Gene expression and 18FDG uptake in atherosclerotic carotid plaques
Bibliographic record
Abstract
PURPOSE: Metabolic assessment of vascular inflammation by 2-[F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG)-PET is a promising new approach for the evaluation of the vulnerability of atherosclerotic plaques. Quantitative real-time PCR allows measurement of gene expression of markers of atherosclerotic plaque vulnerability. These techniques were applied in advanced atherosclerotic disease to relate metabolism and inflammatory activity to the gene expression profile of the vulnerable atherosclerotic plaque. METHODS: Seventeen patients with clinical symptoms of cerebral vascular events (<3 months) and an additional ipsilateral internal carotid artery stenosis of greater than 60% were recruited. FDG uptake in the carotids was determined by PET/computed tomography and expressed as mean and maximal standardized uptake values (SUVmean and SUVmax). The atherosclerotic plaques were subsequently recovered by carotid endarterectomy. The gene expression of markers of vulnerability - CD68, IL-18, matrix metalloproteinase 9, cathepsin K, GLUT-1, and hexokinase type II (HK2) - were measured in plaques by quantitative PCR. RESULTS: In a multivariate linear regression model, GLUT-1, CD68, cathepsin K, and HK2 gene expression remained in the final model as predictive variables of FDG accumulation calculated as SUVmean (R=0.26, P<0.0001). In addition, a multivariate linear regression model found GLUT-1, CD68, cathepsin K, and HK2 gene expression as independent predictive variables of FDG accumulation calculated as SUVmax (R=0.30, P<0.0001). CONCLUSION: GLUT-1, HK2, CD68, and cathepsin K remained in both multivariate models and thus provided independent information regarding FDG uptake. We suggest that FDG uptake is a composite indicator of macrophage load, overall inflammatory activity and collagenolytic plaque destabilization. Accordingly, FDG-PET could prove to be an important predictor of cerebrovascular events in patients with carotid plaques.
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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 | 0.000 | 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 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".