Perivascular Adipose Tissue Inflammation and Coronary Artery Disease: An Autopsy Study Protocol
Bibliographic record
Abstract
BACKGROUND: Perivascular adipose tissue (PAT) inflammation may have a role in coronary artery disease (CAD) pathophysiology. However, most evidence has come from samples obtained during surgical procedures that may imply in some limitations. Moreover, the role of B lymphocytes and inflammation in PAT that is adjacent to unstable atheroma plaques has not been investigated in humans using morphometric measurements. OBJECTIVE: The objective of this study is to investigate the inflammation in PAT, subcutaneous, and perirenal adipose tissues (SAT and PrAT) among chronic CAD, acute CAD, and control groups in an autopsy study. METHODS: Heart, SAT, and PrAT samples are collected from autopsied subjects in a general autopsy service, with the written informed consent of the next-of-kin (NOK). Sociodemographic and clinical data are obtained from a semistructure interview with the NOK. Coronary arteries are dissected and PAT are removed. Sections with the greatest arterial obstruction or unstable plaques, and the local with absence of atherosclerosis in all coronary arteries are sampled. PAT are represented adjacent to these fragments. Adipose tissues are fixed in 4% buffered paraformaldehyde solution and analyzed immunohistochemically for macrophages (CD68), macrophage polarization (CD11c for proinflammatory and CD206 for anti-inflammatory), B lymphocytes (CD20), and T lymphocytes (CD3). Slides will be scanned, and inflammatory cells will be quantified in 20 random fields. Participants will be categorized in CAD groups, after morphometric measurement of arterial obstruction and plaque composition analysis in accordance with American Heart Association classification. Three study groups will be investigated: acute CAD (at least one unstable plaque); chronic CAD (≥50% arterial obstruction); and controls (<50% arterial obstruction). Inflammatory cells in PAT, SAT, and PrAT will be counted and compared between groups using multivariate linear regression, adjusted for age, body mass index, hypertension, diabetes, alcohol use, and smoking. RESULTS: We present the methods of our study that was developed from 2 pilots. Currently, data collection and tissue processing are ongoing. Data collection, histology and immunochemistry procedures, and quantification of all inflammatory cells are expected to be concluded within 1 year. CONCLUSIONS: This study will contribute for the understanding of the mechanisms of CAD pathophysiology because it will help to clarify the role of inflammation both in chronic and acute CAD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".