Are Endogenous BMPs Necessary for Bone Healing during Distraction Osteogenesis?
Bibliographic record
Abstract
Previous reports suggest the application of exogenous BMPs can accelerate bone formation during distraction osteogenesis (DO). However, there are drawbacks associated with the use of exogenous BMPs. A possible alternative to the use of exogenous BMPs is to upregulate the expression of endogenous BMPs. Since DO results in spontaneously generated de novo bone formation in a uniform radiographic, histological, and biomechanical temporal sequence, a genetically engineered model lacking endogenous BMP2 should have measurable deficits in bone formation at different time points. We performed DO on BMP2(fl/+) and BMP2(fl/+ cre) mice using a miniature Ilizarov fixator. Distracted samples were collected at various time points and analyzed using real time-quantitative PCR, lCT, radiology, immunohistochemistry, histology, and biomechanical testing. Immunohistochemical studies of 34-day heterozygous samples showed reduced expression of BMP2, BMP7, BMPR1a, ACTR1, and ACTR2b. lCT analysis of 51-day heterozygous samples revealed a decrease in trabecular number and increase in trabecular separation. Biomechanical testing of 51-day heterozygous samples revealed decreased stiffness and increased ultimate displacement. Radiological analysis showed the heterozygotes contained a decreased bone fill score at 17, 34, and 51 days. These data suggest endogenous BMPs are important for bone healing and manipulating endogenous BMPs may help accelerate bone consolidation during DO.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".