Exercise Does Not Attenuate Early CAD Progression in a Pig Model
Bibliographic record
Abstract
PURPOSE: This study was designed to examine the effects of high-fat (HF) diet and subsequent exercise training (Ex) on coronary arteries of an animal model of early stage CAD. We hypothesized that HF diet would induce early stage disease and promote a proatherogenic coronary phenotype, whereas Ex would blunt disease progression and induce a healthier anti-inflammatory environment reflected by the increased expression of antioxidant capacity and the decreased expression of inflammatory markers in both the macrovasculature and the microvasculature of the coronary circulation. METHODS: Immunohistochemistry in left anterior descending and right coronary arteries and immunoblots in left anterior descending and left ventricular arterioles were used to characterize the effects of HF diet and Ex on the progression of coronary atherosclerosis. RESULTS: Our results revealed that HF diet promoted a proatherogenic coronary endothelial cell phenotype as evidenced by the endothelial expression of inflammatory and oxidative stress markers. Ex did not significantly alter any of these immunohistochemical markers in conduit arteries; however, Ex did increase antioxidant protein content in left ventricular arterioles. CONCLUSIONS: We conclude that, at this early stage of CAD, Ex did not seem to modify vascular cell phenotypes of conduit coronary arteries from proatherogenic to a more favorable antiatherogenic status; however, Ex increased antioxidant protein content in coronary arterioles. These findings also support the idea that endothelial phenotype expression follows different patterns in the macrovasculature and microvasculature of the coronary circulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".