OBJECTIVE To investigate the effects and potential mechanism of Qiangxin decoction on mitochondrion of rats with chronic heart failure （CHF）. METHODS The CHF model was established by ligating the left anterior descending branch of the coronary artery. Modeled rats were divided into model group， Qiangxin decoction low-dose and high-dose groups （12.25， 24.50 g/kg， calculated by crude drug）， and chemical medicine group （Sacubitril valsartan sodium tablets， 10.42 mg/kg）， with 10 rats in each group； control group was set up without treatment. Each group of rats was orally administered with the corresponding medication or normal saline twice a day for 28 consecutive days. After the last medication， the contents of N-terminal pro-brain natriuretic peptide （NT-proBNP） and adenosine triphosphate （ATP） in serum and phosphatidic acid （PA） and cardiolipin （CL） in myocardial tissue were all detected； the pathological damage and collagen fibrosis of rat myocardial tissue were observed； the apoptosis of myocardial cells was determined； the ultrastructure of myocardial tissue was observed； the protein expressions of mitofusin 1 （Mfn1）， Mfn2， optic atrophy protein 1 （OPA1） and dynamin-related protein 1 （Drp1） were all detected in myocardial tissue. RESULTS Compared with control group，the serum content of NT-proBNP， apoptotic rate of myocardial cells， and relative expressions of S-OPA1 and Drp1 proteins were all increased significantly； serum content of ATP，contents of PA and CL， and relative expressions of Mfn1， Mfn2 and L-OPA1 proteins were all significantly reduced （P＜0.05）. There were abnormal membrane tissue structure in various layers of myocardial tissue， degeneration and necrosis of myocardial cells， and severe fibrosis； the mitochondria were swollen， with reduced or absent cristae， and uneven matrix density. After intervention with Qiangxin decoction， the levels of the aforementioned quantitative indicators in serum and myocardial tissue of rats （excluding CL content in the Qiangxin decoction low- dose group） were significantly reversed （P＜0.05）； the pathological damage of myocardial tissue had significantly improved， fibrosis had significantly reduced， mitochondrial morphology tended to be normal， cristae had increased， and matrix density was uniform. CONCLUSIONS Qiangxin decoction can regulate myocardial mitochondrial function and structural integrity of CHF rats， thereby improving myocardial energy metabolism and antagonizing myocardial fibrosis， the mechanism of which may be associated with activating PA/Mfn/CL signaling pathway.

OBJECTIVE To investigate the effects and potential mechanism of Qiangxin decoction on mitochondrion of rats with chronic heart failure （CHF）. METHODS The CHF model was established by ligating the left anterior descending branch of the coronary artery. Modeled rats were divided into model group， Qiangxin decoction low-dose and high-dose groups （12.25， 24.50 g/kg， calculated by crude drug）， and chemical medicine group （Sacubitril valsartan sodium tablets， 10.42 mg/kg）， with 10 rats in each group； control group was set up without treatment. Each group of rats was orally administered with the corresponding medication or normal saline twice a day for 28 consecutive days. After the last medication， the contents of N-terminal pro-brain natriuretic peptide （NT-proBNP） and adenosine triphosphate （ATP） in serum and phosphatidic acid （PA） and cardiolipin （CL） in myocardial tissue were all detected； the pathological damage and collagen fibrosis of rat myocardial tissue were observed； the apoptosis of myocardial cells was determined； the ultrastructure of myocardial tissue was observed； the protein expressions of mitofusin 1 （Mfn1）， Mfn2， optic atrophy protein 1 （OPA1） and dynamin-related protein 1 （Drp1） were all detected in myocardial tissue. RESULTS Compared with control group，the serum content of NT-proBNP， apoptotic rate of myocardial cells， and relative expressions of S-OPA1 and Drp1 proteins were all increased significantly； serum content of ATP，contents of PA and CL， and relative expressions of Mfn1， Mfn2 and L-OPA1 proteins were all significantly reduced （P＜0.05）. There were abnormal membrane tissue structure in various layers of myocardial tissue， degeneration and necrosis of myocardial cells， and severe fibrosis； the mitochondria were swollen， with reduced or absent cristae， and uneven matrix density. After intervention with Qiangxin decoction， the levels of the aforementioned quantitative indicators in serum and myocardial tissue of rats （excluding CL content in the Qiangxin decoction low- dose group） were significantly reversed （P＜0.05）； the pathological damage of myocardial tissue had significantly improved， fibrosis had significantly reduced， mitochondrial morphology tended to be normal， cristae had increased， and matrix density was uniform. CONCLUSIONS Qiangxin decoction can regulate myocardial mitochondrial function and structural integrity of CHF rats， thereby improving myocardial energy metabolism and antagonizing myocardial fibrosis， the mechanism of which may be associated with activating PA/Mfn/CL signaling pathway.

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 understand the epidemiology of rubella and the genetic characteristics of the virus circulating during the period 2021-2022, providing basic scientific data for rubella prevention and control in China.Methods:National rubella incidence data for the period 2021-2022 were obtained from the Infectious Disease Surveillance System module and the Surveillance Report Management module of the China′s Disease Prevention and Control Information System. Positive rubella virus(RuV)isolates were obtained from the National Measles/Rubella Laboratory Network. Two nucleotide (nt) fragments [F1-480 (8 633-9 112 nt) and F2-633 (8 945-9 577 nt)] located in the E1 gene were amplified and determined by reverse transcription polymerase chain reaction (RT-PCR), and the target gene (E1-739) was obtained after collating and splicing. The sequences obtained in this study were used to construct a phylogenetic tree with the reported reference strains for genotype and lineage identification. Additionally, the phylogenetic analysis was performed to assess their genetic relatedness of RuV strains prevalent in China during 2018-2020 from GenBank database.Results:In 2021-2022, the rubella incidence in China was 0.06/100, 000 (2021: 840 cases; 2022: 784 cases), with cases primarily concentrated in the western and southern provinces. Age distribution analysis showed that rubella cases in 2021-2022 was mainly in children under 5 years of age (2021: 34.17%, 287/840; 2022: 42.09%, 330/784), with the highest proportion in children aged 0-2 years. Further analysis of the immunization history of cases revealed that in the 8-23 months age group, a significant proportion of cases had received only one dose of rubella containing vaccine (RCV); cases in the 2-14 years age group were mainly among children who had received two or more doses of RCV; however, cases over 15 years of age were primarily found in individuals who had not received RCV or had unknown immunization history. National virological surveillance data showed that totally 22 RuV virus isolates were obtained, from 6 provinces in China during 2021-2022, which belonged to lineage 1E-L2 (11 strains) and 2B-L2c (11 strains). And these viruses displayed high genetic homology with RuV prevalent from 2018 to 2020.Conclusions:The incidence of rubella in China was maintained at a low level during 2021-2022, and the prevalent RuV strains were lineage 1E-L2 and 2B-L2c.

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.