Objective To observe the ultrasonic manifestations and age distribution of internal abdominal hernia in children.Methods Data of 53 children with internal abdominal hernia confirmed by operation were retrospectively a...Objective To observe the ultrasonic manifestations and age distribution of internal abdominal hernia in children.Methods Data of 53 children with internal abdominal hernia confirmed by operation were retrospectively analyzed.The ultrasonic findings were observed,and the age distribution of children was analyzed.Results Among 53 cases,"cross sign"was observed in 22 cases(22/53,41.51%),and"hernia ring beak sign"was detected in 26 cases(26/53,49.06%)by preoperative ultrasound,according to which 21 cases were diagnosed as internal abdominal hernia,with the accuracy of 39.62%(21/53).Meanwhile,manifestations of intestinal obstruction were noticed in 48 cases(48/53,90.57%),and intestinal necrosis was considered in 22 cases(22/53,41.51%).Four cases were misdiagnosed as intestinal perforation,appendicitis,intestinal atresia and volvulus,each in 1 case.The onset age of postoperative adhesive band internal hernia was larger than that of mesenteric hiatal hernia(P<0.05),while no significant difference of onset age was found among other types of internal abdominal hernias(all P>0.05).Intestinal ischemic necrosis was found in 25 cases,while the incidence of intestinal necrosis in children aged≤1 year,>1 and≤3 years,>3 and≤7 years and those>7 years was 66.67%(12/18),33.33%(4/12),36.36%(4/11)and 41.67%(5/12),respectively.Conclusion The characteristic ultrasonic findings of internal abdominal hernia in children included"cross sign"and"hernia ring beak sign".Internal abdominal hernia in children under 1 year had high risk of intestinal necrosis.展开更多
Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
The evolution of mechanical properties,localized corrosion resistance of a high purity Al-Zn-Mg-Cu alloy during non-isothermal aging(NIA)was investigated by hardness test,electrical conductivity test,tensile test,inte...The evolution of mechanical properties,localized corrosion resistance of a high purity Al-Zn-Mg-Cu alloy during non-isothermal aging(NIA)was investigated by hardness test,electrical conductivity test,tensile test,intergranular corrosion test,exfoliation corrosion test,slow strain rate tensile test and electrochemical test,and the mechanism has been discussed based on microstructure examination by optical microscopy,electron back scattered diffraction,scanning electron microscopy and scanning transmission electron microscopy.The NIA treatment includes a heating stage from 40℃to 180℃with a rate of 20℃/h and a cooling stage from 180℃to 40℃with a rate of 10℃/h.The results show that the hardness and strength increase rapidly during the heating stage of NIA since the increasing temperature favors the nucleation and the growth of strengthening precipitates and promotes the transformation of Guinier-Preston(GPI)zones toη'phase.During the cooling stage,the sizes ofη'phase increase with a little change in the number density,leading to a further slight increase of the hardness and strength.As NIA proceeds,the corroded morphology in the alloy changes from a layering feature to a wavy feature,the maximum corrosion depth decreases,and the reason has been analyzed based on the microstructural and microchemical feature of precipitates at grain boundaries and subgrain boundaries.展开更多
文摘Objective To observe the ultrasonic manifestations and age distribution of internal abdominal hernia in children.Methods Data of 53 children with internal abdominal hernia confirmed by operation were retrospectively analyzed.The ultrasonic findings were observed,and the age distribution of children was analyzed.Results Among 53 cases,"cross sign"was observed in 22 cases(22/53,41.51%),and"hernia ring beak sign"was detected in 26 cases(26/53,49.06%)by preoperative ultrasound,according to which 21 cases were diagnosed as internal abdominal hernia,with the accuracy of 39.62%(21/53).Meanwhile,manifestations of intestinal obstruction were noticed in 48 cases(48/53,90.57%),and intestinal necrosis was considered in 22 cases(22/53,41.51%).Four cases were misdiagnosed as intestinal perforation,appendicitis,intestinal atresia and volvulus,each in 1 case.The onset age of postoperative adhesive band internal hernia was larger than that of mesenteric hiatal hernia(P<0.05),while no significant difference of onset age was found among other types of internal abdominal hernias(all P>0.05).Intestinal ischemic necrosis was found in 25 cases,while the incidence of intestinal necrosis in children aged≤1 year,>1 and≤3 years,>3 and≤7 years and those>7 years was 66.67%(12/18),33.33%(4/12),36.36%(4/11)and 41.67%(5/12),respectively.Conclusion The characteristic ultrasonic findings of internal abdominal hernia in children included"cross sign"and"hernia ring beak sign".Internal abdominal hernia in children under 1 year had high risk of intestinal necrosis.
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
基金Project(202302AB080024)supported by the Department of Science and Technology of Yunnan Province,China。
文摘The evolution of mechanical properties,localized corrosion resistance of a high purity Al-Zn-Mg-Cu alloy during non-isothermal aging(NIA)was investigated by hardness test,electrical conductivity test,tensile test,intergranular corrosion test,exfoliation corrosion test,slow strain rate tensile test and electrochemical test,and the mechanism has been discussed based on microstructure examination by optical microscopy,electron back scattered diffraction,scanning electron microscopy and scanning transmission electron microscopy.The NIA treatment includes a heating stage from 40℃to 180℃with a rate of 20℃/h and a cooling stage from 180℃to 40℃with a rate of 10℃/h.The results show that the hardness and strength increase rapidly during the heating stage of NIA since the increasing temperature favors the nucleation and the growth of strengthening precipitates and promotes the transformation of Guinier-Preston(GPI)zones toη'phase.During the cooling stage,the sizes ofη'phase increase with a little change in the number density,leading to a further slight increase of the hardness and strength.As NIA proceeds,the corroded morphology in the alloy changes from a layering feature to a wavy feature,the maximum corrosion depth decreases,and the reason has been analyzed based on the microstructural and microchemical feature of precipitates at grain boundaries and subgrain boundaries.
