Presence of IFN-γ Does Not Indicate Its Necessity for Induction of Coronary Arteritis in an Animal Model of Kawasaki Disease
Bibliographic record
Abstract
Kawasaki disease is the most common cause of vasculitis affecting children, and the leading cause of acquired heart disease in the developed world. To date, studies on the role of IFN-gamma in the pathogenesis of Kawasaki disease have focused on peripheral production of IFN-gamma, and have yielded conflicting results. Affected heart tissue is not available from children with Kawasaki disease. In this study, we use an animal model of Kawasaki disease, Lactobacillus casei cell wall extract (LCWE)-induced coronary arteritis, to examine the role of IFN-gamma in the development of coronary artery lesions. We report the presence of IFN-gamma, both at the mRNA and protein levels, in the affected vessels. Its biphasic expression, first at days 3-7 and again at days 28-42 post-LCWE injection, corresponds to the first appearance of inflammatory infiltrate in coronary arteries, and later to vascular wall disruption and aneurysm formation, respectively. Interestingly, ablation of IFN-gamma expression did not dampen the inflammatory response, and IFN-gamma-deficient lymphocytes proliferated more vigorously in response to LCWE than those of wild-type animals. Of more importance, the incidence of coronary arteritis was the same in IFN-gamma-deficient and wild-type mice. Taken together, our findings demonstrate that IFN-gamma regulates the immune response during development of coronary arteritis, but is not required for the induction of coronary artery 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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".