TNF-α Is Necessary for Induction of Coronary Artery Inflammation and Aneurysm Formation in an Animal Model of Kawasaki Disease
Bibliographic record
Abstract
Kawasaki disease is the most common cause of multisystem vasculitis in childhood. The resultant coronary artery lesions make Kawasaki disease the leading cause of acquired heart disease in children in the developed world. TNF-alpha is a pleiotropic inflammatory cytokine elevated during the acute phase of Kawasaki disease. In this study, we report rapid production of TNF-alpha in the peripheral immune system after disease induction in a murine model of Kawasaki disease. This immune response becomes site directed, with migration to the coronary arteries dependent on TNF-alpha-mediated events. Production of TNF-alpha in the heart is coincident with the presence of inflammatory infiltrate at the coronary arteries, which persists during development of aneurysms. More importantly, inflammation and elastin breakdown in the coronary vessels are completely eliminated in the absence of TNF-alpha effector functions. Mice treated with the TNF-alpha-blocking agent etanercept, as well as TNFRI knockout mice, are resistant to development of both coronary arteritis and coronary aneurysm formation. Taken together, TNF-alpha is necessary for the development of coronary artery lesions in an animal model of Kawasaki disease. These findings have important implications for potential new therapeutic interventions in children with Kawasaki disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".