PKC-β activation inhibits IL-18-binding protein causing endothelial dysfunction and diabetic atherosclerosis
Bibliographic record
Abstract
AIMS: Clinical observations showed a correlation between accelerated atherosclerosis in diabetes and high plasmatic level of IL-18, a pro-inflammatory cytokine. IL-18 enhances the production of inflammatory cytokines and cellular adhesion molecules contributing to atherosclerotic plaque formation and instability. Previous studies indicated that protein kinase C (PKC)-β inhibition prevented macrophage-induced cytokine expression involved in diabetic (DM) atherosclerotic plaque development. However, the role of PKC-β activation on IL-18/IL-18-binding protein (IL-18BP) pathway causing endothelial dysfunction and monocyte adhesion in diabetes has never been explored. METHODS AND RESULTS: Apoe(-/-) mice were rendered DM and fed with western diet containing ruboxistaurin (RBX), a PKC-β inhibitor. After 20 weeks, atherosclerotic plaque composition was quantified. Compared with non-diabetic, DM mice exhibited elevated atherosclerotic plaque formation, cholestoryl ester content and macrophage infiltration, as well as reduced IL-18BP expression in the aorta which was prevented with RBX treatment. Endothelial cells (ECs) and macrophages were exposed to normal or high glucose (HG) levels with or without palmitate and recombinant IL-18 for 24 h. The combined HG and palmitate condition was required to increase IL-18 expression and secretion in macrophages, while it reduced IL-18BP expression in EC causing up-regulation of the vascular cell adhesion molecule (VCAM)-1 and monocyte adhesion. Elevated VCAM-1 expression and monocyte adherence were prevented by siRNA, RBX, and IL-18 neutralizing antibody. CONCLUSION: Our study unrevealed a new mechanism by which PKC-β activation promotes EC dysfunction caused by the de-regulation of the IL-18/IL-18BP pathway, leading to increased VCAM-1 expression, monocyte/macrophage adhesion, and accelerated atherosclerotic plaque formation in diabetes.
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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) | 0.003 | 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".