High Impact Exercise Improves Bone Microstructure and Strength in Growing Rats
Bibliographic record
Abstract
Physical activity is beneficial for skeletal development. However, impact sports during adolescence, leading to bone growth retardation and/or bone quality improvement, remains unexplained. This study investigated the effects of in vivo low (LI), medium (MI), and high (HI) impact loadings applied during puberty on bone growth, morphometry and biomechanics using a rat model. 4-week old rats (n = 30) were divided into control, sham, LI, MI, and HI groups. The impact was applied on the right tibiae, 5 days/week for 8 weeks mimicking walking (450 µε), uphill running (850 µε) and jumping (1250 µε) conditions. Trabecular and cortical parameters were determined by micro-CT, bone growth rate by calcein labeling and toluidine blue staining followed by histomorphometry. Bio-mechanical properties were evaluated from bending tests. HI group reduced rat body weight and food consumption compared to shams. Bone growth rate also decreased in MI and HI groups despite developing thicker hypertrophic and proliferative zone heights. HI group showed significant increment in bone mineral density, trabecular thickness, cortical and total surface area. Ultimate load and stiffness were also increased in MI and HI groups. We conclude that impact loading during adolescence reduces bone growth moderately but improves bone quality and biomechanics at the end of the growing period.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".