miR-10a rejuvenates aged human mesenchymal stem cells and improves heart function after myocardial infarction through KLF4
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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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 | 0.001 |
| 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".