The Double Role of Epicardial Adipose Tissue as Pro- and Anti-inflammatory Organ
Bibliographic record
Abstract
Obesity is associated with low grade inflammation. Whether this is just an adaptive response to excess adiposity to maintain a normal oxygen supply or a chronic activation of the innate immune system is still unknown. Recent research has focused on the origin of the inflammatory markers in obesity and the extent to which adipose tissue has a direct effect. The production of adipokines by visceral adipose tissue is of particular interest since their local secretion by visceral fat depots may provide a novel mechanistic link between obesity and the associated vascular complications. Growing evidences suggest that the epicardial adipose tissue, the visceral fat depot located around the heart, may locally interact with myocardium and coronary arteries. Epicardial fat is a source of adiponectin and adrenomedullin, adipokines with anti-inflammatory properties, and several proinflammatory cytokines as well as Tumor Necrosis Factor-alpha (TNF-alpha), Interleukin 1 (IL1), IL-1 h, Interleukin (IL6), Monocyte Chemoattractive Protein-1 (MCP-1), Nerve Growth Factor (NGF), resistin, Plasminogen Activator Inhibitor-1 (PAI-1), and free fatty acids. Epicardial adipose tissue could locally modulate the heart and vasculature, through paracrine secretion of pro- and anti-inflammatory cytokines, thereby playing a possible role in the adiposity-related inflammation and atherosclerosis. On the other hand, epicardial fat could exert a protective effect through adiponectin and adrenomedullin secretion as response to local or systemic metabolic or mechanical insults. Future studies will continue to provide new and fascinating insights into the double role of epicardial adipose tissue in the development of cardiovascular pathology and/or in protecting the heart and arteries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".