M-CSF accelerates orthodontic tooth movement by targeting preosteoclasts in mice
Bibliographic record
Abstract
OBJECTIVE: To test the use of macrophage colony-stimulating factor (M-CSF), an early osteoclast recruitment/differentiation factor, in increasing the rate of osteoclastic recruitment and differentiation as a means of accelerating tooth movement. MATERIALS AND METHODS: The distribution of osteoclasts and their precursors in the periodontal ligament (PDL) of teeth was initially characterized in a mouse model by immunohistochemical expression analyses of markers of osteoclast differentiation. We next administered two different dosages of M-CSF in the PDL of molars subject to force. Tooth movement was measured and correlated with changes in expression of M-CSF downstream genes in the PDL. RESULTS: We found that monocytes may have differentiated into preosteoclasts before being recruited to the PDL during the lag phase of tooth movement, and an influx of multinucleated osteoclasts occurred after 6 days. The lower dose of M-CSF was found to be most effective in increasing the amount of tooth movement and expression of M-CSF downstream genes and TRAP, an osteoclast marker. In contrast, administration of a higher dose of M-CSF resulted in a decrease in the expression of one gene downstream of M-CSF and possible inhibition of osteoclast formation. CONCLUSIONS: Exogenous administration of optimal dosages of M-CSF to orthodontically moved teeth provides potential for clinical studies in accelerating tooth movement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".