ObjectiveMicrotube associated protein-2 （MAP-2）， alpha-tubulin （α-tubulin）， and synaptophysin （SYP） are important proteins in neuronal signal communication. This paper observed the effects of modified Zhigancao Tang on the expression of serum α-Synuclein （α-Syn） and its oligomers， MAP-2， α-tubulin， and SYP of patients with liver and kidney deficiency of Parkinson's disease （PD）， analyzed their correlation， and evaluated the therapeutic effect of modified Zhigancao Tang in patients with liver and kidney deficiency of PD based on α-Syn transmission pathway mediated by neuronal communication in vivo. MethodsA total of 60 patients with PD who met the inclusion criteria were randomly divided into a treatment group （30 cases） and a control group （30 cases）. Both groups were treated on the basis of PD medicine， and the treatment group was treated with modified Zhigancao Tang. Both groups were treated for 12 weeks. The changes in UPDRS score， TCM syndrome score， and expression of serum α-Syn and its oligomers， MAP-2， α-tubulin， and SYP were observed before and after 12 weeks of treatment in each group. The correlation between the above-mentioned serum biological indexes and the levels of serum α-Syn and its oligomers was analyzed. ResultsAfter treatment， the TCM syndrome score， UPDRS score， UPDRS-Ⅱ score， and UPDRS-Ⅲ score of the treatment group were significantly decreased （P<0.05， P<0.01）. The UPDRS score， UPDRS-Ⅱ score， and UPDRS-Ⅲ scores in the treatment group were significantly decreased compared with those in the control group after treatment （P<0.05）. After treatment， the total effective rate of the control group was 63.3% （19/30）， and that of the treatment group was 86.7% （26/30）. The clinical effect of the observation group was better than the control group （Z=-2.03， P<0.05）. The total effective rate of the observation group was better than that of the control group， and the difference was statistically significant （χ²=5.136， P<0.05）. After treatment， the oligomer level of serum α-Syn and MAP-2 level in the treatment group were significantly decreased （P<0.05， P<0.01）. The levels of serum α-Syn and its oligomers， as well as α-tubulin in the treatment group， were significantly decreased compared with those in the control group after treatment （P<0.05， P<0.01）. Serum α-Syn was correlated with serum MAP-2 and α-Syn oligomer in patients with PD （P<0.05， P<0.01） but not correlated with serum SYP . Serum α-Syn oligomers of patients with PD were correlated with serum MAP-2 and α-tubulin （P<0.05， P<0.01） but not correlated with serum SYP level. Serum SYP of patients with PD was correlated with serum MAP-2 （P<0.05）. ConclusionModified Zhigancao Tang has a therapeutic effect on patients with liver and kidney deficiency of PD by inhibiting the production of α-Syn oligomers and intervening α-Syn microtubule transport pathway in vivo.

Modern Chinese medicine studies have confirmed that the interaction between traditional Chinese medicine(TCM)and intestinal flora is the key to the treatment of diseases with tradi-tional Chinese medicine.This interplay includes such activities as:traditional Chinese medicine can be metabolized by intestinal flora into effective components with different biological activities from its precursors;TCM chemicals improve the composition of gut microbiota,consequently ameliorating its dysfunction as well as associated pathological conditions;and gut microbiota mediate the interactions between the multiple chemicals in TCM.There-fore,it becomes an important way to understand the modern sci-entific connotation of traditional Chinese medicine theory to study the pharmacological mechanism of the efficacy of traditional Chi-nese medicine by targeting Gut microbiota.

Aim To explore the ameliorative effects of berberine(BBR)on diabetic nephropathy(DN)in mice and investigate its potential mechanisms through transcriptomic analysis.Methods 8-week-old db/db mice were randomly assigned into four groups:model group(DN group),BBR 50 mg·kg-1 group(BBR-L group),BBR 100 mg·kg-1 group(BBR-H group),and empagliflozin 10 mg·kg-1 group(EMPA group).Age-matched db/m mice were used as the control group(NC group),with eight mice in each group.Each group received intragastric administration once daily for eight weeks.After the treatment,serum,u-rine,and kidney samples were collected to evaluate re-nal function indicators and observe renal pathological changes.Differentially expressed genes(DEGs)in kidney tissue were identified through transcriptomic a-nalysis,followed by KEGG and GO enrichment analy-sis.Potential targets were further validated using mo-lecular docking,molecular dynamics simulations,West-ern blot,and immunohistochemistry.Results Both BBR and EMPA significantly reduced fasting blood glu-cose levels in DN mice,improved renal function,and alleviated renal injury and fibrosis.Compared to the NC group,855 DEGs were identified in the DN group,while 194 DEGs were identified in the BBR-H group compared to the DN group.KEGG enrichment analysis indicated that the mechanisms underlying BBR's effects on DN were primarily related to type 1 diabetes and bile secretion pathways.Molecular docking results demonstrated a strong binding affinity between BBR and FXR and a moderate binding affinity with SHP.Molecular dynamics simulations corroborated the doc-king results.FXR and SHP protein expression signifi-cantly decreased in the DN group compared to the NC group.At the same time,BBR treatment significantly increased the expression of these proteins compared to the DN group.Conclusion BBR may mitigate DN-in-duced renal injury by modulating bile acid and lipid homeostasis through the FXR-SHP pathway.

Objective:The main purpose of this study is to analyse the current situation and inequity status of children's medical security in China from the vision of children first.Methods:Using data from the China Family Panel Studies 2020 and based on the framework of universal health coverage cube,multivariate regression is used to analyse the differences in medical security between children and adults and among groups of children.Results:The participation rate of children in China is 80.96%,out-of-pocket ratios are 64.71%and 90.09%for inpatient and outpatient groups respectively.In terms of participation rate,insured children are less than that of adults(OR=0.434,P＜0.01);within children's groups,attending school(OR=2.075,P＜0.01)significantly increases children's participation rate,while left-behind by parent(s)(OR=0.791,P＜0.05)significantly decrease children's participation rate.With respect to service and cost coverage,children have higher out-of-pocket ratios compared to adults(β=0.066,P＜0.01);within children's groups,children eged 6 years and older have lower out-of-pocket medical expenses(β＜-0.316,P＜0.01),children with higher family income(β＜-0.022,P＜0.05),participated(β=-0.033,P＜0.01),and hospitalized(β=-0.270,P＜0.01)have lower out-of-pocket ratios.Conclusion:Double in equality exists in children's medical security in China.The level of children's health security in China is significantly lower than that of adults;within children's groups,children aged 0～5 years,not in school,left-behind by parent(s),and from lower-income families are more vulnerable.It is proposed to focus on increasing the participation rate of children through measures such as optimizing the contribution for children and launching family joint insurance.Policy design should also consider the needs of children and raise the level of children's benefits.Meanwhile,the focus should be on helping vulnerable groups in children,so as to ultimately achieve"children first"in health security.

Objective:To analyze the efficiency of human resource allocation and its spatial distribution characteristics of district-level Center for Disease Control and Prevention(CDC)in city N of Jiangsu Province in 2020,in order to provide a strong decision-making reference for optimizing and strengthening the CDC talent team.Methods:The efficiency of human resources of district-level CDC of City N in2020 was measured using the Super-Efficiency SBM model,and the spatial association pattern was analyzed using the natural break point classification method and Moran's index,with the visualization presented through LISA maps.Results:The overall level of human resource efficiency in district-level CDC of City N is relatively high.However,spatially,there are significant differences among the regions,showing a trend of high efficiency in the central areas and low efficiency at the ends.Moran's index and LISA maps indicate a negative spatial correlation in efficiency,with a low-high(L-H)cluster centered on Area L and a high-low(H-L)cluster centered on Area J.The high-high(H-H)cluster pattern has not yet formed,exhibiting a characteristic of interspersed high and low efficiency.Conclusions:There are regional differences in the human resource efficiency of the Disease Control Center in City N,and the spatial cluster pattern needs to be optimized.It is recommended to focus on efficiency improvement in Areas P and L,formulate appropriate policies,and promote coordinated regional development.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To investigate the early cognitive development characteristics of children with Williams syndrome(WS)at different age stages.Methods From September 2018 to June 2023,106 children diagnosed with WS at the Department of Pediatric Health Care,Children's Hospital,Zhejiang University School of Medicine,aged 1 to<5 years,were prospectively enrolled.All children underwent Gesell developmental diagnostic assessments to analyze the cognitive development characteristics of WS children across different age groups.Results The average age of the 106 WS children was(3.1±1.2)years;58 were male,and 48 were female.There were no significant differences in developmental levels between males and females in the five domains of gross motor skills,fine motor skills,language,personal-social skills,and adaptive behavior(P>0.05).The incidence rates of mild,moderate,severe,and profound developmental disabilities among children of different age groups showed no significant differences(P>0.05).Comparisons of developmental levels in gross motor skills,language,personal-social skills,and adaptive behavior among different age groups were also not statistically significant(P>0.05).With the increase of age,the developmental level of fine motor skills showed a decreasing trend(P<0.05).There were no significant differences in verbal IQ and non-verbal IQ within each age group of WS children(P>0.05).Conclusions The overall developmental level of WS children stabilizes with age,and their early language abilities do not significantly exceed their non-verbal abilities.

Objective To explore the research status and hot highlights in the field of Alzheimer's disease nursing,and provide reference and direction for future research.Methods The high-level articles on Alzheimer's disease nursing during 2012 to 2022 were collected from Web of Science core database,were analyzed and visualized by the CiteSpace 5.8.R3C software.Re-sults 956 articles were included in the Web of Science core database.The demand and focus on AD nursing research increased year by year.United States America had the largest number of articles(175 articles),followed by France(43 articles)and Chi-na(31 articles).Minnesota University and Harvard Medical School had the largest number of articles(11 articles).The authors'analysis shows that BRUNO VELLAS,an academician of the French Academy of Sciences,had the largest number of articles.Keyword co-occurrence analysis shows that the research in the past decade mainly focuses on"nursing home","people"and"quality of life","long-term care"and"exercise therapy"may become the key research directions in the future.Conclusion Domestic scholars should improve the social security system of long-term care,promote"people-oriented"humanistic nursing services and develop appropriate sports training programs in the future.