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Fenofibrate, as a lipid-lowering drug in clinic, participates in the regulation of inflammatory response. Recently, increasing studies have indicated that sirtuin1 (SIRT1), a NAD+-dependent deacetylase, has potential anti-inflammatory effect in endothelial cells. However, whether the regulatory effect of fenofibrate on inflammation response is mediated by SIRT1 remains unclear. The aim of this study was to investigate the effect of fenofibrate on the expressions of SIRT1 and pro-inflammatory cytokine CD40 in endothelial cells and explore the underlying mechanisms. The results showed that fenofibrate upregulated SIRT1 expression and inhibited CD40 expression in TNF-α-stimulated endothelial cells, but these effects were reversed by peroxisome proliferator-activated receptor-α (PPARα) antagonist GW6471. Furthermore, SIRT1 inhibitors sirtinol/nicotinamide (NAM) or SIRT1 knockdown could attenuate the effect of fenofibrate on CD40 expression in endothelial cells. Importantly, NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC) augmented the effect of fenofibrate on CD40 expression. Further study found that fenofibrate decreased the expression of acetylated-NF-κB p65 (Ac-NF-κB p65) in TNF-α-stimulated endothelial cells, which was abolished by SIRT1 knockdown. These results indicate that fenofibrate has protective effect against TNF-α-induced CD40 expression through SIRT1-mediated deacetylation of the p65 subunit of NF-κB.

We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient’s languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health translation outputs based on the computational modelling of a small number of optimised features of the original English source texts. The best performing multinominal Naïve Bayes classifier (MNB) using a small number of optimised features (8) achieved statistically higher AUC (M = 0.760, SD = 0.03) than the classifier using high-dimension natural features (135) (M = 0.631, SD = 0.006, p < 0.0001, SE = 0.004) and the automatically optimised classifier (22) (M = 0.7231, SD = 0.0084, p < 0.0001, SE = 0.004). Furthermore, MNB (8) had statistically higher sensitivity (M = 0.885, SD = 0.100) compared with the full-feature classifier (135) (M = 0.577, SD = 0.155, p < 0.0001, SE = 0.005) and the automatically optimised classifier (22) (M = 0.731, SD = 0.139, p < 0.0001, SE = 0.0023). Finally, MNB (8) reached statistically higher specificity (M = 0.667, SD = 0.138) compared to the full-feature classifier (135) (M = 0.567, SD = 0.139, p = 0.0002, SE = 0.026) and the automatically optimised classifier (22) (M = 0.633, SD = 0.141, p = 0.0133, SE = 0.026).

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