Melatonin protects bone marrow mesenchymal stem cells against iron overload‐induced aberrant differentiation and senescence
Bibliographic record
Abstract
Bone marrow mesenchymal stem cells (BMSCs) are an expandable population of stem cells which can differentiate into osteoblasts, chondrocytes and adipocytes. Dysfunction of BMSCs in response to pathological stimuli contributes to bone diseases. Melatonin, a hormone secreted from pineal gland, has been proved to be an important mediator in bone formation and mineralization. The aim of this study was to investigate whether melatonin protected against iron overload-induced dysfunction of BMSCs and its underlying mechanisms. Here, we found that iron overload induced by ferric ammonium citrate (FAC) caused irregularly morphological changes and markedly reduced the viability in BMSCs. Consistently, osteogenic differentiation of BMSCs was significantly inhibited by iron overload, but melatonin treatment rescued osteogenic differentiation of BMSCs. Furthermore, exposure to FAC led to the senescence in BMSCs, which was attenuated by melatonin as well. Meanwhile, melatonin was able to counter the reduction in cell proliferation by iron overload in BMSCs. In addition, protective effects of melatonin on iron overload-induced dysfunction of BMSCs were abolished by its inhibitor luzindole. Also, melatonin protected BMSCs against iron overload-induced ROS accumulation and membrane potential depolarization. Further study uncovered that melatonin inhibited the upregulation of p53, ERK and p38 protein expressions in BMSCs with iron overload. Collectively, melatonin plays a protective role in iron overload-induced osteogenic differentiation dysfunction and senescence through blocking ROS accumulation and p53/ERK/p38 activation.
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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 | 0.000 | 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) | 0.001 | 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".