting techniques behave differently during the process of graft consolidation; the underlying reasons are however not fully understood. One theory is that harvesting techniques have an impact on the number and activity of the transplanted cells which contribute to the process of graft consolidation. MATERIALS AND METHODS: To test this assumption, porcine bone grafts were harvested with four different surgical procedures: bone mill, piezosurgery, bone drilling (bone slurry), and bone scraper. After determining cell viability, the release of molecules affecting bone formation and resorption was assessed by reverse transcription polymerase chain reaction and immunoassay. The mitogenic and osteogenic activity of the conditioned media was evaluated in a bioassay with isolated bone cells. RESULTS: Cell viability and the release of molecules affecting bone formation were higher in samples harvested by bone mill and bone scraper when compared with samples prepared by bone drilling and piezosurgery. The harvesting procedure also affected gene expression, for example, bone mill and bone scraper samples revealed significantly higher expression of growth factors such as bone morphogenetic protein-2 and vascular endothelial growth factor compared with the two other modalities. Receptor activator of nuclear factor kappa B ligand expression was lowest in bone scraper samples. CONCLUSION: These data can provide a scientific basis to better understand the impact of harvesting techniques on the number and activity of transplanted cells, which might contribute to the therapeutic outcome of the augmentation procedure.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".