Swertisin contents in rat urine,feces and tissues were determined by reversed-phase high-performance liquid chromatography（RP-HPLC） method.Chromatographic separations were performed on a C18 column with acetonitrile...Swertisin contents in rat urine,feces and tissues were determined by reversed-phase high-performance liquid chromatography（RP-HPLC） method.Chromatographic separations were performed on a C18 column with acetonitrile-water（23：77,v/v） as the mobile phase.The calibration curves were linear over the ranges of 0.175-35.0μg/mL for rat urine,0.5-60.0μg/mL for rat feces,and 0.014 to 53.0μg/mL for all tissues.The inter-and intra-day precisions and accuracy for all measured samples were satisfactory.The fully validated method was applied for tissue distribution and excretion of swertisin in rat urine and bile after intravenous administration.The maximum level of swertisin was found in kidney,which reached 83.87± 6.36μg/g.In rat heart,swertisin was hardly detected under used experimental conditions.Swetisin level in liver,kidney,stomach,smooth muscle and skeletal muscle continued to decrease from 5 to 60min.Swertisin showed increasing tendency in intestine,spleen and testis tissues at scheduled time points.Detectable swertisin was found in brain and lung tissue.Totally 11.9% swertisin dose was cumulatively excreted from urine in 60h after intravenous administration.There was small amount of swertisin in rat feces and the cumulative excretion level reached 4.59% of intravenous dose in 60h.展开更多
Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma(HCC).Overwhelming evidence from publishe...Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma(HCC).Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis,predicting responses to specific therapies,evaluating prognosis before or after therapy,as well as serving as novel therapeutic targets.Detection and analysis of proteins,metabolites,circulating nucleic acids,circulating tumor cells,and extracellular vesicles in body fluids(e.g.,blood and urine)and gut microbiota(e.g.,in feces)have excellent capabilities to improve different aspects of management of HCC.Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis,treatment,and monitoring responses of HCC to conventional therapies,most of which may improve diagnosis and management of HCC in the future.This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance.展开更多
Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming.The traditional prediction methods such as polluta...Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming.The traditional prediction methods such as pollutant generation coefficient(PGC)and mathematical model based on linear regression(LR)may be limited by prediction range and regression function assumption,and sometimes may deviate from the actual condition.In order to solve these problems,the support vector regression(SVR)was applied for predicting the cows'feces,urine and N excretions,taking Holstein dry cows as a case study.SVR is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data,and also it can fit the function closest to the actual in most cases.To evaluate prediction accuracy effectively,the SVR technique was compared with the LR and radial basis function artificial neural network(RBF-ANN)methods,using the required sample data obtained from actual feeding experiments.The prediction results indicate that the proposed technique is superior to the other two conventional(especially LR)methods in predicting the main indicators of feces,urine,and N excretions of Holstein dry cows.展开更多
基金Supported by the Excellent Young Scholars Research Foundation of Beijing Institute of Technology(2007Y0612)
文摘Swertisin contents in rat urine,feces and tissues were determined by reversed-phase high-performance liquid chromatography（RP-HPLC） method.Chromatographic separations were performed on a C18 column with acetonitrile-water（23：77,v/v） as the mobile phase.The calibration curves were linear over the ranges of 0.175-35.0μg/mL for rat urine,0.5-60.0μg/mL for rat feces,and 0.014 to 53.0μg/mL for all tissues.The inter-and intra-day precisions and accuracy for all measured samples were satisfactory.The fully validated method was applied for tissue distribution and excretion of swertisin in rat urine and bile after intravenous administration.The maximum level of swertisin was found in kidney,which reached 83.87± 6.36μg/g.In rat heart,swertisin was hardly detected under used experimental conditions.Swetisin level in liver,kidney,stomach,smooth muscle and skeletal muscle continued to decrease from 5 to 60min.Swertisin showed increasing tendency in intestine,spleen and testis tissues at scheduled time points.Detectable swertisin was found in brain and lung tissue.Totally 11.9% swertisin dose was cumulatively excreted from urine in 60h after intravenous administration.There was small amount of swertisin in rat feces and the cumulative excretion level reached 4.59% of intravenous dose in 60h.
基金Supported by National Natural Science Foundation of China,No.81972726,No.81871949 and No.81572345.
文摘Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma(HCC).Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis,predicting responses to specific therapies,evaluating prognosis before or after therapy,as well as serving as novel therapeutic targets.Detection and analysis of proteins,metabolites,circulating nucleic acids,circulating tumor cells,and extracellular vesicles in body fluids(e.g.,blood and urine)and gut microbiota(e.g.,in feces)have excellent capabilities to improve different aspects of management of HCC.Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis,treatment,and monitoring responses of HCC to conventional therapies,most of which may improve diagnosis and management of HCC in the future.This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance.
基金The authors would like to acknowledge the financial support from the National Key R&D Program of China(2016YFD0700204-02)the China Agriculture Research System(CARS-36)+1 种基金the China Postdoctoral Science Foundation(2017M611346)the Natural Science Foundation of Heilongjiang Province of China(C2018018).
文摘Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming.The traditional prediction methods such as pollutant generation coefficient(PGC)and mathematical model based on linear regression(LR)may be limited by prediction range and regression function assumption,and sometimes may deviate from the actual condition.In order to solve these problems,the support vector regression(SVR)was applied for predicting the cows'feces,urine and N excretions,taking Holstein dry cows as a case study.SVR is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data,and also it can fit the function closest to the actual in most cases.To evaluate prediction accuracy effectively,the SVR technique was compared with the LR and radial basis function artificial neural network(RBF-ANN)methods,using the required sample data obtained from actual feeding experiments.The prediction results indicate that the proposed technique is superior to the other two conventional(especially LR)methods in predicting the main indicators of feces,urine,and N excretions of Holstein dry cows.
