Endurance training attenuates loss of bone strength in the polymerase gamma mutator mouse model of aging
Bibliographic record
Abstract
A causal role for mtDNA mutagenesis in aging‐associated bone loss is supported by recent studies demonstrating that the polymerase gamma (PolG) mutator mouse, harbouring a proofreading‐deficient copy of PolG, exhibits a significant reduction in whole‐body bone mineral content and density. Physiological aging is accompanied by kyphosis, osteoporosis and an increased incidence of fractures. Anecdotal evidence from human and rodent studies suggests that endurance training prevents loss of bone mineral density, mobility and posture, and is recommended to reduce the likelihood of falling and associated morbidity and mortality. We investigated whether endurance training can prevent bone strength decrements in PolG mice. At 3 months of age, 24 PolG mice (♀ = ♂) were randomly assigned to a sedentary (SED) or forced‐endurance training (END; 15m/min for 45 min, 3x/week for 5 months) group. PolG‐SED mice exhibited a marked decrease in maximal load (33%), maximal energy (39%) and stress (33%) vs. wild‐type (WT) controls (P<0.01) with a trend toward decreased stiffness (19%) in the femur (P<0.07) and bone area (9%) at the mid‐diaphysis (P<0.08). PolG‐END showed an improvement in all structural parameters (P<0.01) returning to WT levels. We found no differences in femur length, periosteal and endosteal area at the mid‐diaphysis. Five months of endurance training rescued the loss of femur strength in PolG mice. (Funded by CIHR)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".