To evaluate a calcium activated potassium channel (KCa3.1) inhibitor attenuates liver disease in models of non-alcoholic fatty liver disease (NAFLD).METHODSWe have performed a series of in vitro and in vivo studies us...To evaluate a calcium activated potassium channel (KCa3.1) inhibitor attenuates liver disease in models of non-alcoholic fatty liver disease (NAFLD).METHODSWe have performed a series of in vitro and in vivo studies using the KCa3.1 channel inhibitor, Senicapoc. Efficacy studies of Senicapoc were conducted in toxin-, thioacetamide (TAA) and high fat diet (HFD)-induced models of liver fibrosis in rats. Efficacy and pharmacodynamic effects of Senicapoc was determined through biomarkers of apoptosis, inflammation, steatosis and fibrosis.RESULTSUpregulation of KCa3.1 expression was recorded in TAA-induced and high fat diet-induced liver disease. Treatment with Senicapoc decreased palmitic acid-driven HepG2 cell death. (P < 0.05 vs control) supporting the finding that Senicapoc reduces lipid-driven apoptosis in HepG2 cell cultures. In animals fed a HFD for 6 wk, co-treatment with Senicapoc, (1) reduced non-alcoholic fatty liver disease (NAFLD) activity score (NAS) (0-8 scale), (2) decreased steatosis and (3) decreased hepatic lipid content (Oil Red O, P < 0.05 vs vehicle). Randomization of TAA animals and HFD fed animals to Senicapoc was associated with a decrease in liver fibrosis as evidenced by hydroxyproline and Masson’s trichrome staining (P < 0.05 vs vehicle). These results demonstrated that Senicapoc mitigates both steatosis and fibrosis in liver fibrosis models.CONCLUSIONThese data suggest that Senicapoc interrupts more than one node in progressive fatty liver disease by its anti-steatotic and anti-fibrotic activities, serving as a double-edged therapeutic sword.展开更多
To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex chal...To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.展开更多
Microbial communities in marine habitats are regarded as underexplored reservoirs for discovering new natural products with potential application.However,only a few microbes in nature can be cultivated in the laborato...Microbial communities in marine habitats are regarded as underexplored reservoirs for discovering new natural products with potential application.However,only a few microbes in nature can be cultivated in the laboratory.This has led to the development of a variety of isolation and cultivation methods,and in situ cultivation is one of the most popular.Diverse in situ cultivation methods,with the same basic principle,have been applied to a variety of environmental samples.Compared with conventional approaches,these new methods are able to cultivate previously uncultured and phylogenetically novel microbes,many with biotechnological potential.This review introduces the various in situ cultivation methods for the isolation of previously uncultured microbial species and their potential for marine microbial resource mining.Furthermore,studies that investigated the key and previously unidentified mechanisms of growing uncultivated microorganisms by in situ cultivation,which will shed light on the understanding of microbial uncultivability,were also reviewed.展开更多
文摘To evaluate a calcium activated potassium channel (KCa3.1) inhibitor attenuates liver disease in models of non-alcoholic fatty liver disease (NAFLD).METHODSWe have performed a series of in vitro and in vivo studies using the KCa3.1 channel inhibitor, Senicapoc. Efficacy studies of Senicapoc were conducted in toxin-, thioacetamide (TAA) and high fat diet (HFD)-induced models of liver fibrosis in rats. Efficacy and pharmacodynamic effects of Senicapoc was determined through biomarkers of apoptosis, inflammation, steatosis and fibrosis.RESULTSUpregulation of KCa3.1 expression was recorded in TAA-induced and high fat diet-induced liver disease. Treatment with Senicapoc decreased palmitic acid-driven HepG2 cell death. (P < 0.05 vs control) supporting the finding that Senicapoc reduces lipid-driven apoptosis in HepG2 cell cultures. In animals fed a HFD for 6 wk, co-treatment with Senicapoc, (1) reduced non-alcoholic fatty liver disease (NAFLD) activity score (NAS) (0-8 scale), (2) decreased steatosis and (3) decreased hepatic lipid content (Oil Red O, P < 0.05 vs vehicle). Randomization of TAA animals and HFD fed animals to Senicapoc was associated with a decrease in liver fibrosis as evidenced by hydroxyproline and Masson’s trichrome staining (P < 0.05 vs vehicle). These results demonstrated that Senicapoc mitigates both steatosis and fibrosis in liver fibrosis models.CONCLUSIONThese data suggest that Senicapoc interrupts more than one node in progressive fatty liver disease by its anti-steatotic and anti-fibrotic activities, serving as a double-edged therapeutic sword.
基金This research was supported by Study on Diagnostic and Prognostic of Lithium-Ion Battery for Electric Vehicle funded by Xynergypower Co.,Ltd.(UNIST-2.200733.01)also supported by the Hydrogen Energy Innovation Technology Development Program of the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT(MSIT))(NRF-2019M3E6A1064290).
文摘To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.
基金This work was funded by the National Natural Science Foundation of China(41776168,31600016)the National 111 Project of China(D16013)Li Dak Sum Yip Yio Chin Kenneth Li Marine Biopharmaceutical Development Fund.
文摘Microbial communities in marine habitats are regarded as underexplored reservoirs for discovering new natural products with potential application.However,only a few microbes in nature can be cultivated in the laboratory.This has led to the development of a variety of isolation and cultivation methods,and in situ cultivation is one of the most popular.Diverse in situ cultivation methods,with the same basic principle,have been applied to a variety of environmental samples.Compared with conventional approaches,these new methods are able to cultivate previously uncultured and phylogenetically novel microbes,many with biotechnological potential.This review introduces the various in situ cultivation methods for the isolation of previously uncultured microbial species and their potential for marine microbial resource mining.Furthermore,studies that investigated the key and previously unidentified mechanisms of growing uncultivated microorganisms by in situ cultivation,which will shed light on the understanding of microbial uncultivability,were also reviewed.