Resveratrol As Anti-Aging Therapy for Age-Related Bone Loss
Bibliographic record
Abstract
INTRODUCTION: Previous studies have indicated that resveratrol, a natural phytoestrogen, can act as an anti-aging therapy to resist age-related changes of several body tissues. However, the anti-aging effects of resveratrol on bone have been poorly investigated in this natural aging population. Accordingly, this study was design to evaluate the effects of resveratrol on bone mass and biomechanical properties in old rat femora. METHODS: Twenty 22-month-old male Wistar rats were divided into two randomly assigned groups (n=10). The first group was treated for 10 weeks with resveratrol (10 mg/kg per day) and the second group was left untreated (control). Rat femora were collected. Bone mass and bone microestructure were investigated by microcomputed tomography and histomorphometry. Biomechanical properties were determined by a three-point bending test. Plasma levels of CTX (carboxy-terminal telopeptide of type I collagen) and osteocalcin were also determined. Statistical analyses were performed by a Student two-tailed unpaired t-test. In all experiments, a value of p<0.05 was considered significant. RESULTS: Microcomputed tomography analyses demonstrated that resveratrol-treated rats had significant higher bone volume, bone trabecular number, and cortical thickness and lower spacing between trabeculae in comparison to the control group. Histomorphometric analyses confirmed the increase of bone volume in resveratrol-treated rats compared to controls. Resveratrol-treated rats had significant higher bone flexural modulus, stiffness, and ultimate load compared to control group. Treatment was not associated with changes in plasma CTX or osteocalcin. CONCLUSION: These findings demonstrate that resveratrol increases bone microstructure and bone mechanical properties in old male rats, suggesting that resveratrol might be used as anti-aging therapy to resist age-induced bone loss.
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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) | 0.001 | 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) | 0.002 | 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".