Inhibition of matrix metalloproteinase-9 activity improves coronary outcome in an animal model of Kawasaki disease
Bibliographic record
Abstract
Kawasaki disease (KD) is the leading cause of acquired heart disease of children in North America. It is characterized by a massive immune activation and multi-system vasculitis, which evolves into a site-specific inflammatory response focused at the coronary arteries. Coronary artery (CA) inflammation leads to elastin breakdown, destruction of the vessel wall and aneurysm formation. We have demonstrated recently the pivotal role of tumour necrosis factor (TNF)-alpha-mediated matrix metalloproteinase (MMP)-9 activity in the pathogenesis of elastin breakdown in a murine model of KD, Lactobacillus casei cell wall extract-induced coronary arteritis. Using this model, we evaluated the in vitro effects of doxycycline, an antibiotic with MMP inhibitory function, in modulating key pathogenic stages of disease leading to CA damage. Doxycycline inhibits T cell activation and TNF-alpha production in peripheral immune cells, as assessed by thymidine incorporation and a TNF bioassay respectively. Additionally, doxycycline inhibits directly MMP-9 enzymatic activity derived from TNF-alpha-stimulated vascular smooth muscle cells as assayed by zymography. More importantly, in vivo treatment of Lactobacillus casei cell wall extract (LCWE)-injected mice with doxycycline reduces significantly the incidence of CA elastin breakdown and reduces loss of elastin. Therefore, doxycycline can mitigate TNF-alpha-induced MMP-9-mediated coronary elastin breakdown and improve coronary outcome. Agents with the ability to inhibit both inflammation and the downstream effects of inflammation, such as MMP-9 activity, offer a promising therapeutic strategy for the management of children with KD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".