Myocardial glucose suppression may interfere with the detection of inflammatory cells with FDG-PET as suggested in a canine model of myocardial infarction
Bibliographic record
Abstract
BACKGROUND: After myocardial infarction, fibrosis and an ongoing dysregulated inflammatory response have been shown to lead to adverse cardiac remodeling. FDG PET is an imaging modality sensitive to inflammation as long as suppression protocols are observed while gadolinium enhanced MRI can be used to determine extracellular volume (ECV), a measure of fibrosis. In patients, glucose suppression is achieved variously through a high fat diet, fasting and injection of heparin. To emulate this process in canines, a heparin injection and lipid infusion are used, leading to similar fatty acids in the blood. The aim of this study was to examine the effect of glucose suppression on the uptake of FDG in the infarcted myocardial tissue and also on the determination of ECV in both the infarcted tissue and in the myocardium remote to the zone of infarction during a long constant infusion of FDG and Gd-DTPA. RESULTS: Extracellular volume was affected neither by suppression nor the length of the constant infusion in remote and infarcted tissue. Metabolic rate of glucose in infarcted tissue decreased during and after suppression of glucose uptake by lipid infusion and heparin injection. An increase in fibrosis and inflammatory cells was found in the center of the infarct as compared to remote tissue. CONCLUSION: The decrease in the metabolic rate of glucose in the infarcted tissue suggests that inflammatory cells may be affected by glucose suppression through heparin injection and lipid infusion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".