Diabetes affects extracellular vesicle content and function
Bibliographic record
Abstract
Introduction: The role of circulating extracellular vesicles in cardiovascular diseases remains incompletely understood.We previously demonstrated that extracellular vesicles circulating in plasma of healthy mice can suppress monocyte activation through transfer of anti-inflammatory microRNAs. Here we set out to determine the effect of diabetes on the function of plasma extracellular vesicles since diabetes is known to negatively affect vascular function, playing a contributory role in cardiovascular diseases. Methods: Circulating plasma extracellular vesicles were isolated from mouse and rat models of type 2 diabetes. Extracellular vesicles were characterised with nanoparticle tracking analysis. Furthermore, qPCR and RNA-sequencing approaches were used to characterise vesicle content and function. Results: We found that vesicle abundance and size were increased in mouse and rat models of type 2 diabetes. MicroRNAs in plasma extracellular vesicles were dysregulated during the progression of diabetes in thesemodels. Finally, we demonstrate that vesicles isolated from diabetic plasma can activate inflammatory pathways in endothelial cells. Current studies are seeking to determine the contribution of microRNA transfer to endothelial dysfunction. Conclusions: These studies suggest that the microRNA content and function of extracellular vesicles are dysregulated during diabetes. Advancements in this area could facilitate the development of more effective non-invasive diagnostics, prognostics, and therapeutics. Funding: Supported by funding from the Canadian Vascular Network and the Canadian Institutes of Health Research.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".