Mesenchymal stem cell-derived small extracellular vesicles mitigate oxidative stress-induced senescence in endothelial cells via regulation of miR-146a/Src
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".