Copper (Cu) is a kind of micronutrient element that is essential for human metabolism.However,it is also considered as an environmental pollutant which is toxic to organisms at a high concentration level.Probiotics,re...Copper (Cu) is a kind of micronutrient element that is essential for human metabolism.However,it is also considered as an environmental pollutant which is toxic to organisms at a high concentration level.Probiotics,regarded as beneficial microorganisms for promoting human health,have functions of antioxidant capacity,immune-enhancing properties,intestinal barrier protection and regulation.Several studies have reported that probiotics show positive effects on alleviating and intervening heavy metals toxicity.However,evidence for relieving copper-induced toxicity by probiotics is still limited.In this study,we firstly conducted a zebrafish larvae model to screen out microorganisms which are helpful for CuSO_(4)toxicity resistance and one novel strain named as Bacillus coagulans XY2 was discovered with the best protective activity.B.coagulans XY2 significantly reduced the mortality of zebrafish larvae exposed to 10μmol/L CuSO_(4)for 96 hr,as well as alleviated the neutrophils infiltration in the larvae lateral line under a 2 hr exposure.B.coagulans XY2 exhibited a high in vitro antioxidant activity and against CuSO_(4)-induced oxidative stress in zebrafish larvae by up-regulating sod1,gstp1 and cat gene transcriptional levels and relevant enzymatic activities.CuSO_(4)stimulated the inflammation process resulting in obvious increases of gene il-1βand il-10 transcription,which were suppressed by B.coagulans XY2 intervention.Overall,our results underline the bio-function of B.coagulans XY2 on protecting zebrafish larvae from copper toxicity,suggesting the potential application values of probiotics in copper toxicity alleviation on human and the environment.展开更多
The Shanggu gas field is the low porosity and low permeability. Single well controlled reserves, economic limit well spacing and economic rational spacing through different methods are calculated. With the development...The Shanggu gas field is the low porosity and low permeability. Single well controlled reserves, economic limit well spacing and economic rational spacing through different methods are calculated. With the development experience of Su Lige gas field as guidance, the rational spacing of Shanggu gas reservoir is 700m×900m by calculating daily gas production rate and cumulative gas production with different well spacing using numerical simulation method.展开更多
Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the...Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the liver and spleen to precisely assess the risk of GEV and high-risk GEV(HRV).Methods:This was a multicenter,prospective study conducted from October 2020 to September 2022 across 12 hospitals in China.Patients with compensated advanced chronic liver disease(cACLD)were enrolled,with informed consent obtained.A total of 1136 liver stiffness measurement(LSM)images and 1042 spleen stiffness measurement(SSM)images generated by 2D SWE.Weleveraged deep learning methods to uncover associations between image features and patient risk;in this manner,we constructed models to predict GEV and HRV.Results:A multimodality deep learning risk prediction(DLRP)model was constructed to assess GEV and HRV based on LSM and SSM images and clinical information.Validation analysis revealed that the area under the curve(AUC)values of DLRP were 0.91 for GEV(95%confidence interval[CI],0.90-0.93,p<0.05)and 0.88 for HRV(95%CI,0.86-0.89,p<0.01),which were significantly and robustly better than those of canonical risk indicators,including the values of LSM(0.63 and 0.68 for GEV and HRV)andSSM(0.75for both GEV andHRV).Moreover,the DLRP model outperformed the model using individual parameters.In HRV prediction,the 2D-SWE SSM images(0.75)were more informative than LSM(0.68,p<0.01).Conclusion:Our DLRP model shows excellent performance in predicting GEV and HRV,outperforming the canonical risk indicators LSM and SSM.Additionally,the 2D-SWE SSM images provided more information and thus better accuracy in HRV prediction than the LSM images.展开更多
文摘Copper (Cu) is a kind of micronutrient element that is essential for human metabolism.However,it is also considered as an environmental pollutant which is toxic to organisms at a high concentration level.Probiotics,regarded as beneficial microorganisms for promoting human health,have functions of antioxidant capacity,immune-enhancing properties,intestinal barrier protection and regulation.Several studies have reported that probiotics show positive effects on alleviating and intervening heavy metals toxicity.However,evidence for relieving copper-induced toxicity by probiotics is still limited.In this study,we firstly conducted a zebrafish larvae model to screen out microorganisms which are helpful for CuSO_(4)toxicity resistance and one novel strain named as Bacillus coagulans XY2 was discovered with the best protective activity.B.coagulans XY2 significantly reduced the mortality of zebrafish larvae exposed to 10μmol/L CuSO_(4)for 96 hr,as well as alleviated the neutrophils infiltration in the larvae lateral line under a 2 hr exposure.B.coagulans XY2 exhibited a high in vitro antioxidant activity and against CuSO_(4)-induced oxidative stress in zebrafish larvae by up-regulating sod1,gstp1 and cat gene transcriptional levels and relevant enzymatic activities.CuSO_(4)stimulated the inflammation process resulting in obvious increases of gene il-1βand il-10 transcription,which were suppressed by B.coagulans XY2 intervention.Overall,our results underline the bio-function of B.coagulans XY2 on protecting zebrafish larvae from copper toxicity,suggesting the potential application values of probiotics in copper toxicity alleviation on human and the environment.
文摘The Shanggu gas field is the low porosity and low permeability. Single well controlled reserves, economic limit well spacing and economic rational spacing through different methods are calculated. With the development experience of Su Lige gas field as guidance, the rational spacing of Shanggu gas reservoir is 700m×900m by calculating daily gas production rate and cumulative gas production with different well spacing using numerical simulation method.
基金funded by the Key Research and Development Program of Jiangsu Province(No.BE2023767a)The Fundamental Research Fund of Southeast University(No.3290002303A2)+5 种基金Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University(No.2023YJXYYRCPY03)Research Personnel Cultivation Programme of Zhongda Hospital Southeast University(No.CZXM-GSP-RC125,CZXM-GSP-RC119)China Postdoctoral Science Foundation(No.2024M750461)National Natural Science Foundation of China(No.82402413)Natural Science Foundation of Jiangsu Province(No.BK20241681)National Natural Science Foundation of China(No.62061160369).
文摘Aims:Bleeding from gastroesophageal varices(GEV)is a medical emergency associated with high mortality.We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography(2D-SWE)of the liver and spleen to precisely assess the risk of GEV and high-risk GEV(HRV).Methods:This was a multicenter,prospective study conducted from October 2020 to September 2022 across 12 hospitals in China.Patients with compensated advanced chronic liver disease(cACLD)were enrolled,with informed consent obtained.A total of 1136 liver stiffness measurement(LSM)images and 1042 spleen stiffness measurement(SSM)images generated by 2D SWE.Weleveraged deep learning methods to uncover associations between image features and patient risk;in this manner,we constructed models to predict GEV and HRV.Results:A multimodality deep learning risk prediction(DLRP)model was constructed to assess GEV and HRV based on LSM and SSM images and clinical information.Validation analysis revealed that the area under the curve(AUC)values of DLRP were 0.91 for GEV(95%confidence interval[CI],0.90-0.93,p<0.05)and 0.88 for HRV(95%CI,0.86-0.89,p<0.01),which were significantly and robustly better than those of canonical risk indicators,including the values of LSM(0.63 and 0.68 for GEV and HRV)andSSM(0.75for both GEV andHRV).Moreover,the DLRP model outperformed the model using individual parameters.In HRV prediction,the 2D-SWE SSM images(0.75)were more informative than LSM(0.68,p<0.01).Conclusion:Our DLRP model shows excellent performance in predicting GEV and HRV,outperforming the canonical risk indicators LSM and SSM.Additionally,the 2D-SWE SSM images provided more information and thus better accuracy in HRV prediction than the LSM images.
