Skeletal site-specific effects of endurance running on structure and strength of tibia, lumbar vertebrae, and mandible in male Sprague–Dawley rats
Bibliographic record
Abstract
Bone microarchitecture, bone mineral density (BMD), and bone strength are affected positively by impact activities such as running; however, there are discrepancies in the magnitude of these effects. These inconsistencies are mainly a result of varying training protocols, analysis techniques, and whether or not the skeletal sites measured are weight bearing. This study's purpose was to determine the effects of endurance running on sites that experience different weight bearing and load. Eight-week-old male Sprague-Dawley rats (n = 20) were randomly assigned to either a group with a progressive treadmill running protocol (25 m/min for 1 h, incline of 10%) or a nontrained control group for 8 weeks. The trabecular structure of the tibia, lumbar vertebra (L3), and mandible and the cortical structure at the tibia midpoint were measured using microcomputed tomography to quantify bone volume fraction (i.e., bone volume divided by total volume (BV/TV)), trabecular number (Tb.N), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), and cortical thickness. BMD at the proximal tibia, lumbar vertebrae (L1-L3), and mandible was measured using dual energy X-ray absorptiometry. The tibia midpoint strength was measured by 3-point bending using a materials testing system. Endurance running resulted in superior bone structure at the proximal tibia (12% greater BV/TV (p = 0.03), 14% greater Tb.N (p = 0.01), and 19% lower Tb.Sp (p = 0.05)) but not at other sites. Contrary to our hypothesis, mandible bone structure was altered after endurance training (8% lower BV/TV (p < 0.01) and 15% lower Tb.Th (p < 0.01)), which may be explained by a lower food intake, resulting in less mechanical loading from chewing. These results highlight the site-specific effects of loading on the skeleton.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".