Objective: The endothelial inflammatory response plays an important role in atherogenesis by inducing nuclear factor (NF)κB-dependent cell adhesion molecule expression and monocyte recruitment. Here, we screened for natural ligands and investigated the ability of shinjulactone A to inhibit interleukin-1β (IL-1β)-induced endothelial inflammatory signaling. Methods: The natural compound library included 880 single compounds isolated from medicinal plants by the Korean Medicinal Material Bank. Primary endothelial cells were pretreated with single compounds before stimulation with IL-1β to induce endothelial inflammation. Endothelial inflammation was measured by assaying NFκB activation and monocyte adhesion. The endothelial-mesenchymal transition (EndMT) was evaluated using cell type-specific marker protein expression and morphology. Results: Shinjulactone A was identified as an efficient blocker of IL-1β -induced NFκB activation, with a half-maximal inhibitory concentration of approximately 1 µM, and monocyte recruitment in endothelial cells. However, it did not affect lipopolysaccharideinduced NFκB activation in macrophages. Compared to Bay 11-782, a well-known NFκB inhibitor that shows considerable cytotoxicity during long-term treatment, shinjulactone A did not affect endothelial cell viability. Furthermore, it also significantly inhibited the EndMT, which is known to promote atherosclerosis and plaque instability. Conclusion We suggest that shinjulactone A may be an effective and safe drug candidate for atherosclerosis because it targets and inhibits both endothelial inflammation and the EndMT, without impairing NFκB-dependent innate immunity in macrophages.

Objective: Fatigue is a common, debilitating nonmotor symptom of Parkinson’s disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD. Methods: We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality. Results: No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected). Conclusion Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.