Rest insertion combined with high-frequency loading enhances osteogenesis
Bibliographic record
Abstract
Mechanical loading can significantly affect skeletal adaptation. High-frequency loading can be a potent osteogenic stimulus. Additionally, insertion of rest periods between consecutive loading bouts can be a potent osteogenic stimulus. Thus we investigated whether the insertion of rest-periods between short-term high-frequency loading bouts would augment adaptation in the mature murine skeleton. Right tibiae of skeletally mature (16 wk) female C57BL/6 mice were loaded in cantilever bending at peak of 800 microepsilon, 30 Hz, 5 days/wk for 3 wk. Left tibiae were the contralateral control condition. Mice were randomly assigned into one of two groups: continuous high-frequency (CT) stimulation for 100 s (n = 9), or 1-s pulses of high-frequency stimuli followed by 10 s of rest (RI) for 100 s (n = 9). Calcein labels were administered on days 1 and 21; label incorporation was used to histomorphometrically assess periosteal and endosteal indexes of adaptation. Periosteal surface referent bone formation rate (pBFR/BS) was significantly enhanced in CT (>88%) and RI (>126%) loaded tibiae, relative to control tibiae. Furthermore, RI tibiae had significantly greater pBFR/BS, relative to CT tibiae (>72%). The endosteal surface was not as sensitive to mechanical loading as the periosteal surface. Thus short-term high-frequency loading significantly elevated pBFR/BS, relative to control tibiae. Furthermore, despite the 10-fold reduction in cycle number, the insertion of rest periods between bouts of high-frequency stimuli significantly augmented pBFR/BS, relative to tibiae loaded continually. Optimization of osteogenesis in response to mechanical loading may underpin the development of nonpharmacological regiments designed to increase bone strength in individuals with compromised bone structures.
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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 | 0.001 |
| 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".