Carotid Endothelial VCAM-1 Is an Early Marker of Carotid Atherosclerosis and Predicts Coronary Artery Disease in Swine
Bibliographic record
Abstract
Objective: The aim was to determine if endothelial VCAM-1 (eVCAM-1) expression in the common carotid artery (CCA) would correlate with predictive markers of atherosclerotic disease, would precede reduction of markers of endothelial cell function and would predict coronary artery disease (CAD). Methods and results: Carotid arterial segments (bifurcation, proximal and distal CCA) were harvested from 14 and 24 month-old male castrated familial hypercholesterolemic (FH) swine, a model of spontaneous atherosclerosis. Quantification of local expression of eVCAM-1, intimal macrophage accumulation, oxidative stress, intima-media (I/M) ratio, intima-media thickness (IMT), endothelial nitric oxide synthase (eNOS) and phosphorylated eNOS (p-eNOS) in selected regions of the carotids revealed a relationship between local inflammation and atherosclerotic plaque progression. Importantly, inflammation was not uniform throughout the CCA. Endothelial VCAM-1 expression was the greatest at the bifurcation and increased with age. Finally, eVCAM-1 best estimated the severity of CAD compared to blood levels of glucose, hypercholesterolemia, carotid IMT, and p-eNOS. Conclusion: Our data suggested that eVCAM-1 was closely associated with atherosclerotic plaque progression and preceded impairment of EDD. Thus, this study supported the use of carotid VCAM-1 targeting agents to estimate the severity of CAD.
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 | 0.000 |
| 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 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".