ObjectiveTo analyze the genomic characteristics of Streptococcus pyogenes (GAS) isolated from children with respiratory tract infections in a tertiary hospital in Jinshan District of Shanghai during 2013‒2024, to compare the changes in trend for genomic characteristics before and after 2000, and to provide scientific data for the prevention and control of GAS infections. MethodsGAS strains isolated from children with respiratory tract infections in this hospital were collected from 2013 to 2024. Antimicrobial susceptibility of the isolated strains to 12 antibiotics, including penicillin, cefotaxime, cefepime, linezolid, vancomycin, meropenem, chloramphenicol, ofloxacin, levofloxacin, erythromycin, clindamycin, and tetracycline, was determined using broth microdilution plate method. Besides, whole genome sequencing (WGS) was used to analyze multilocus sequence type (MLST), emm typing, carriage of superantigen genes, mobile genetic element (MGE), carriage of virulence gene, and genomic phylogenetic tree of the isolated strains. ResultsA total of 50 GAS strains were collected and identified from children with respiratory tract infections aged 4‒14 years old, and the resistance rates of those isolates to erythromycin, clindamycin, and tetracycline were 100.00%, 100.00%, and 86.00%, respectively. There were two emm types in the GAS isolates; the emm12 type accounted for 76.00% (38/50), corresponding to ST36 type, and the emm1 type accounted for 24.00% (12/50), corresponding to ST28, ST1274, and new-1 types. There was a statistically significant difference in the constitution of the MLST before and after 2020 (P=0.015). All the isolates carried the superantigen genes speC, speG, ssa, and smeZ. The predominant emm12 isolates belonged to the Clade Ⅱ, carrying the mobile elements ICE-emm12 (harboring erythromycin-resistance gene ermB and tetracycline-resistance gene tetM) and ΦHKU.vir (carrying virulence genes speC and ssa). The emm1 isolates carried the mobile elements ICE-HKU488 (harboring erythromycin-resistance gene ermB and tetracycline-resistance gene tetM) and ΦHKU488.vir (carrying virulence genes speC and ssa), and had close phylogenetical relationships with isolates from Hong Kong, China. No M1_UK new clone strains were found. The ST1274 isolates of emm1 were newly discovered in 2020‒2024, and belonged to a separate phylogenetic clade. ConclusionGAS strains isolated from children with respiratory tract infections in a tertiary hospital in Jinshan District of Shanghai exhibit a high resistance to erythromycin, clindamycin, and tetracycline. It is recommended that the clinical treatments change to use other antimicrobial drugs, such as penicillin, third-generation cephalosporins, and fluoroquinolones. During 2020‒2024, a new ST1274 clone strain is discovered in emm1 GAS isolates, without M1_UK new clone strains being found. It is essential to continuously concern locally prevalent GAS strains and perform early identification of MLST types to promptly monitor the internal changes of the bacterial population and potential prevalence of new clones.

Objective: To analyze the prevalence and determinants of comorbid overweight/obesity and elevated blood pressure among primary and secondary school students in Yangzhou City, and to explore the association between sleep patterns, eating behavior and the comorbidity of overweight/obesity and elevated blood pressure, so as to provide reference for developing prevention strategies targeting common comorbidities in students. Methods: By using stratified cluster random sampling, a total of 8 735 primary and secondary school students were selected from 36 schools in six counties of Yangzhou from October to November 2023. Students underwent physical examinations and a questionnaire survey was conducted using the questionnaire on students health status and influencing factors. The Chi square test was used to compare the detection rate of comorbid overweight/obesity and elevated blood pressure in different groups of primary and secondary school students. The Logistic regression model was used to explore the association between sleep and dietary behaviors and their combined effects and coexistence. Results: The detection rate of comorbid overweight/obesity and elevated blood pressure among primary and secondary school students in Yangzhou was 9.85%, which was higher among boys (12.14%) than girls (7.59%)( χ 2=50.86, P <0.01). After controlling for gender, residence, educational stage, parental education, smoking, drinking, and moderate to vigorous exercise, multivariate Logistic regression analysis showed that irregular breakfast consumption and inadequate daily sleep were associated with a higher risk of comorbidities compared with regular breakfast consumption and adequate daily sleep among overall and primary school students (overall: OR =1.52, 95% CI =1.18- 1.96 , primary school students: OR =2.79, 95% CI =1.61-4.82)(both P <0.05). From the perspective of primary school students of different genders, the risk of comorbidities in girls who consumed breakfast irregularly and had inadequate daily sleep was 3.59 times higher than that in girls who consumed breakfast irregularly and had inadequate daily sleep (95% CI =1.65-7.82, P <0.01). Conclusion The sleep patterns and breakfast behaviors of primary and secondary school students are found to be associated with comorbid overweight/obesity and elevated blood pressure, especially in primary school girls.

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.

ObjectiveThe lung mesenchymal stem cells （LMSCs） induced by D-galactose （D-gal） were intervened by Wenfei Huaxian decoction-containing serum to explore the mechanism of Wenfei Huaxian decoction in delaying the senescence of LMSCs through the nicotinamide phosphoribosyltransferase/silent information regulator 1 （NAMPT/SIRT1） signaling pathway. MethodWenfei Huaxian decoction-containing serum was prepared. LMSCs were isolated by gradient density centrifugation， and they were cultured and identified in vitro. The senescence model in vitro was established by stimulating cells via D-gal for 24 h. LMSCs cells were modeled after being treated with different volume fractions （5%， 10%， 20%， 40%， and 80%） of Wenfei Huaxian decoction-containing serum for 24 h， and the cell proliferation level was detected by methyl thiazolyl tetrazolium （MTT） method. The cells were randomly divided into blank serum group， model group， and high， medium， and low dose groups of Wenfei Huaxian decoction-containing serum. Senescence-associated β-galactosidase （SA-β-gal） staining was used to detect the senescence of LMSCs in each group. The content of NAD + was detected by colorimetry. The levels of senescence-associated factors （p16 and p53）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） in cell culture supernatant were detected by enzyme-linked immunosorbent assay （ELISA）. Western blot was used to detect the relative expression of senescence-associated proteins and NAMPT/SIRT1 signaling pathway-related proteins. ResultCompared with the blank serum group， the proliferation of LMSCs was significantly inhibited after D-gal stimulation for 24 h （P<0.01）. Compared with the model group， the proliferation of LMSCs could be promoted after intervention with the corresponding Wenfei Huaxian decoction-containing serum （P<0.05， P<0.01）. Compared with the blank serum group， the SA-β-gal staining of LMSCs in the model group after D-gal stimulation was enhanced， and the content of NAD⁺ was increased （P<0.01）. The expression levels of senescence factors p16 and p53， as well as SASP pro-inflammatory factors IL-6 and TNF-α in the cell culture supernatant， were significantly increased （P<0.01）. The expression of senescence-associated proteins p16， p21， and p53 increased （P<0.01）， and the protein expression of NAMPT， SIRT1， peroxisome proliferator-activated receptor γ coactivator 1α （PGC-1α）， and forkhead box family transcription factor O1 （FoxO1） decreased （P<0.01）. Compared with the model group， the SA-β-gal staining of LMSCs in each group of Wenfei Huaxian decoction-containing serum was significantly reduced， and the content of NAD⁺ was decreased （P<0.01）. The senescence factors （p16 and p53） and inflammatory factors （IL-6 and TNF-α） in the cell culture supernatant were significantly decreased （P<0.01）. The expression of senescence-associated proteins （P16， P21， and P53） decreased （P<0.05， P<0.01）. The protein expressions of NAMPT， SIRT1， PGC-1α， and FoxO1 were significantly up-regulated （P<0.05， P<0.01）. ConclusionWenfei Huaxian decoction can alleviate senescence and inflammatory response damage of D-gal-induced LMSCs， and its mechanism may be related to the regulation of the NAMPT/SIRT1 signaling pathway.

ObjectiveTo investigate the possible mechanism of Wenfei Huaxian Decoction （温肺化纤汤） in treatment of pulmonary fibrosis in systemic sclerosis-associated interstitial lung disease （SSc-ILD）. MethodsSixty C3H/He female rats were randomly divided into a control group， a model group， a pirfenidone group， and low-， medium-， and high-dose Wenfei Huaxian Decoction groups. The SSc-ILD model mice was established by subcutaneous injection of bleomycin solution 0.04 mg/d into the back of mice for 28 days in all groups but the control group. After successful modelling， the pirfenidone group was given pirfenidone capsule 300 mg/（kg·d） by gavage， the low-， medium- and high-dose Wenfei Huaxian Decoction groups were given Wenfei Huaxian Decoction 7.81， 15.62， and 31.24 g/（kg·d） by gavage， respectively， and the control group as well as the model group were given normal saline 0.1 ml/10 g by gavage， for a total of 21 days. At the end of the intervention， HE staining and Masson staining were used to observe the pathological changes in the skin and lung tissues； the hydroxyproline content of the skin and lung tissues was detected； the protein expression levels of endoplasmic reticulum stress-related proteins glucose-regulated protein 78 （BIP） and C/EBP homologous protein （CHOP） as well as those of nuclear factor kappa B （NF-κB） pathway p65 were measured by western blot； ELISA was performed to determine the expression levels of interferon gamma （IFN-γ）， interleukin 6 （IL-6） and tumour necrosis factor alpha （TNF-α） in serum of rats. ResultsThe results of HE and Masson staining indicated that compared with the control group， the dermis significantly thickened， the number of collagen fibers significantly enlarged， and the number of inflammatory cells significantly increased in the model group； the lung tissue showed a marked inflammatory cellular response with massive collagen fibre proliferation with inflammatory cell infiltration. Compared with the model group， the skin tissue and lung tissue collagen fibre proliferation significantly reduced and inflammatory cell infiltration reduced in the pirfenidone group and all dose groups of Wenfei Huaxian Decoction， and the effects of pirfenidone group and Wenfei Huaxian Decoction medium- and high-dose groups were basically comparable. Compared with the model group， the content of hydroxyproline in skin and lung tissue， the serum level of IFN-γ， IL-6 and TNF-α， and the expression levels of BIP and CHOP protein in lung tissue increased in model group （P＜0.05）. Compared with model group， the content of hydroxyproline in skin tissue of pirfenidone group， low-and medium-dose Wenfei Huaxian Decoction groups decreased， and the content of hydroxyproline in lung tissue of medium-dose Wenfei Huaxian Decoction group decreased. The serum level of IFN-γ， IL-6， TNF-α and the expression levels of BIP， CHOP and p65 protein in lung tissue of rats in pirfenidone group and high-dose Wenfei Huaxian Decoction group decreased （P＜0.05）. The content of hydroxyproline in lung tissue of medium-dose Wenfei Huaxian Decoction group was significantly lower than that of low-dose and high-dose Wenfei Huaxian Decoction group， and the serum level of IFN-γ， IL-6， TNF-α in low- and medium-dose Wenfei Huaxian Decoction group were higher than those in high-dose Wenfei Huaxian Decoction group. The expression level of BIP protein in high-dose group was significantly lower than that in low- and medium-dose Wenfei Huaxian Decoction groups （P＜0.05）. ConclusionWenfei Huaxian Decoction can improve the skin and lung fibrosis of SSc-ILD rats， which may act through anti-inflammation， inhibition of NF-κB pathway， and then inhibition of endoplasmic reticulum stress， which ultimately blocked the fibrotic process.

Objective To explore the possible mechanism of emodin in inhibiting proliferation,migration,and invasion of AGS cells and in suppressing the expressions of YAP1 and FOXD1.Methods Normal gastric cell GES-1 and gastric cancer cell AGS were cultured with different concentrations of emodin.CCK8 test,scratch test and Transwell assay were used to verify changes in the biological phenotype of AGS cells.TCGA database was applied to analyze expressions of HK2,YAP1 and FOXD1 in gastric cancer tissues and normal gastric tissues.Western blotting method was used to detect the impacts of emodin on HK2,YAP1 and FOXD1 proteins in AGS cells.Exogenous pyruvic acid was added to verify the changes in YAP1 and FOXD1.Results The IC50 of emodin was significantly higher in GES-1 cells than in AGS cells(P<0.05).CCK8 proliferation test,scratch test,and Transwell assay showed that emodin significantly inhibited the biological abilities of AGS(P<0.05 for comparisons).Analysis on the TCGA bioinformatics database found that the expression of key enzymes HK2 in the glycolysis pathway and oncogenes YAP1 and FOXD1 was significantly higher in gastric cancer tissues than in normal gastric tissues(P<0.05 for comparisons).Emodin significantly inhibited the protein expressions of key glycolytic enzymes HK2 and oncogenes YAP1 and FOXD1(P<0.05 for comparisons).With supplement of exogenous glycolytic metabolite pyruvate,the protein expressions of oncogenes YAP1 and FOXD1 significantly increased(P<0.05 for comparisons).Conclusions Emodin has a significant pharmacological inhibitory effect on gastric cancer AGS cells,markedly suppressing their biological phenotype.Emodin not only significantly inhibits the key enzyme HK2 in glycolysis metabolism,but also the protein expressions of oncogenes YAP1 and FOXD1.With the addition of exogenous pyruvate to enhance the glycolytic metabolic pathway,the protein expressions of oncogenes YAP1 and FOXD1 significantly increased.The above results suggest a close association of YAP1 and FOXD1 with glycolytic metabolism.Emodin may inhibit oncogenes YAP1 and FOXD1 through the glycolytic metabolism of gastric cancer AGS cells.

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.