HMG-CoA Reductase Inhibition Reduces T-cell Activation, TNFα Production, and MMP-9 Gene Expression in a Superantigen-mediated Mouse Model of Kawasaki Disease
Bibliographic record
Abstract
Kawasaki disease (KD) is a multisystem vasculitis leading to coronary artery aneurysm formation. In a superantigen-mediated murine model of KD, the development of coronary arteritis is mediated by T-cells through the production of TNFα. TNFα localizes to the coronary arteries, where it induces the expression of MMP-9, resulting in the breakdown of elastin and the formation of aneurysms. Statins have been recently shown to have anti-inflammatory and immunomodulatory properties as a result of the inhibition of small GTPases. In \nour murine model of KD, atorvastatin treatment inhibits superantigen mediated T- cell proliferation and cytokine production, including IL-2 and TNFα. Additionally, \nstatin treatment inhibits TNFα-mediated MMP-9 production by vascular smooth muscle cells, through inhibition of the MEK/ERK pathway. Thus, statins modulate each of the critical steps in the pathogenesis of KD in a disease model, suggesting that statin use could alter the outcome and prognosis of children \nsuffering with this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.003 | 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".