γδT cells are unconventional T lymphocytes that bridge innate and adaptive immunity.Based on the composition of T cell receptor and the cytokines produced,γδT cells can be divided into diverse subsets that may be ...γδT cells are unconventional T lymphocytes that bridge innate and adaptive immunity.Based on the composition of T cell receptor and the cytokines produced,γδT cells can be divided into diverse subsets that may be present at different locations,including the liver,epithelial layer of the gut,the dermis and so on.Many of these cells perform specific functions in liver diseases,such as viral hepatitis,autoimmune liver diseases,non-alcoholic fatty liver disease,liver cirrhosis and liver cancers.In this review,we discuss the distribution,subsets,functions ofγδT cells and the relationship between the microbiota andγδT cells in common hepatic diseases.AsγδT cells have been used to cure hematological and solid tumors,we are interested inγδT cell-based immunotherapies to treat liver diseases.展开更多
BACKGROUND Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.AIM To identify pancreatic cancer in com...BACKGROUND Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.AIM To identify pancreatic cancer in computed tomography(CT)images automatically by constructing a convolutional neural network(CNN)classifier.METHODS A CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018.We established three datasets from these images according to the image phases,evaluated the approach in terms of binary classification(i.e.,cancer or not)and ternary classification(i.e.,no cancer,cancer at tail/body,cancer at head/neck of the pancreas)using 10-fold cross validation,and measured the effectiveness of the RESULTS The overall diagnostic accuracy of the trained binary classifier was 95.47%,95.76%,95.15%on the plain scan,arterial phase,and venous phase,respectively.The sensitivity was 91.58%,94.08%,92.28%on three phases,with no significant differences(χ2=0.914,P=0.633).Considering that the plain phase had same sensitivity,easier access,and lower radiation compared with arterial phase and venous phase,it is more sufficient for the binary classifier.Its accuracy on plain scans was 95.47%,sensitivity was 91.58%,and specificity was 98.27%.The CNN and board-certified gastroenterologists achieved higher accuracies than trainees on plain scan diagnosis(χ2=21.534,P<0.001;χ2=9.524,P<0.05;respectively).However,the difference between CNN and gastroenterologists was not significant(χ2=0.759,P=0.384).In the trained ternary classifier,the overall diagnostic accuracy of the ternary classifier CNN was 82.06%,79.06%,and 78.80%on plain phase,arterial phase,and venous phase,respectively.The sensitivity scores for detecting cancers in the tail were 52.51%,41.10%and,36.03%,while sensitivity for cancers in the head was 46.21%,85.24%and 72.87%on three phases,respectively.Difference in sensitivity for cancers in the head among the three phases was significant(χ2=16.651,P<0.001),with arterial phase having the highest sensitivity.CONCLUSION We proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images.It was suitable for screening purposes in pancreatic cancer detection.展开更多
Background:Liver cirrhosis is associated with immune deficiency,which causes these patients to be susceptible to various infections,including cryptococcus infection.Mortality in cirrhotic patients with cryptococcosis ...Background:Liver cirrhosis is associated with immune deficiency,which causes these patients to be susceptible to various infections,including cryptococcus infection.Mortality in cirrhotic patients with cryptococcosis has increased.The present study was to explore the risk factors of mortality and the predictive ability of different prognostic models.Methods:Forty-seven cirrhotic patients with cryptococcosis at a tertiary care hospital were included in this retrospective study.Data on demographics,clinical parameters,laboratory exams,diagnostic methods,medication during hospitalization,severity scores and prognosis were collected and analyzed.Student’s t test and Mann-Whitney test were used to compare characteristics of survivors and non-survivors at a 90-day follow-up and cerebrospinal fluid(CSF)manifestations of cryptococcal meningitis.Multivariate Cox regression analysis was used to identify the independent risk factors for mortality.Kaplan-Meier curves were used to analyze patient survival.Receiver operating characteristic(ROC)curves were used to evaluate the different prognostic factors.Results:The 30-and 90-day survival rates were 93.6%and 80.9%,respectively,in cirrhotic patients with cryptococcosis.Cryptogenic liver diseases[hazard ratio(HR)=7.567,95%confidence interval(CI):1.616-35.428,P=0.010],activated partial thromboplastin time(APTT)(HR=1.117,95%CI:1.016-1.229,P=0.022)and Child-Pugh score(HR=2.146,95%CI:1.314-3.504,P=0.002)were risk factors for 90-day mortality in cirrhotic patients with cryptococcosis.Platelet count(HR=0.965,95%CI:0.940-0.991,P=0.008)was a protective factor.APTT(HR=1.120,95%CI:1.044-1.202,P=0.002)and Child-Pugh score(HR=1.637,95%CI:1.086-2.469,P=0.019)were risk factors for 90-day mortality in cirrhotic patients with cryptococcal meningitis.There was significant difference in the percentage of lymphocytes in CSF between survivors and non-survivors[60.0(35.0-75.0)vs.95.0(83.8-97.2),P<0.001].The model of end-stage liver disease-sodium(MELD-Na)score was more accurate for predicting 30-day mortality both in patients with cryptococcosis[area under curve(AUC):0.826,95%CI:0.618-1.000]and those with cryptococcal meningitis(AUC:0.742,95%CI:0.560-0.924);Child-Pugh score was more useful for predicting 90-day mortality in patients with cryptococcosis(AUC:0.823,95%CI:0.646-1.000)and those with cryptococcal meningitis(AUC:0.815,95%CI:0.670-0.960).Conclusions:These results showed that cryptogenic liver diseases,APTT and Child-Pugh score were associated with mortality in cirrhotic patients with cryptococcosis and cryptococcal meningitis.MELD-Na score was important for predicting 30-day mortality,and Child-Pugh score was critical for predicting 90-day mortality.展开更多
Tetrazole-l-acetic acid was found to serve as a superior ligand for Cul-catalyzed N-arylation of imidazoles with aryl iodides under a low catalyst loading （5 mol% of Cul）. A variety of aryl iodides could he aminated...Tetrazole-l-acetic acid was found to serve as a superior ligand for Cul-catalyzed N-arylation of imidazoles with aryl iodides under a low catalyst loading （5 mol% of Cul）. A variety of aryl iodides could he aminated to provide the N-arylated products in good to excellent yields without the need of an inert atmosphere.展开更多
基金Supported by the National Science and Technology Major Project of China,No.2018ZX10302206 and No.2017ZX10202203-007-010。
文摘γδT cells are unconventional T lymphocytes that bridge innate and adaptive immunity.Based on the composition of T cell receptor and the cytokines produced,γδT cells can be divided into diverse subsets that may be present at different locations,including the liver,epithelial layer of the gut,the dermis and so on.Many of these cells perform specific functions in liver diseases,such as viral hepatitis,autoimmune liver diseases,non-alcoholic fatty liver disease,liver cirrhosis and liver cancers.In this review,we discuss the distribution,subsets,functions ofγδT cells and the relationship between the microbiota andγδT cells in common hepatic diseases.AsγδT cells have been used to cure hematological and solid tumors,we are interested inγδT cell-based immunotherapies to treat liver diseases.
基金the National Natural Science Foundation of China,No.81900509Fundamental Research Funds for the Central Universities,No.2018XZZX002-10High-Level Talents Special Support Plan of Zhejiang Province(known as the Ten Thousand Talents Plan),No.ZJWR0108008.
文摘BACKGROUND Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.AIM To identify pancreatic cancer in computed tomography(CT)images automatically by constructing a convolutional neural network(CNN)classifier.METHODS A CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018.We established three datasets from these images according to the image phases,evaluated the approach in terms of binary classification(i.e.,cancer or not)and ternary classification(i.e.,no cancer,cancer at tail/body,cancer at head/neck of the pancreas)using 10-fold cross validation,and measured the effectiveness of the RESULTS The overall diagnostic accuracy of the trained binary classifier was 95.47%,95.76%,95.15%on the plain scan,arterial phase,and venous phase,respectively.The sensitivity was 91.58%,94.08%,92.28%on three phases,with no significant differences(χ2=0.914,P=0.633).Considering that the plain phase had same sensitivity,easier access,and lower radiation compared with arterial phase and venous phase,it is more sufficient for the binary classifier.Its accuracy on plain scans was 95.47%,sensitivity was 91.58%,and specificity was 98.27%.The CNN and board-certified gastroenterologists achieved higher accuracies than trainees on plain scan diagnosis(χ2=21.534,P<0.001;χ2=9.524,P<0.05;respectively).However,the difference between CNN and gastroenterologists was not significant(χ2=0.759,P=0.384).In the trained ternary classifier,the overall diagnostic accuracy of the ternary classifier CNN was 82.06%,79.06%,and 78.80%on plain phase,arterial phase,and venous phase,respectively.The sensitivity scores for detecting cancers in the tail were 52.51%,41.10%and,36.03%,while sensitivity for cancers in the head was 46.21%,85.24%and 72.87%on three phases,respectively.Difference in sensitivity for cancers in the head among the three phases was significant(χ2=16.651,P<0.001),with arterial phase having the highest sensitivity.CONCLUSION We proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images.It was suitable for screening purposes in pancreatic cancer detection.
基金supported by grants from the National Science and Technology Major Project of China(2018ZX10302206 and 2017ZX10202203)。
文摘Background:Liver cirrhosis is associated with immune deficiency,which causes these patients to be susceptible to various infections,including cryptococcus infection.Mortality in cirrhotic patients with cryptococcosis has increased.The present study was to explore the risk factors of mortality and the predictive ability of different prognostic models.Methods:Forty-seven cirrhotic patients with cryptococcosis at a tertiary care hospital were included in this retrospective study.Data on demographics,clinical parameters,laboratory exams,diagnostic methods,medication during hospitalization,severity scores and prognosis were collected and analyzed.Student’s t test and Mann-Whitney test were used to compare characteristics of survivors and non-survivors at a 90-day follow-up and cerebrospinal fluid(CSF)manifestations of cryptococcal meningitis.Multivariate Cox regression analysis was used to identify the independent risk factors for mortality.Kaplan-Meier curves were used to analyze patient survival.Receiver operating characteristic(ROC)curves were used to evaluate the different prognostic factors.Results:The 30-and 90-day survival rates were 93.6%and 80.9%,respectively,in cirrhotic patients with cryptococcosis.Cryptogenic liver diseases[hazard ratio(HR)=7.567,95%confidence interval(CI):1.616-35.428,P=0.010],activated partial thromboplastin time(APTT)(HR=1.117,95%CI:1.016-1.229,P=0.022)and Child-Pugh score(HR=2.146,95%CI:1.314-3.504,P=0.002)were risk factors for 90-day mortality in cirrhotic patients with cryptococcosis.Platelet count(HR=0.965,95%CI:0.940-0.991,P=0.008)was a protective factor.APTT(HR=1.120,95%CI:1.044-1.202,P=0.002)and Child-Pugh score(HR=1.637,95%CI:1.086-2.469,P=0.019)were risk factors for 90-day mortality in cirrhotic patients with cryptococcal meningitis.There was significant difference in the percentage of lymphocytes in CSF between survivors and non-survivors[60.0(35.0-75.0)vs.95.0(83.8-97.2),P<0.001].The model of end-stage liver disease-sodium(MELD-Na)score was more accurate for predicting 30-day mortality both in patients with cryptococcosis[area under curve(AUC):0.826,95%CI:0.618-1.000]and those with cryptococcal meningitis(AUC:0.742,95%CI:0.560-0.924);Child-Pugh score was more useful for predicting 90-day mortality in patients with cryptococcosis(AUC:0.823,95%CI:0.646-1.000)and those with cryptococcal meningitis(AUC:0.815,95%CI:0.670-0.960).Conclusions:These results showed that cryptogenic liver diseases,APTT and Child-Pugh score were associated with mortality in cirrhotic patients with cryptococcosis and cryptococcal meningitis.MELD-Na score was important for predicting 30-day mortality,and Child-Pugh score was critical for predicting 90-day mortality.
基金Project supported by the National Science and Technology Major Project of China(Nos.2018ZX10302206 , 2017ZX10202203)the Zhejiang University Academic Award for Outstanding Doctoral Candidates(No.2020052),China。
基金supported fnancially by the National Basic Research Program of China(973 Program,No.2012CB722603)the Start-Up Foundation for Young Scientists of Shihezi University(Nos.RCZX201012,RCZX201014,RCZX201015)
文摘Tetrazole-l-acetic acid was found to serve as a superior ligand for Cul-catalyzed N-arylation of imidazoles with aryl iodides under a low catalyst loading （5 mol% of Cul）. A variety of aryl iodides could he aminated to provide the N-arylated products in good to excellent yields without the need of an inert atmosphere.
