ObjectiveTo explore the therapeutic effect and mechanism of Guipitang on rats with myocardial ischemia. MethodFifty SD rats were divided into five groups： a control group， a model group， low and high-dose Guipitang （7.52， 15.04 g·kg^-1） groups， and a trimetazidine group （0.002 g·kg^-1）. By intragastric administration of vitamin D₃ and feeding rats with high-fat forage and injecting isoproterenol， the rat model of myocardial ischemia was established. After drug treatment of 15 d， an electrocardiogram （ECG） was performed to analyze the degree of myocardial injury. A fully automatic biochemical analyzer was used to detect the changes in the serum levels of total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， triglyceride （TG）， and low-density lipoprotein cholesterol （LDL-C）. Hematoxylin-eosin （HE） staining and Masson staining were used to observe myocardial histopathological changes. TdT-mediated dUTP nick end labeling （TUNEL） staining was used to detect cardiomyocyte apoptosis. Western blot was adopted to detect the protein levels of extracellular signal-regulated kinase 1/2 （ERK1/2）， phospho-ERK1/2 （p-ERK1/2）， p38 mitogen-activated protein kinase （p38 MAPK）， phospho-p38 MAPK （p-p38 MAPK）， B-cell lymphoma-2 （Bcl-2）-associated X （Bax）， Bcl-2， and cleaved cysteine aspartate proteolytic enzyme （cleaved Caspase-3）. ResultCompared with the control group， the ECG S-T segment decreased in the model group. The serum levels of TC， TG， and LDL-C were increased significantly （P<0.05）. The arrangement of myocardial tissue was disordered， and the proportion of cardiomyocyte apoptosis increased. The protein levels of cleaved Caspase-3， Bax， and p-p38 MAPK in the heart were increased， and the Bcl-2 expression was decreased （P<0.05）. Compared with the model group， the S-T segment downward shift was restored in the low and high-dose Guipitang groups and trimetazidine group， and the levels of TC， TG， and LDL-C were decreased. The protein expression of cleaved Caspase-3 and Bax in the heart dropped， and p-p38 MAPK and p-ERK1/2 protein expressions increased significantly （P<0.05）. The degree of myocardial injury was alleviated， and the proportion of cardiomyocyte apoptosis decreased. Bcl-2 protein expression was increased significantly in the low-dose Guipitang group （P<0.05）. ERK1/2 and p38 MAPK proteins had no significant difference among different groups. ConclusionGuipitang could alleviate myocardial injury and inhibit cardiomyocyte apoptosis in rats by activating the expression of ERK1/2 and p38 MAPK.

Objective To investigate the predictive efficacy of serum aminoterminal brain natriuretic pep-tide precursor(NT-proBNP),hypersensitive C-reactive protein(hs-CRP),D-dimer(D-D)and procalcitonin(PCT)in heart failure after acute myocardial infarction(AMI),Methods A total of 100 AMI patients admit-ted to the hospital from July 2021 to July 2023 were enrolled in the study as the observation group,In addi-tion,100 healthy people who underwent physical examination in the hospital during the same period were en-rolled as the control group,The serum levels of NT-proBNP,hs-CRP,D-D and PCT were detected and com-pared between the observation group and the control group,The AMI patients enrolled in the study were fur-ther divided into the heart failure group(31 cases)and the non-heart failure group(69 cases)according to the presence or absence of heart failure.The serum levels of NT-proBNP,hs-CRP,D-D,and PCT were compared between the two groups,Univariate analysis and multivariate Logistic regression analysis were used to analyze the risk factors of heart failure after AMI,Receiver operating characteristic(ROC)curve and decision curve a-nalysis(DCA)were used to analyze the predictive efficacy of serum NT-proBNP,hs-CRP,D-D and PCT for heart failure after AMI.Results The levels of serum NT-proBNP,hs-CRP,D-D and PCT in the observation group were higher than those in the control group(P＜0.05).The serum levels of NT-proBNP,hs-CRP,D-D and PCT in the complicated heart failure group were higher than those in the non-heart failure group(P＜0.05),Body mass index(BMI),smoking history,hypertension,number of diseased vessels,serum uric acid(SUA),low-density lipoprotein cholesterol(LDL-C),NT-proBNP,hs-CRP,D-D and PCT were risk factors for heart failure after AMI(P＜0.05).ROC curve analysis showed that the area under the curve(AUC)of combined detection of serum NT-proBNP,hs-CRP,D-D and PCT for predicting heart failure after AMI was 0.857(95％CI:0.811-0.948),the sensitivity was 96.12％,and the specificity was 91.28％,which were higher than the corresponding efficacy indexes of single detection(P＜0.05).DCA analysis showed that when the high-risk threshold was 0-0.99,the net benefit rate was greater than 0,which had clinical significance,When the threshold was 0-0.76,the net benefit rate of combined detection of serum NT-proBNP,hs-CRP,D-D and PCT was better than that of serum NT-proBNP,hs-CRP,D-D and PCT alone.Conclusion Combined detection of serum NT-proBNP,hs-CRP,D-D and PCT can improve the predictive efficiency of AMI compli-cated with heart failure,BMI,smoking history,hypertension,number of diseased vessels,SUA,LDL-C,NT-proBNP,hs-CRP,D-D and PCT are risk factors for AMI complicated with heart failure.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective To investigate the application of MRI T2*-mapping in the early damage of the medial meniscus posterior root(MMPR)in asymptomatic knee osteoarthritis(OA).Methods Eighty subjects were included in this study,35 were diagnosed with knee OA(OA group)and clinically confirmed MMPR injury,35 were asymptomatic OA group with gender and age matching,and 10 were normal control group.All subjects were examined by T2*-mapping.The T2*-mapping values at the bone attachment,middle part,and 1 cm bone attachment point of MMPR were measured in each group,and the consistency of T2*-mapping values between the knee OA group and the asymptomatic OA group was verified by the Kappa test.The T2*-mapping values of each measurement area were statistically compared,and the clinical diagnosis accuracy and other indicators of the T2*-mapping parameter values were statistically analyzed.Results The Kappa value of the knee OA group and the asymptomatic OA group analyzed by T2*-mapping was 0.787(P＜0.01),Kappa statistical analysis showed that there was a good consistency between the two diagnostic results.The T2*-mapping values of the knee OA group,asymptomatic OA group,and normal control group at the bone attachment,middle part,and 1 cm bone attachment point of MMPR showed that the T2*-mapping values of each measurement area in the knee OA group and asymptomatic OA group were higher than those in the normal control group(P＜0.05).The T2*-mapping values of the knee OA group were higher than those of the asymptomatic OA group,and the difference was statistically significant(P＜0.05).While the T2*-mapping values were used in the asymptomatic OA group to diagnose the early damage of MMPR,the sensitivity,specificity,accuracy,negative predictive value,and positive predictive value were 89.6％,88.9％,91.1％,87.5％,and 88.3％respectively.Conclusion T2*-mapping value may be used as a reference index to predict the progression of knee OA,and has a certain value in the early diagnosis of asymptomatic OA MMPR injury.