Green tea polyphenols ameliorate non-alcoholic fatty liver disease through upregulating AMPK activation in high fat fed Zucker fatty rats

AIM To investigate protective effects and molecular mechanisms of green tea polyphenols(GTP) on nonalcoholic fatty liver disease(NAFLD) in Zucker fatty(ZF) rats.METHODS Male ZF rats were fed a high-fat diet(HFD) for 2 wk then treated with GTP(200 mg/kg) or saline(5 m L/kg) for 8 wk, with Zucker lean rat as their control. At the end of experiment, serum and liver tissue were collected for measurement of metabolic parameters, alanine aminotransferase(ALT) and aspartate aminotransferase(AST), inflammatory cytokines and hepatic triglyceride and liver histology. Immunoblotting was used to detect phosphorylation of AMP-activated protein kinase(AMPK) acetyl-Co A carboxylase(ACC), and sterol regulatory element-binding protein 1c(SREBP1c). RESULTS Genetically obese ZF rats on a HFD presented with metabolic features of hepatic pathological changes comparable to human with NAFLD. GTP intervention decreased weight gain(10.1%, P = 0.052) and significantly lowered visceral fat(31.0%, P < 0.01). Compared with ZF-controls, GTP treatment significantly reduced fasting serum insulin, glucose and lipids levels. Reduction in serum ALT and AST levels(both P < 0.01) were observed in GTP-treated ZF rats. GTP treatment also attenuated the elevated TNFα and IL-6 in the circulation. The increased hepatic TG accumulation and cytoplasmic lipid droplet were attenuated by GTP treatment, associated with significantly increased expression of AMPK-Thr172(P < 0.05) and phosphorylated ACC and SREBP1c(both P < 0.05), indicating diminished hepatic lipogenesis and triglycerides out flux from liver in GTP treated rats. CONCLUSION The protective effects of GTP against HFD-induced NAFLD in genetically obese ZF rats are positively correlated to reduction in hepatic lipogenesis through upregulating the AMPK pathway.

Isolation and identification of an isomeric sildenafil analogue as an adulterant in an instant coffee premix

The proliferation of adulterated health foods and beverages in the market demands a comprehensive analytical strategy to identify the adulterants,particularly those of isomeric phosphodiesterase 5(PDE5)inhibitors.An instant coffee premix(ICP)purchased from an online retailer was flagged for suspected adulteration through PDE5 inhibition assay.The ICP was then analysed using suspected-target and non-targeted screenings of a liquid chromatography-quadrupole time-of-flight mass spectrometry.Based on these findings,a PDE5 inhibitor initially assigned as compound X was isolated from the ICP by employing a liquid chromatography-diode array detection before its structural elucidation with liquid chromatography-ultraviolet(LC-UV)spectroscopy and nuclear magnetic resonance(NMR)spectroscopy.The suspected-target screening matched the protonated molecule([MþH]þ)precursor ion of compound X at m/z 499.2310 with two suspected analytes that are structural isomers of one another.The fragmentation patterns of compound X were comparable to those analogues in the dithiocarbodenafil group through the non-targeted screening.These findings,complemented by the LC-UV and NMR spectroscopy data,together with the chromatographic separation of related structural isomers,conclude the identity of compound X.To our best knowledge,this is the first study to report the presence of 3,5-dimethylpiperazinyl-dithiodesmethylcarbodenafil in an ICP sample.

Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels

Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions.