Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided i...Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.展开更多
Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-cham...Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.展开更多
Recent genome studies indicate that tree shrew is in the order or a closest sister of primates,and thus may be one of the best animals to model human diseases.In this paper,we report on a social defeat model of depres...Recent genome studies indicate that tree shrew is in the order or a closest sister of primates,and thus may be one of the best animals to model human diseases.In this paper,we report on a social defeat model of depression in tree shrew（Tupaia belangeri chinensis）.Two male tree shrews were housed in a pair-cage consisting of two independent cages separated by a wire mesh partition with a door connecting the two cages.After one week adaptation,the connecting door was opened and a brief fighting occurs between the two male tree shrews and this social conflict session consisted of 1 h direct conflict（fighting） and 23 h indirect influence（e.g.smell,visual cues） per day for 21 days.The defeated tree shrew was considered the subordinate.Compared with na？ve animals,subordinate tree shrews at the final week of social conflict session showed alterations in body weight,locomotion,avoidance behavior and urinary cortisol levels.Remarkably,these alterations persisted for over two weeks.We also report on a novel captive conditioning model of learning and memory in tree shrew.An automatic trapping cage was placed in a small closed room with a freely-moving tree shrew.For the first four trials,the tree shrew was not trapped when it entered the cage and ate the bait apple,but it was trapped and kept in the cage for 1 h on the fifth trial.Latency was defined as the time between release of the tree shrew and when it entered the captive cage.Latencies during the five trials indicated adaptation.A test trial 24 h later was used to measure whether the one-trial trapping during the fifth trial could form captive memory.Tree shrews showed much longer trapping latencies in the test trial than the adaptation trials.The N-methyl-d-aspartate（NMDA） receptor antagonist MK-801（0.2 mg/kg,i.p.）,known to prevent the formation of memory,did not affect latencies in the adaptation trails,but did block captive memory as it led to much shorter trapping latencies compared to saline treatment in the test trial.These results demonstrate a chronic social defeat model of depression and a novel one-trial captive conditioning model for learning and memory in tree shrews,which are important for mechanism studies of depression,learning,memory,and preclinical evaluation for new antidepressants.展开更多
对杭州湘湖剖面高密度连续采集的样品,分析了Fe2O3、FeO、TiO2、CaCO3、Al2O3及Fe2O3/FeO比值的高分辨记录,发现它们对全新世的重要地层界面都有较清晰的反映,同时揭示了不同沉积环境下地球化学背景值存在显著的差异.运用Fe3+/Fe2+与年...对杭州湘湖剖面高密度连续采集的样品,分析了Fe2O3、FeO、TiO2、CaCO3、Al2O3及Fe2O3/FeO比值的高分辨记录,发现它们对全新世的重要地层界面都有较清晰的反映,同时揭示了不同沉积环境下地球化学背景值存在显著的差异.运用Fe3+/Fe2+与年平均温度的关系式推算了各样品点的古温度值,并结合多种地球化学指标、14C测年结果以及岩性岩相组合特征,将湘湖地区全新世早、中期(10～3 ka B.P.)初步划分为4个气候发展阶段,并指出7.5～5.1 kaB.P.之间为本区的全新世气候适宜期.展开更多
文摘Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.
文摘Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.
基金supported by grants KSCX2-EW-R-12 and KSCX2-EW-J-23 from the Chinese Academy of Sciences
文摘Recent genome studies indicate that tree shrew is in the order or a closest sister of primates,and thus may be one of the best animals to model human diseases.In this paper,we report on a social defeat model of depression in tree shrew（Tupaia belangeri chinensis）.Two male tree shrews were housed in a pair-cage consisting of two independent cages separated by a wire mesh partition with a door connecting the two cages.After one week adaptation,the connecting door was opened and a brief fighting occurs between the two male tree shrews and this social conflict session consisted of 1 h direct conflict（fighting） and 23 h indirect influence（e.g.smell,visual cues） per day for 21 days.The defeated tree shrew was considered the subordinate.Compared with na？ve animals,subordinate tree shrews at the final week of social conflict session showed alterations in body weight,locomotion,avoidance behavior and urinary cortisol levels.Remarkably,these alterations persisted for over two weeks.We also report on a novel captive conditioning model of learning and memory in tree shrew.An automatic trapping cage was placed in a small closed room with a freely-moving tree shrew.For the first four trials,the tree shrew was not trapped when it entered the cage and ate the bait apple,but it was trapped and kept in the cage for 1 h on the fifth trial.Latency was defined as the time between release of the tree shrew and when it entered the captive cage.Latencies during the five trials indicated adaptation.A test trial 24 h later was used to measure whether the one-trial trapping during the fifth trial could form captive memory.Tree shrews showed much longer trapping latencies in the test trial than the adaptation trials.The N-methyl-d-aspartate（NMDA） receptor antagonist MK-801（0.2 mg/kg,i.p.）,known to prevent the formation of memory,did not affect latencies in the adaptation trails,but did block captive memory as it led to much shorter trapping latencies compared to saline treatment in the test trial.These results demonstrate a chronic social defeat model of depression and a novel one-trial captive conditioning model for learning and memory in tree shrews,which are important for mechanism studies of depression,learning,memory,and preclinical evaluation for new antidepressants.
文摘对杭州湘湖剖面高密度连续采集的样品,分析了Fe2O3、FeO、TiO2、CaCO3、Al2O3及Fe2O3/FeO比值的高分辨记录,发现它们对全新世的重要地层界面都有较清晰的反映,同时揭示了不同沉积环境下地球化学背景值存在显著的差异.运用Fe3+/Fe2+与年平均温度的关系式推算了各样品点的古温度值,并结合多种地球化学指标、14C测年结果以及岩性岩相组合特征,将湘湖地区全新世早、中期(10～3 ka B.P.)初步划分为4个气候发展阶段,并指出7.5～5.1 kaB.P.之间为本区的全新世气候适宜期.
