Free-fall landing and interval running have different effects on trabecular bone mass and microarchitecture, serum osteocalcin, biomechanical properties, <i>SOST</i> expression and on osteocyte-related characteristics
Bibliographic record
Abstract
The effects of treadmill interval training (IT) and free-fall exercise were evaluated on bone parameters including osteocyte related characteristics. Thirty-eight 4-month-old male Wistar rats were randomly divided into a control (C) group and exercise groups: IT, 10 free-fall impacts/day with a 10-s (FF10) or 20-s interval between drops (FF20), 5 days/week, for 9 weeks. We assessed bone mineral density (BMD); microarchitecture by µCT; mechanical strength by a 3-point bending test; density and occupancy of the osteocyte lacunae by toluidine blue staining; osteocalcin and NTx systemic levels by ELISA; and bone tissue Sost messenger RNA (mRNA) expression by RT-PCR. NTx levels were significantly lower in exercise groups as compared with the C group. In exercise groups the Sost mRNA expression was significantly lower than in C. Tb.N was significantly higher for IT and FF20 compared with the C group. Tb.Sp was significantly lower in FF10 compared with the C group. Both IT and FF20 were associated with higher tibial lacunar density as compared with FF10. compared with FF10, IT fat mass was lower, while tibial osteocyte lacunae occupancy and systemic osteocalcin level were higher. All exercise modes were efficient in reducing bone resorption. Both IT and free-fall impact with appropriate recovery periods, which may be beneficial for bone health and osteocyte-related characteristics. Novelty: Interval training is beneficial for bone mineral density. Exercises decreased both bone resorption and inhibition of bone formation (Sost mRNA). Longer interval recovery time favors osteocyte lacunae density.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".