Objective To clone silent information regulator 1 (SIRT1) gene full-length cDNA,construct recombinant eukaryotic expression vector containing SIRT1 gene and its mutant T200I,E420K,so as to lay the foundation for further research of SIRT1 gene function.Methods RT-PCR amplified SIRT1 gene full-length cDNA.PCR products were cloned into the eukaryotic expression vector pcDNA3.1 (+) through double digestion and pcDNA3.1(+)-SIRT1 recombinant plasmid was obtained.Meanwhile,site-directed mutagenesis was applied to build its mutant pcDNA3.1 (+)-T200I and pcDNA3.1 (+)-E420K expression vector.Recombinant plasmid was identified by enzyme digestion and DNA sequencing and the recombinant eukaryotic expression vector of success was screened out.Results SIRT1 gene full-length cDNA was successfully cloned,and pcDNA3.1 (+)-SIRT1 eukaryotic expression vector and its mutant were also successfully constructed.Positive recombinant plasmid sequencing was compared after enzyme digestion,and it was completely consistent with the expected sequence.Transfected 293T cell line was established and HIS tagged SIRT1 protein was expressed.Conclusions We successfully constructed pcDNA3.1 (+)-SIRT1 and its mutant pcDNA3.1 (+)-T200I,pcDNA3.1 (+)-E420K eukaryotic expression vector,which may provide genetic material for biological function study of SIRT1 gene and its mutant T200I,E420K.

Abstract: Objective　To collect and organize malaria case data in Hubei Province from 2017 to 2021, compare and analyze the malaria epidemic characteristics on the before and after malaria elimination, and provide scientific support for Hubei Province to further optimize the comprehensive strategies to prevent re-transmission after the elimination of malaria. Methods The study was conducted by collecting the data of reported malaria cases of Hubei during 2017-2021 from the Infectious Disease Surveillance Reporting and Management System, and conducting the epidemiological characteristics of malaria on pre-elimination (2017-2019) and post-elimination (2020-2021). Results A total of 429 cases of imported malaria were reported in Hubei Province from 2017 to 2021, and the malaria epidemic showed an obvious trend of rising first and then falling. On the pre-malaria elimination, 374 malaria cases were reported, including 262 cases of P.falciparum (70.05%); on the post-malaria elimination, 55 malaria cases were reported, including 25 cases of P.falciparum (45.45%). There was a statistically significant difference in the proportion of infections caused by the four types of malaria parasites before and after the elimination of malaria (χ2=14.248, P<0.05). On the pre-malaria elimination, the peak of disease onset mainly occurred in January, July, and November; on the post-malaria elimination, the peak of disease onset mainly occurred in January to February, and December. Both before and after malaria elimination, the reported cases were mainly concentrated in Wuhan, Yichang, Huangshi, Xiangyang, Shiyan and Huanggang, but the range of cases showed a clear trend of narrowing. Before and after malaria elimination, malaria cases in Hubei Province were mainly among young and middle-aged males aged 30-49. The proportions of workers and migrant workers increased from 37.70% and 9.09% before the elimination to 50.91% and 18.18% after the elimination, respectively, with a statistically significant difference (χ2=17.839, P<0.05). The percentage of interval from onset of illness to initial diagnosis ≥ 5d decreased from 21.66% before the elimination to 10.91% after the elimination (χ2=6.448, P<0.05). The percentage of definitive diagnosis of malaria at initial diagnosis in town clinic increased from 18.18% before the elimination to 50.00% after the elimination. The proportion of malaria cases diagnosed by county-level medical institutions increased from 22.73% before the elimination to 34.55% after elimination. There was no statistically significant difference in the proportion of malaria cases diagnosed by medical institutions at all levels before and after the elimination of malaria (χ2=5.630, P>0.05). The proportion of cases with the interval between initial diagnosis and diagnosis within 24h increased from 43.85% before the elimination to 70.91% after the elimination. There was a statistically significant difference in the proportion of cases with the interval between initial diagnosis and diagnosis before and after the elimination of malaria (χ2=14.006, P<0.05). Before and after malaria elimination, all reported cases were mainly imported from African countries. Conclusions There are imported malaria cases reported every year in Hubei Province before and after the elimination of malaria, which poses a great challenge to the prevention of re-transmission. Therefore, it is necessary to strengthen the surveillance system, detect and standardize the treatment of imported malaria cases in a timely manner, conduct targeted retransmission risk surveys and assessments, and consolidate the achievements of malaria elimination.

Objective To understand the current status of paragonimiasis epidemic in western mountain areas in Hubei Prov-ince. Methods Four counties(cities)of Western Hubei Province(Xingshan,Enshi,Yunxi,Baokang)were selected as the investigation sites for active surveillance. Crabs were captured and the metacercariae of Paragonimus were detected. Meanwhile, the blood samples were collected from the residents in the surveillance sites and the unique IgG and IgM antibodies against Para-gonimus in the sera were detected by ELISA. In addition,a questionnaire survey about knowledge and behavior of prevention and control of paragonimiasis was taken among the residents. Results A total of 1143 residents were investigated in the active surveillance,the total positive rate of the serology test was 1.84%(21/1143),while the rates of the male and the female were 1.78%(10/562)and 1.89%(11/581),respectively,with no statistical significance between them(χ2=0.002,P>0.05). The average weight of 161 fresh-water crabs captured was 11.72 g,with the positive rate of 9.32%(15/161)and the infective density of 7.07 metacercariae per positive crab. The positive rates of the male and female crabs were 11.54%(9/78)and 7.23%(6/83), respectively(χ2=0.884,P>0.05),and the infective densities were 6.67 and 7.67 metacercariae per positive crab,respective-ly. Totally 1143 residents were investigated by questionnaires,and 0.44%of them had the behavior of eating raw or half-done fresh-water crab,and 0.87% of them had the behavior of drinking un-boiled stream water. Conclusions The transmission chain of paragonimiasis still exists in the nature environment of mountain area in Western Hubei Province. The positive rate of the second intermediate host rebounds in some investigation sites. Therefore,the measures of continuous surveillance and health education should be taken to avoid the appearance of the prevalence or outbreak.

OBJECTIVETo test the effects of 7 virus-encoded RNA silencing suppressors (RSSs) for enhancement of a plant virus-based vector system-mediated heterologous expression of green fluorescence protein (GFP) in Nicotiana benthamiana.

METHODSSeven transient expression vectors for the 7 RSSs were constructed and co-inoculated on the leaves of Nicotiana benthamiana with PVXdt-GFP vector, a novel Potato virus X-based plant expression vector, through agroinfiltration. The protein and mRNA expression levels of the reporter gene GFP in the co-inoculated Nicotiana leaves were examined by Western blotting, ELISA and RT-qPCR to assess the effect of the RSSs for GFP expression enhancement.

RESULTSThe 7 RSSs differed in the degree and duration of enhancement of heterologous GFP expression, and the p19 protein of Tomato bushy stunt virus (TBSV) induced the highest expression of GFP. African cassava mosaic virus AC2 protein and Rice yellow mettle virus P1 protein produced no obvious enhancement GFP expression.

CONCLUSIONTransient co-expression of RSSs suppresses host silencing response to allow high-level and long-term expression of heterologous genes in plant, but the optimal RSS has to be identified for each plant virus-based expression vector system.

Genetic Vectors ; Green Fluorescent Proteins ; genetics ; Plant Viruses ; genetics ; Plants, Genetically Modified ; genetics ; metabolism ; Potexvirus ; genetics ; RNA Interference ; Tobacco ; genetics ; metabolism

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.