The Transcriptome of Human Epicardial, Mediastinal and Subcutaneous Adipose Tissues in Men with Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: The biological functions of epicardial adipose tissue (EAT) remain largely unknown. However, the proximity of EAT to the coronary arteries suggests a role in the pathogenesis of coronary artery disease (CAD). The objectives of this study were to identify genes differentially regulated among three adipose tissues, namely EAT, mediastinal (MAT) and subcutaneous (SAT) and to study their possible relationships with the development of cardiovascular diseases. METHODS AND RESULTS: Samples were collected from subjects undergoing coronary artery bypass grafting surgeries. Gene expression was evaluated in the three adipose depots of six men using the Illumina® HumanWG-6 v3.0 expression BeadChips. Twenty-three and 73 genes were differentially up-regulated in EAT compared to MAT and SAT, respectively. Ninety-four genes were down-regulated in EAT compared to SAT. However, none were significantly down-regulated in EAT compared to MAT. More specifically, the expression of the adenosine A1 receptor (ADORA1), involved in myocardial ischemia, was significantly up-regulated in EAT. Levels of the prostaglandin D2 synthase (PTGDS) gene, recently associated with the progression of atherosclerosis, were significantly different in the three pairwise comparisons (EAT>MAT>SAT). The results of ADORA1 and PTGDS were confirmed by quantitative real-time PCR in 25 independent subjects. CONCLUSIONS: Overall, the transcriptional profiles of EAT and MAT were similar compared to the SAT. Despite this similarity, two genes involved in cardiovascular diseases, ADORA1 and PTGDS, were differentially up-regulated in EAT. These results provide insights about the biology of EAT and its potential implication in CAD.
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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) | 0.000 | 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".