Carotid Inflammation Is Unaltered by Exercise in Hypercholesterolemic Swine
Bibliographic record
Abstract
INTRODUCTION: Reduction of vascular inflammation might contribute to the beneficial effects of exercise. We hypothesized that 1) exercise would reduce carotid endothelial vascular cell adhesion molecule-1 (VCAM-1) and that 2) in vivo detection of carotid inflammation can be achieved in a large animal model using contrast-enhanced ultrasound (CEU) with VCAM-1-targeted microbubbles (MBs). METHODS: Familial hypercholesterolemic (FH) swine were divided into sedentary (Sed) and exercise-trained (Ex) groups. Ex pigs underwent 16-20 wk of treadmill aerobic exercise. At the end of the study, in vivo CEU with VCAM-1-targeted MBs and assessment of endothelial-dependent dilation (EDD) were performed in carotid arteries. VCAM-1 mRNA and protein expression were compared with markers of atherosclerotic disease and health, and in vitro EDD was assessed in carotid arteries. RESULTS: Exercise training neither reduced inflammation nor improved EDD in carotid arteries of FH swine. Markers of atherosclerosis including VCAM-1 were prominent in the bifurcation compared with the proximal or distal common carotid artery and inversely associated with phosphorylated and total endothelial nitric oxide synthase. Signal intensity from VCAM-1-to-control MBs positively correlated with carotid VCAM-1 protein expression, validating our technique. CONCLUSION: These results first demonstrate that aerobic exercise has no effect on carotid endothelial inflammatory markers and EDD in FH swine. Second, our findings indicate that CEU using VCAM-1-targeted MBs can detect inflammation in vivo, providing strong foundations for longitudinal studies examining the effect of therapeutic interventions on the inflammatory status of the endothelium.
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) | 0.001 | 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".