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Few studies on anti-inflammatory drugs with indole groups have been published. This is the first study that demonstrates the anti-inflammatory effects of indole derivative XCR-5a in vitro and in vivo. This study aimed to discover more anti-inflammatory drugs with indole groups and investigate their anti-inflammatory mechanisms. First, a series of indole derivatives was synthesized, then screened for XCR-5a, a compound with anti-inflammatory effects. Second, the in vitro production of IL-1��, IL-6, TNF-��, inducible nitric oxide synthase (iNOS), and cyclo-oxygenase-2 (COX-2) in lipopolysaccharide (LPS)-induced primary cells of mice pretreated with XCR-5a was determined using qPCR and ELISA. Finally, the effect of XCR-5a on LPS-induced NF-��B signaling activation was determined by Western blotting. An in vivo mouse sepsis model was established. In mouse lung tissue, the production of IL-1��, IL-6, and TNF-�� was determined and H&E staining was performed. Our findings showed that XCR-5a could suppress the production of LPS-induced IL-1��, IL-6, and TNF-��, as well as mRNA expression of iNOS and COX-2. Pretreatment with XCR-5a inhibited the LPS-induced inflammatory response in septic mice in vivo by decreasing pro-inflammatory cytokines production in serum and reducing immune cell infiltration. Mechanistically, XCR-5a suppressed LPS-induced activation of the NF-��B signaling pathway. XCR-5a has anti-inflammatory effects in vitro and in vivo. Therefore, XCR-5a could be a potential drug candidate for the treatment of inflammatory diseases.

A deterministic Extreme Value Analysis method is of particular importance in an engineering-oriented context. In this paper, three extreme value analysis (EVA) methods, in which annual maxima, monthly maxima or the Peaks-Over-Threshold are fitted into Generalized Extreme Value distribution (GEV) function or Generalized Pareto distribution (GP) function, are used to estimate return values of significant wave height. Sensitivity of return levels on water depth and sampling length is vigorously investigated, based on a 40-year long and high-resolution wave hindcast for the South Yellow Sea (SYS). A spatially-and-temporally varied POT sampling method combined with GP function produces most conservative and confident estimates of return levels for a large return period, but produces wider confidence level than the other two EVA approaches based on GEV function for short return period. In the SYS, the return level estimates are significantly reduced with a longer sample length. However, we find that the reduction is closely related to the long-term trend in extreme wave heights, rather than due to the sampling effect. From deep to shallow waters, spatial inhomogeneity of return levels increases, which should be considered in the engineering practice.