In today's society， depression is a kind of highly prevalent chronic mental illness. It leads to a high disability rate and a heavy economic burden. Depression is defined by fundamental symptoms of low mood and diminished pleasure. Its causes and mechanisms remain unclear， and it presents a broad spectrum of symptoms and a persistent nature that significantly impacts both physical and mental well-being. Treatment in Western medicine primarily focuses on alleviating symptoms， yet it entails numerous adverse effects and contraindications. Traditional Chinese medicine （TCM） treatment is based on resolving depression， which is often accompanied by soothing liver， and the key medicine is Bupleuri Radix. Bupleuri Radix associated prescriptions refer to a class of prescriptions using Bupleuri Radix as the sovereign medicinal or having a high dose of Bupleuri Radix， which are widely used in the field of anti-depression. Previous studies from animal experiments， clinical research， and modern pharmacological research have confirmed that Bupleuri Radix associated prescriptions have precise anti-depression efficacy in multiple ways and at multiple levels， but lack a comprehensive and systematic summarization. This paper summarized and analyzed the literature related to the clinical application and mechanism of action of Bupleuri Radix associated prescriptions in anti-depression treatment. The results showed that the anti-depression mechanism of the Bupleuri Radix associated prescriptions （such as Xiaochaihu Tang， Xiaoyao San， Sini San， Chaihu Shugan San， and Chaihu jia Longgu Muli San） was associated with the effects of regulating monoamine neurotransmitters， the brain-derived neurotrophic factor （BDNF）， intestinal flora， and the hypothalamic-pituitary-adrenal （HPA） axis， inhibiting inflammatory responses， and modulating related signaling pathways. Applying them in clinical practice can effectively alleviate patient symptoms， lower the TCM syndrome score and the severity of depression， and also reduce adverse reactions. This underscores advantages of TCM in depression treatment， which offers patients a secure， effective， and more individualized alternative treatment regimen. On this basis， the shortcomings of current studies and the future trend were analyzed. This study aimed to provide an evidence-based medicine basis for the research and development of novel antidepressant medications.

ObjectiveTo identify the patients with metabolic dysfunction-associated fatty liver disease （MAFLD） among the health check-up population， and to perform stratified management of patients with the low， medium， and high risk of advanced fibrosis based on noninvasive fibrosis scores. MethodsA cross-sectional study was conducted among 3 125 individuals who underwent physical examination in Beijing Physical Examination Center from December 2017 to December 2019， and they were divided into MAFLD group with 1 068 individuals and non-MAFLD group with 2 057 individuals. According to BMI， the MAFLD group was further divided into lean MAFLD group （125 individuals with BMI<24 kg/m²） and non-lean MAFLD group （943 individuals with BMI≥24 kg/m²）. Indicators including demographic data， past history， laboratory examination， and liver ultrasound were compared between groups. Fibrosis-4 （FIB-4） score， NAFLD fibrosis score （NFS）， aspartate aminotransferase-to-platelet ratio index （APRI）， and BARD score were calculated for the patients in the MAFLD group to assess the risk of advanced fibrosis. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. A logistic regression analysis was used to investigate the influence of each indicator in MAFLD. ResultsCompared with the non-MAFLD group， the MAFLD group had significantly higher age （Z=-9.758， P<0.05）， proportion of male patients （χ²=137.555， P<0.05）， and levels of body weight （Z=-27.987， P<0.05）， BMI （Z=-32.714， P<0.05）， waist circumference （Z=-31.805， P<0.05）， hip circumference （Z=-26.342， P<0.05）， waist-hip ratio （Z=-28.554， P<0.05）， alanine aminotransferase （ALT） （Z=-25.820， P<0.05）， aspartate aminotransferase （AST） （Z=-16.894， P<0.05）， gamma-glutamyl transpeptidase （GGT） （Z=-25.069， P<0.05）， alkaline phosphatase （Z=-12.533， P<0.05）， triglyceride （Z=-27.559）， total cholesterol （Z=-7.833， P<0.05）， low-density lipoprotein cholesterol （LDL-C） （Z=-8.222， P<0.05）， and uric acid （UA） （Z=-20.024， P<0.05）， as well as a significantly higher proportion of patients with metabolic syndrome （MetS） （χ²=578.220， P<0.05）， significantly higher prevalence rates of hypertension （χ²=241.694， P<0.05）， type 2 diabetes （χ²=796.484， P<0.05）， and dyslipidemia （χ²=369.843， P<0.05）， and a significant reduction in high-density lipoprotein cholesterol （HDL-C） （Z=23.153， P<0.001）. The multivariate logistic regression analysis showed that male sex （odds ratio ［OR］=1.45， 95% confidence interval ［CI］： 1.203‍ ‍—‍ ‍1.737）， ALT （OR=1.05， 95%CI： 1.046‍ ‍—‍ ‍1.062）， LDL-C （OR=1.23， 95%CI： 1.102‍ ‍—‍ ‍1.373）， and comorbidity with MetS （OR=5.97， 95%CI： 4.876‍ ‍—‍ ‍7.316） were independently associated with MAFLD. Compared with the non-lean MAFLD group， the lean MAFLD group had significantly higher age （Z=3.736， P<0.05） and HDL-C （Z=2.679， P<0.05） and significant reductions in the proportion of male patients （χ²=28.970， P<0.05）， body weight （Z=-14.230， P<0.05）， BMI （Z=-18.188， P<0.05）， waist circumference （Z=-13.451， P<0.05）， hip circumference （Z=-13.317， P<0.05）， ALT （Z=-4.519， P<0.05）， AST （Z=-2.258， P<0.05）， GGT （Z=-4.592， P<0.05）， UA （Z=-4.415， P<0.05）， the proportion of patients with moderate or severe fatty liver disease or MetS （χ²=42.564， P<0.05）， and the prevalence rates of hypertension （χ²=12.057， P<0.05） and type 2 diabetes （χ²=3.174， P<0.05）. Among the patients with MAFLD， 10 patients （0.9%） had an FIB-4 score of >2.67， 4 patients （0.4%） had an NFS score of >0.676， 8 patients （0.7%） had an APRI of >1， and 551 patients （51.6%） had a BARD score of ≥2. ConclusionThere is a relatively high prevalence rate of MAFLD among the health check-up population in Beijing， but with a relatively low number of patients with a high risk of advanced fibrosis， and such patients need to be referred to specialized hospitals for liver diseases.

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.

ObjectiveTo screen for the patients with metabolic dysfunction-associated fatty liver disease （MAFLD） among the physical examination population， to observe the characteristics of MAFLD patients， and to compare the differences in lifestyle between the MAFLD population and the non-MAFLD population. MethodsA cross-sectional study was conducted among 6 206 individuals who underwent physical examination in a physical examination institution in Beijing from December 2015 to December 2019， and according to the new diagnostic criteria for MAFLD， the examination population was divided into MAFLD group and non-MAFLD group. Based on body mass index （BMI）， the MAFLD group was further divided into lean MAFLD group （BMI<24 kg/m²） and non-lean MAFLD group （BMI ≥24 kg/m²）. Related data were compared between groups， including demographic indicators， education level， work pressure， physical measurement indicators， and lifestyles such as sleep， diet， and exercise. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. ResultsOf all individuals in this study， 1 926 （31.1%） had MAFLD and 4 280 （68.9%） did not have MAFLD. Compared with the non-MAFLD group， the MAFLD group had significantly higher age （Z=-14.459， P<0.001）， proportion of male patients （χ²=72.004， P<0.001）， work pressure （χ²=7.744， P=0.005）， body weight （Z=-43.508， P<0.001）， BMI （Z=-47.621， P<0.001）， waist circumference （Z=-48.515， P<0.001）， hip circumference （Z=-42.121， P<0.001）， and waist-hip ratio （Z=-43.535， P<0.001）， as well as a significantly lower education level （χ²=33.583， P<0.001）. In terms of behavior， the MAFLD group had a significantly shorter sleep time （χ²=5.820， P=0.016） and a significantly faster eating speed （χ²=74.476， P<0.001）. In terms of diet， the patients in the MAFLD group consumed more high-sodium， high-sugar， and high-calorie diets （χ²=42.667， P<0.001） and low-fiber diet （χ²=4.367， P=0.008）. In terms of exercise， the MAFLD group had a significantly higher proportion of patients without exercise habits （χ²=10.278， P=0.001）. Further analysis showed that there were 202 individuals （10.5%） in the lean MAFLD group and 1 724 （89.5%） in the non-lean MAFLD group. Compared with the non-lean MAFLD group， the lean MAFLD group had significantly higher age （Z=3.368， P=0.001） and education level （χ²=9.647， P=0.002） and significantly lower proportion of male patients （χ²=27.664， P<0.001）， body weight （Z=-18.483， P<0.001）， BMI （Z=-23.286， P<0.001）， waist circumference （Z=-18.565， P<0.001）， and hip circumference （Z=-18.097， P<0.001）， and in terms of behavior， the non-lean MAFLD group had a significantly faster eating speed （χ²=4.549， P=0.033）. ConclusionThere is a relatively high prevalence rate of MAFLD among the physical examination population in Beijing， with a higher number of people with unhealthy lifestyles compared with the non-MAFLD population.

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.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder worldwide, causing dementia and affecting millions of individuals. One prominent characteristic in the brains of AD patients is glucose hypometabolism. In the context of galactose metabolism, intracellular glucose levels are heightened. Galactose mutarotase (GALM) plays a crucial role in maintaining normal galactose metabolism by catalyzing the conversion of β-D-galactose into α-D-galactose (α-D-G). The latter is then converted into glucose-6-phosphate, improving glucose metabolism levels. However, the involvement of GALM in AD progression is still unclear. In the present study, we found that the expression of GALM was significantly increased in AD patients and model mice. Genetic knockdown of GALM using adeno-associated virus did not change the expression of amyloid precursor protein (APP) and APP-cleaving enzymes including a disintegrin and metalloprotease 10 (ADAM10), β-site APP-cleaving enzyme 1 (BACE1), and presenilin-1 (PS1). Interestingly, genetic overexpression of GALM reduced APP and Aβ deposition by increasing the maturation of ADAM10, although it did not alter the expression of BACE1 and PS1. Further electrophysiological and behavioral experiments showed that GALM overexpression significantly ameliorated the deficits in hippocampal CA1 long-term potentiation (LTP) and spatial learning and memory in AD model mice. Importantly, direct α-D-G (20 mg/kg, i.p.) also inhibited Aβ deposition by increasing the maturation of ADAM10, thereby improving hippocampal CA1 LTP and spatial learning and memory in AD model mice. Taken together, our results indicate that GALM shifts APP processing towards α-cleavage, preventing Aβ generation by increasing the level of mature ADAM10. These findings indicate that GALM may be a potential therapeutic target for AD, and α-D-G has the potential to be used as a dietary supplement for the prevention and treatment of AD.
Animals ; ADAM10 Protein/metabolism* ; Alzheimer Disease/pathology* ; Amyloid Precursor Protein Secretases/metabolism* ; Disease Models, Animal ; Humans ; Mice ; Amyloid beta-Peptides/metabolism* ; Male ; Mice, Transgenic ; Membrane Proteins/metabolism* ; Cognitive Dysfunction/pathology* ; Mice, Inbred C57BL ; Amyloid beta-Protein Precursor/metabolism* ; Female ; Hippocampus/metabolism* ; Long-Term Potentiation/physiology*

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.