miR-10a rejuvenates aged human mesenchymal stem cells and improves heart function after myocardial infarction through KLF4
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Bibliographic record
Abstract
BACKGROUND: Aging is one of the key factors that regulate the function of human bone marrow mesenchymal stem cells (hBM-MSCs) and related changes in microRNA (miRNA) expression. However, data reported on aging-related miRNA changes in hBM-MSCs are limited. METHODS: We demonstrated previously that miR-10a is significantly decreased in aged hBM-MSCs and restoration of the miR-10a level attenuated cell senescence and increased the differentiation capacity of aged hBM-MSCs by repressing Krüpple-like factor 4 (KLF4). In the present study, miR-10a was overexpressed or KLF4 was downregulated in old hBM-MSCs by lentiviral transduction. The hypoxia-induced apoptosis, cell survival, and cell paracrine function of aged hBM-MSCs were investigated in vitro. In vivo, miR-10a-overexpressed or KLF4-downregulated old hBM-MSCs were implanted into infarcted mouse hearts after myocardial infarction (MI). The mouse cardiac function of cardiac angiogenesis was measured and cell survival of aged hBM-MSCs was investigated. RESULTS: Through lentivirus-mediated upregulation of miR-10a and downregulation of KLF4 in aged hBM-MSCs in vitro, we revealed that miR-10a decreased hypoxia-induced cell apoptosis and increased cell survival of aged hBM-MSCs by repressing the KLF4-BAX/BCL2 pathway. In vivo, transplantation of miR-10a-overexpressed aged hBM-MSCs promoted implanted stem cell survival and improved cardiac function after MI. Mechanistic studies revealed that overexpression of miR-10a in aged hBM-MSCs activated Akt and stimulated the expression of angiogenic factors, thus increasing angiogenesis in ischemic mouse hearts. CONCLUSIONS: miR-10a rejuvenated aged hBM-MSCs which improved angiogenesis and cardiac function in injured mouse hearts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it