Strain rate influences periosteal adaptation in mature bone
Bibliographic record
Abstract
Mechanical forces influence bone form and function. Although the adaptive capabilities of bone are well known, the nuances of the mechanical stimuli regulating adaptation remain elusive. Recently, it was suggested that strain rate influences bone adaptation, and impact exercises with high strain rates during growth may be more osteogenic than low impact aerobic exercises. Building on those findings, we hypothesized that higher rates of mechanical loading would evoke greater adaptive responses than lower rates of loading in mature bone. To test that hypothesis, skeletally mature (16 weeks) female C57BL/6 mice underwent non-invasive exogenous cantilever bending of the right tibia with a 1 Hz trapezoidal waveform for 60 s, 5 days per week, for 4 weeks. Loading was calibrated (strain gauge) to induce peak magnitudes of 1000 microepsilon on the lateral tibial middiaphysis. Mice were randomly assigned to three groups based on strain rate of the applied load: low (0.004 s(-1); n = 14), medium (0.020 s(-1); n = 15), and high (0.100 s(-1); n = 14). Calcein injections (i.p., 10 mg kg(-1)) permitted histomorphometric analyses of bone formation. Loading significantly enhanced periosteal mineral apposition rate (MAR), mineralizing surface (MS), and bone formation rate (BFR BS(-1)) in all three strain rate groups, relative to control tibiae. Furthermore, a graded dose-response relation was observed between the applied strain rate and periosteal BFR BS(-1). These increases in MAR, MS, and BFR BS(-1) were not seen on the endosteal surface. Endosteal adaptation was not statistically different between loaded and control tibiae in most endosteal indices of bone adaptation. Moreover, endosteal adaptation did not increase with strain rate. Understanding the nature of the stimuli to which bone cells respond to may underpin the development of non-pharmacological treatments devised to enhance bone mass.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".