Calycosin Alleviates Sepsis-Induced Acute Lung Injury via the Inhibition of Mitochondrial ROS-Mediated Inflammasome Activation.

Sepsis-induced acute lung injury (ALI) culminates in multiple organ failure via uncontrolled inflammatory responses and requires effective treatment. Herein, we aimed to investigate the effect of calycosin (CA), a natural isoflavonoid, on sepsis-induced ALI. CA attenuated lipopolysaccharide (LPS) and cecal ligation and puncture (CLP)-induced structural damage and inflammatory cell infiltration in lung tissues by histopathological analysis. CA significantly reduced lung wet/dry ratio, inflammatory cell infiltration in bronchoalveolar lavage fluid, and myeloperoxidase activity. Moreover, CA improved the survival of septic mice. CA also substantially inhibited interleukin (IL)-1ß and IL-18 levels and cleaved caspase 1 expression and activity in lung tissues. Additionally, CA markedly suppressed oxidative stress by increasing levels of superoxide dismutase and glutathione while decreasing malondialdehyde. In vitro assay showed that CA significantly inhibited LPS-induced IL-1ß and IL-18 levels and cleaved caspase 1 expression and activity in BMDMs. Moreover, CA blocked the interaction among NLRP3, ASC, and caspase 1 in LPS-treated cells. CA markedly reduced mitochondrial ROS levels. Significantly, compared with CA treatment, the combination of CA and MitoTEMPO (mitochondria-targeted antioxidant) did not further reduce the IL-1ß and IL-18 levels and cleaved caspase 1 expression and activity and decreased mitochondrial ROS levels. Collectively, the inhibition of mitochondrial ROS-mediated NLRP3 inflammasome activation contributes to the protective effects of CA, which may be considered a potential therapeutic agent for septic ALI.

[Selection of advantage prediction model for forest fire occurrence in Tahe, Daxing'an Mountain].

This study chose zero-inflated model and Hurdle model that have been widely used in economic and social fields to model the fire occurrence in Tahe, Daxing'an Mountain. The AIC, LR and SSR were used to compare the models including zero-inflated Poisson model (ZIP), zero-inflated negative binomial model (ZINB), Poisson-Hurdle model (PH) and negative Binomial Hurdle (NBH) (two types, four models in total) so as to determine a better-fit model to predict the local fire occurrence. The results illustrated that ZINB model was superior over the other three models (ZIP, PH and NBH) based on the result of AIC and SSR tests. LR test revealed that the negative binomial distribution was suitable to both the "count" portion of zero-inflated model and hurdle model. Furthermore, this paper concluded that the zero-inflated model could better fit the fire feature of the study area according to the hypotheses of the two types of models.