Melatonin Improves the Resistance of Oxidative Stress‐Induced Cellular Senescence in Osteoporotic Bone Marrow Mesenchymal Stem Cells
Bibliographic record
Abstract
Accumulation of senescent bone marrow‐derived mesenchymal stem cells (BMMSCs) has led to an age‐related bone loss. However, the role of stem cell senescence in estrogen deficiency‐induced osteoporosis remains elusive. Though melatonin plays a vital role in bone metabolism regulation, the underlying mechanisms of melatonin‐mediated antiosteoporosis are partially elucidated. Therefore, this study purposed to explore (1) whether estrogen deficiency causes cellular senescence of BMMSCs, and if so, (2) the potential of melatonin in preventing bone loss via senescence signaling inhibition. BMMSCs derived from ovariectomized (OVX) rats (OVX BMMSCs) showed an impaired osteogenic capacity, albeit having comparable levels of senescence biomarkers than the sham cells. When exposed to low levels of hydrogen peroxide (H2O2), OVX BMMSCs rapidly exhibited senescence‐associated phenotypes such as the increased activity of senescence‐associated β‐galactosidase (SA‐β‐gal) and upregulation of cell cycle inhibitors. Notably, the in vitro treatment with melatonin hindered H2O2‐induced senescence in OVX BMMSCs and restored their osteogenic capacity. Treatment with either SIRT1 inhibitor (sirtinol) or melatonin receptor antagonists (luzindole and 4‐P‐PDOT) eliminated melatonin protective effects, thus indicating its potential in preventing stem cell senescence via SIRT1 activation through the melatonin membrane receptors. Following in vivo intravenous administration with melatonin, it successfully protected the bone microstructure and preserved the antisenescence property of BMMSCs in OVX rats. Collectively, our findings demonstrated that melatonin protected against estrogen deficiency‐related bone loss by improving the resistance of BMMSCs to cellular senescence. Therefore, melatonin‐mediated antisenescence effect on stem cells provides vital information to facilitate the development of a novel and effective strategy for treating postmenopausal OP.
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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 | 0.000 | 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 | 0.001 |
| 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".