Circulating Extracellular Vesicles as Putative Mediators of Cardiovascular Disease in Paediatric Chronic Kidney Disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD). However, the pathogenesis of CVD in CKD remains incompletely understood. Endothelial extracellular vesicles (EC-EVs) have previously been associated with CVD. We hypothesized that CKD alters EV release and cargo, subsequently promoting vascular remodelling. We recruited 94 children with CKD, including patients after kidney transplantation and healthy donors, and performed EV phenotyping and functional EV analyses in the absence of age-related comorbidities. Plasma EC-EVs were increased in haemodialysis patients and decreased after kidney transplantation. Thirty microRNAs were less abundant in total CKD plasma EVs with predicted importance in angiogenesis and smooth muscle cell proliferation. In vitro, CKD plasma EVs induced transcriptomic changes in angiogenesis pathways and functionally impaired angiogenic properties, migration and proliferation in ECs. High shear stress, as generated by arterio-venous fistulas, and uremic toxins were considered as potential drivers of EV release, but only the combination increased EV generation from venous ECs. The resulting EVs recapitulated miRNA changes observed in CKD in vivo. In conclusion, CKD results in the release of EVs with altered miRNA profiles and anti-angiogenic properties, which may mediate vascular pathology in children with CKD. EVs and their miRNA cargo may represent future therapeutic targets to attenuate CVD in CKD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".