Extracellular vesicles derived from menstrual blood-derived mesenchymal stem cells suppress inflammatory atherosclerosis by inhibiting NF-κB signaling
Bibliographic record
Abstract
Numerous studies have highlighted the beneficial effects of mesenchymal stem cells (MSCs) in various inflammatory disorders. However, the regulatory role of MSCs in inflammatory atherosclerosis and the molecular mechanisms underlying their anti-inflammatory properties have largely remained elusive. Differential ultracentrifugation was performed to isolate extracellular vesicles (EVs) released by menstrual blood-derived mesenchymal stem cells (MenSCs). An ApoE knockout atherosclerotic animal model was employed to investigate the regulatory effect of MenSC-EVs on inflammatory atherosclerosis. miRNA microarray screening analyses were conducted to identify potential effectors in MenSC-EVs that play a key role in the suppression of atherosclerosis mediated by the EVs. We demonstrated the remarkable potential of MenSC-EVs in alleviating atherosclerosis through the NF-κB signaling pathway. miR-574-5p serves as a crucial effector molecule transported by MenSC-EVs, suppressing endothelial inflammation and promoting nitric oxide production. This regulation contributes to the attenuation of atherosclerosis by regulating the abundance of c-Rel. The miR-574-5p/c-Rel axis shows significant clinical relevance to atherosclerosis. This study reveals that the engineering of EVs derived from MenSCs holds significant promise as a strategic clinical approach for addressing inflammatory atherosclerosis. • MenSC-derived EVs possess an intrinsic ability to suppress inflammatory atherosclerosis. • miR-574 knockout clearly promotes atherosclerotic plaque formation. • The MenSC-EVs lacking miR-574-5p exhibit a diminished therapeutic effect on inflammatory atherosclerosis. • miR-574-5p negatively regulates NF-κB signaling through the posttranscriptional degradation of c-Rel.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".