Pericardial Fluid–Derived Small Extracellular Vesicles from Patients with Coronary Disease Alter the Lipidome of Human Coronary Artery Endothelium
Bibliographic record
Abstract
Background: To date, we have lacked an understanding of how factors in the pericardial fluid (PF) of patients with coronary artery disease (CAD) can influence the lipidome of coronary artery endothelium. This study explores the impact of PF-derived extracellular vesicles (EVs) on the lipidome of human coronary artery endothelial cells (HCAECs) in patients with CAD. Methods: In this observational study, PF was collected from patients with CAD (n = 3) and without CAD (n = 3). PF-derived small EVs were isolated and characterized using microfluidic resistive pulse sensing. HCAECs were exposed to these EVs, and untargeted liquid chromatography-mass spectrometry was subsequently used to determine the lipidome of the HCAECs. In silico analysis was used to evaluate changes in lipid species and classes. Results: A total of 1043 lipid species were identified in untreated HCAECs and HCAECs treated with PF-derived small EVs. The predominant lipid types were glycerophospholipids, glycerolipids, and sphingolipids. Quantification of individual lipid classes showed HCAECs treated with PF EVs showed reduced summed intensities of lysophosphatidylglycerols and diacylglycerophosphoinositols compared to untreated controls. Treatment with PF EVs derived from Non-CAD patients led to an increase in sphingoid bases, whereas this effect was not observed with CAD-derived PF EVs. Both Non-CAD and CAD PF EV treatments resulted in elevated prenol lipids compared to controls. Conclusions: We identify that small EVs isolated from the PF of patients with CAD alter the lipid profile and metabolism of human coronary artery endothelium. Future studies should determine whether such changes can contribute to the pathophysiology of ischemic heart disease.
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 | 0.001 |
| 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".