Exercise improves load bearing bone structural properties in female secreted protein acidic and rich in cysteine (SPARC) null mice but not in males
Bibliographic record
Abstract
Abstract Secreted protein acidic and rich in cysteine (SPARC) is the most abundant glycoprotein in bone and is thought to play a critical role in bone remodeling and homeostasis. However, the effect of SPARC in relation to gender and exercise on bone quality is not well understood. The purpose of this study was to quantify differences in the structural and biomechanical properties between calvarial and femoral bone from male and female wild‐type (WT) and SPARC null (SPARC(−/−)) mice as well as the ability of exercise to rescue bone health. Male and female WT and transgenic SPARC(−/−) mice were given either a fixed or rotating running wheel for exercise. Bone structural, biomechanical, and morphological parameters were quantified using micro computed tomography, push out testing for the calvaria, three‐point flexural testing for the femurs, histological and immunofluorescent staining. Similar reductions in structural and biomechanical strength were observed in both male and female SPARC(−/−) calvaria, most of which were not significantly affected by exercise. In femurs, SPARC(−/−) had a significant effect on structural parameters in both sexes, but was more pronounced in females with some properties being rescued with running. Interestingly, the effect of SPARC(−/−) on bone mineral density was only detected in female SPARC(−/−) mice, not males, and was subsequently rescued with exercise. This study emphasizes the differences between sexes in WT and SPARC(−/−) mice in regard to structural parameters and biomechanical properties. Research into gender differences can help inform and personalize treatment options to more accurately meet patient needs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".