Nicaraven inhibits TNFα-induced endothelial activation and inflammation through suppression of NF-κB signaling pathway
Bibliographic record
Abstract
Inflammation-induced activation and dysfunction of endothelial cells play an important role in the pathology of multiple vascular diseases. Nicaraven, a potent hydroxyl radical scavenger, has recently been found to have anti-inflammatory roles; however, the mechanism of its action is not fully understood. Here we investigated the effects of Nicaraven on tumor necrosis factor α (TNFα) - induced inflammatory response in human umbilical vein endothelial cells and we explore the underlying mechanisms related to the nuclear factor-κB (NF-κB) signaling pathway. Our results showed that Nicaraven significantly reduced the reactive oxygen species production after TNFα stimulation. Nicaraven suppressed TNFα-induced mRNA expression of multiple adhesion molecules and pro-inflammatory cytokines, including vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), E-selectin, MCP-1, TNFα, interleukin-1β (IL-1β), IL-6, and IL-8. In addition, Nicaraven inhibited monocyte adhesion and reduced the protein levels of VCAM-1 and ICAM-1. Mechanistically, Nicaraven prevented TNFα-induced activation of NF-κB signaling pathway by suppressing the phosphorylation of NF-κB p65, IκBα, and IκB kinase (IKK)α/β, stabilizing IκBα, and inhibiting the translocation of p65 from cytosol to nucleus. Finally, we showed that Nicaraven improved the functions of endothelial cells, seen as the upregulation of endothelial nitric oxide synthase and increased nitric oxide levels. Our findings indicated that Nicaraven effectively inhibits TNFα-induced endothelial activation and inflammatory response at least partly through inhibiting NF-κB signaling pathway.
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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 | 0.001 |
| 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".