Statin‐induced calcification in human mesenchymal stem cells is cell death related
Bibliographic record
Abstract
Statins are widely used in clinics to lower cholesterol levels. Recently, they have been shown to positively affect bone formation and bone mass in a rat model. The aim of this study was to investigate the effect of pravastatin, simvastatin and lovastatin on the osteoblastic differentiation of human mesenchymal stem cells (MSCs) in vitro. Cell number, alkaline phosphatase (ALP) activity, matrix mineralization and gene expression pattern were determined. Pravastatin did not affect cell differentiation. Simvastatin and lovastatin enhanced bone morphogenetic protein 2 (BMP-2) mRNA levels. In contrast, ALP activity and mRNA levels were suppressed by statins, as well as the DNA content and cell activity (MTT). An increase in apoptotic events was observed at high concentrations of statins, along with high Ca-45 incorporation. Lower concentrations of statins did not increase apoptotic staining, but also failed to induce calcification. When statin-induced calcification did occur, the morphology of the deposits was very different from the conventional nodule formation; the calcium was laid down along the membranes of the rounded cells suggesting it was as a result of cell death. Our results indicate that statins are not able to differentiate human MSCs into osteoblasts and that high concentrations of statins (>1 microM) have a cytotoxic effect.
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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 | 0.001 |
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".