The inhibitory effect of simvastatin on the ADMA-induced inflammatory reaction is mediated by MAPK pathways in endothelial cells
Bibliographic record
Abstract
Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase, is emerging as a key contributor for endothelial dysfunction associated with inflammation. Statins can inhibit vascular inflammatory reaction and improve endothelial function. The aim of this study was to investigate in human endothelial cells the signaling pathways of ADMA-induced inflammatory reaction and potential inhibitory effects of simvastatin. Endothelial cells were cultured and used for all of the studies. Tumor necrosis factor-alpha(TNF-alpha) and soluble intercellular adhesion molecule-1 (sICAM-1) were determined by enzyme-linked immunosorbent assay. Nuclear factor-kappaB (NF-kappaB) was assayed by electrophoretic mobility shift assay. The activation of mitogen-activated protein kinases (MAPKs), including p38 MAPK and extracellular signal-related kinase (ERK(1/2)), were characterized by Western blot analysis. Treatment with ADMA (3-30 micromol/L) increased the concentration of sICAM-1 in a dose-dependent manner. ADMA (30 micromol/L) significantly enhanced the concentrations of TNF-alpha and sICAM-1, the activity of NF-kappaB and the phosphorylation of p38 MAPK and ERK(1/2). The increased secretion of TNF-alpha and sICAM-1 and the increased activity of NF-kappaB by ADMA were altered by SB203580 (5 micromol/L) or PD98059 (20 micromol/L), but not by LY294002 (20 micromol/L). Simvastatin (0.1, 0.5, or 2.5 micromol/L) markedly inhibited the elevated concentrations of TNF-alpha and sICAM-1, the activity of NF-kappaB, and the phosphorylation of p38 MAPK and ERK(1/2) induced by ADMA. Simvastatin inhibited ADMA-induced inflammatory reaction by p38 MAPK and ERK(1/2) pathways in cultured endothelial cells.
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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) | 0.000 | 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 | 0.000 |
| 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".