Intravenous immunoglobulin and salicylate differentially modulate pathogenic processes leading to vascular damage in a model of Kawasaki disease
Bibliographic record
Abstract
OBJECTIVE: Kawasaki disease (KD) is a multisystem vasculitis affecting children and is characterized by immune activation in the acute stage of disease. Systemic inflammation eventually subsides, although coronary arteritis persists, resulting in aneurysm formation. KD is the leading cause of acquired heart disease among children in North America. Accepted treatment guidelines include high-dose intravenous immunoglobulin (IVIG) and aspirin in the acute phase. Although this therapy is effective, the cellular and molecular mechanisms involved are not clear. The aim of this study was to examine the effect of IVIG and salicylate at each stage of disease development. METHODS: Using a murine model of KD, we established and validated several in vitro techniques to reflect 3 key steps involved in disease pathogenesis, as follows: thymidine incorporation to evaluate T cell activation, enzyme-linked immunosorbent assay to measure tumor necrosis factor alpha (TNFalpha) production, and real-time polymerase chain reaction to examine TNFalpha-mediated expression of matrix metalloproteinase 9 (MMP-9). RESULTS: At therapeutic concentrations, IVIG, but not salicylate, effectively reduced the immune response leading to TNFalpha expression. Unexpectedly, pharmacologic doses of salicylate were not able to inhibit TNFalpha production and in fact enhanced its production. Neither drug directly regulated MMP-9 expression but did so only indirectly via modulating TNFalpha. TNFalpha activity was a prerequisite for local expression of MMP-9 at the coronary artery. CONCLUSION: Therapeutic concentrations of IVIG and salicylate differentially modulate the expression of TNFalpha and its downstream effects. Further dissection of the biologic effects of aspirin in acute KD is necessary for the rational design of therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".