Mesenchymal stem cell-derived small extracellular vesicles mitigate oxidative stress-induced senescence in endothelial cells via regulation of miR-146a/Src
Why this work is in the frame
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Bibliographic record
Abstract
Senescent endothelial cells (ECs) could impair the integrity of the blood vessel endothelium, leading to vascular aging and a series of diseases, such as atherosclerosis, diabetes. Preventing or mitigating EC senescence might serve as a promising therapeutic paradigm for these diseases. Recent studies showed that small extracellular vesicles (sEV) have the potential to transfer bioactive molecules into recipient cells and induce phenotypic changes. Since mesenchymal stem cells (MSCs) have long been postulated as an important source cell in regenerative medicine, herein we investigated the role and mechanism of MSC-derived sEV (MSC-sEV) on EC senescence. In vitro results showed that MSC-sEV reduced senescent biomarkers, decreased senescence-associated secretory phenotype (SASP), rescued angiogenesis, migration and other dysfunctions in senescent EC induced by oxidative stress. In the In vivo natural aging and type-2 diabetes mouse wound-healing models (both of which have senescent ECs), MSC-sEV promoted wound closure and new blood vessel formation. Mechanically, miRNA microarray showed that miR-146a was highly expressed in MSC-sEV and also upregulated in EC after MSC-sEV treatment. miR-146a inhibitors abolished the stimulatory effects of MSC-sEV on senescence. Moreover, we found miR-146a could suppress Src phosphorylation and downstream targets VE-cadherin and Caveolin-1. Collectively, our data indicate that MSC-sEV mitigated endothelial cell senescence and stimulate angiogenesis through miR-146a/Src.
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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 | 0.000 | 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) | 0.000 | 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