Cortical and trabecular morphology is altered in the limb bones of mice artificially selected for faster skeletal growth
Bibliographic record
Abstract
Bone strength is influenced by mineral density and macro- and microstructure. Research into factors that contribute to bone morphology and strength has focused on genetic, environmental and morphological factors (e.g., body mass index), but little is known regarding the impact of rates of skeletal elongation on adult skeletal morphology and strength. Using micro-CT, we examined the impact of rates of skeletal elongation on bone cortical and trabecular morphology, and on rates of estrogen-dependent bone loss in the tibia in CD-1 mice, and in mice with accelerated skeletal growth (Longshanks). Groups of adult mice (n = 7/group) were subjected to ovariectomy or sham surgeries, scanned for 6 weeks, and indices of bone morphology were collected. Results show that Longshanks mice had significantly less trabecular bone at skeletal maturity, characterized by fewer, thinner trabeculae, and furthermore lost trabecular bone more slowly in response to ovariectomy. Artificial selection for rapid skeletal growth relative to somatic growth thus had a significant impact on trabecular bone morphology in Longshanks. Our data do not unequivocally demonstrate a causal relationship between rapid bone growth and reduced trabecular bone quality, but suggest that rapid linear bone growth may influence the risk of cancellous bone fragility.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".