Genetic predisposition to low bone mass is paralleled by an enhanced sensitivity to signals anabolic to the skeleton
Bibliographic record
Abstract
The structure of the adult skeleton is determined, in large part, by its genome. Whether genetic variations may influence the effectiveness of interventions to combat skeletal diseases remains unknown. The differential response of trabecular bone to an anabolic (low-level mechanical vibration) and a catabolic (disuse) mechanical stimulus were evaluated in three strains of adult mice. In low bone-mineral-density C57BL/6J mice, the low-level mechanical signal caused significantly larger bone formation rates (BFR) in the proximal tibia, but the removal of functional weight bearing did not significantly alter BFR. In mid-density BALB/cByJ mice, mechanical stimulation also increased BFR, whereas disuse significantly decreased BFR. In contrast, neither anabolic nor catabolic mechanical signals influenced any index of bone formation in high-density C3H/HeJ mice. Together, data from this study indicate that the sensitivity of trabecular tissue to both anabolic and catabolic stimuli is influenced by the genome. Extrapolated to humans, these results may explain in part why prophylaxes for low bone mass are not universally effective, yet also indicate that there may be a genotypic indication of people who are at reduced risk of suffering from bone loss.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".