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.

To investigate the therapeutic effect and related mechanisms of hordenine on ovalbumin (OVA)-induced allergic rhinitis (AR) in rats, HE and AB-PAS staining were used to detect the improvement of pathological damage to the nasal mucosa induced by hordenine. ELISA was employed to detect the effect of hordenine on OVA-sIgE in serum and IL-4 in the nasal mucosa supernatant of rats. IHC and Western blot experiments were undertaken to examine the effect of hordenine on Th1/Th2 cell balance. Bioinformatics analysis was performed to predict pathways, which were verified by in vivo and in vitro experiments. The experimental results showed that hordenine could alleviate the behavioral manifestations of OVA-induced AR rats, alleviate nasal mucosal pathological damage caused by AR, and reduce the secretion of OVA-sIgE and IL-4. In addition, hordenine could regulate the Th1/Th2 balance. Bioinformatics analysis results showed that the potential pathway of action of hordenine on AR was the phosphoinositide 3 kinase (PI3K)/protein kinase B (Akt) signaling pathway. The in vivo experimental results showed that the expression of PI3K and p-Akt proteins in the nasal mucosa of the model group rats was significantly increased (P < 0.01), and that the protein expression level was significantly decreased after the administration of hordenine, which was also confirmed by an in vitro experiment. This study suggests that hordenine may regulate Th1/Th2 cell balance through the PI3K/Akt signaling pathway, thereby exerting an alleviating effect on OVA-induced AR.

ObjectiveThis paper proposes a novel real-time bedside pulmonary ventilation monitoring method for the diagnosis of chronic obstructive pulmonary disease (COPD), based on electrical impedance tomography (EIT). Four indicators—center of ventilation (CoV), global inhomogeneity index (GI), regional ventilation delay inhomogeneity (RVDI), and the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC)—are calculated to enable the spatiotemporal assessment of COPD. MethodsA simulation of the respiratory cycles of COPD patients was first conducted, revealing significant differences in certain indicators compared to healthy individuals. The effectiveness of these indicators was then validated through experiments. A total of 93 subjects underwent multiple pulmonary function tests (PFTs) alongside simultaneous EIT measurements. Ventilation heterogeneity under different breathing patterns—including forced exhalation, forced inhalation, and quiet tidal breathing—was compared. EIT images and related indicators were analyzed to distinguish healthy individuals across different age groups from COPD patients. ResultsSimulation results demonstrated significant differences in CoV, GI, FEV₁/FVC, and RVDI between COPD patients and healthy individuals. Experimental findings indicated that, in terms of spatial heterogeneity, the GI values of COPD patients were significantly higher than those of the other two groups, while no significant differences were observed among healthy individuals. Regarding temporal heterogeneity, COPD patients exhibited significantly higher RVDI values than the other groups during both quiet breathing and forced inhalation. Moreover, during forced exhalation, the distribution of FEV₁/FVC values further highlighted the temporal delay heterogeneity of regional lung function in COPD patients, distinguishing them from healthy individuals of various ages. ConclusionEIT technology effectively reveals the spatiotemporal heterogeneity of regional lung function, which holds great promise for the diagnosis and management of COPD.

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:To analyze the factors affecting overall survival(OS)of adult patients with core-binding factor acute myeloid leukemia(CBF-AML)and establish a prediction model.Methods:A total of 216 newly diagnosed patients with CBF-AML in the First Affiliated Hospital of Zhengzhou University from May 2015 to July 2021 were retrospectively analyzed.The 216 CBF-AML patients were divided into the training and the validation cohort at 7:3 ratio.The Cox regression model was used to analyze the clinical factors affecting OS.Stepwise regression was used to establish the optimal model and the nomogram.Receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to evaluate the model performance.Results:Age(≥ 55 years old),peripheral blood blast(≥80％),fusion gene(AML1-ETO),KIT mutations were identified as independent adverse factors for OS.The area under the ROC curve at 3-year was 0.772 and 0.722 in the training cohort and validation cohort,respectively.The predicted value of the calibration curve is in good agreement with the measured value.DCA shows that this model performs better than a single factor.Conclusion:This prediction model is simple and feasible,and can effectively predict the OS of CBF-AML,and provide a basis for treatment decision.

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:To develop and validate a predictive model of 28-day mortality in sepsis based on lactate dehydrogenase-to-albumin ratio (LAR).Methods:Sepsis patients diagnosed in the department of intensive care medicine of the First Affiliated Hospital of Soochow University from August 1, 2017 to September 1, 2022 were retrospective selected. Clinical data, laboratory indicators, disease severity scores [acute physiology and chronic health evaluation Ⅱ(APACHEⅡ), sequential organ failure assessment (SOFA)] were collected. Patients were divided into death group and survival group according to whether they died at 28 days, and the difference between the two groups was compared. The dataset was randomly divided into training set and validation set according to 7∶3. Lasso regression method was used to screen the risk factors affecting the 28-day death of sepsis patients, and incorporating multivariate Logistic regression analysis (stepwise regression) were included, a prediction model was constructed based on the independent risk factors obtained, and a nomogram was drawn. The nomogram prediction model was established. Receiver operator characteristic curve (ROC curve) was drawn to analyze and evaluate the predictive efficacy of the model. Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and clinical practicability of the model, respectively.Results:A total of 394 patients with sepsis were included, with 248 survivors and 146 non-survivors at 28 days. Compared with the survival group, the age, proportion of chronic obstructive pneumonia, respiratory rate, lactic acid, red blood cell distribution width, prothrombin time, activated partial thromboplastin time, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, blood potassium, blood phosphorus, LAR, SOFA score, and APACHEⅡ score in the death group were significantly increased, while oxygenation index, monocyte count, platelet count, fibrinogen, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and blood calcium were significantly reduced. In the training set, LAR, age, oxygenation index, blood urea nitrogen, lactic acid, total cholesterol, fibrinogen, blood potassium and blood phosphorus were screened by Lasso regression. Multivariate Logistic regression analysis finally included LAR [odds ratio ( OR) = 1.029, 95% confidence interval (95% CI) was 1.014-1.047, P < 0.001], age ( OR = 1.023, 95% CI was 1.005-1.043, P = 0.012), lactic acid ( OR = 1.089, 95% CI was 1.003-1.186, P = 0.043), oxygenation index ( OR = 0.996, 95% CI was 0.993-0.998, P = 0.002), total cholesterol ( OR = 0.662, 95% CI was 0.496-0.865, P = 0.003) and blood potassium ( OR = 1.852, 95% CI was 1.169-2.996, P = 0.010). A total of 6 predictor variables were used to establish a prediction model. ROC curve showed that the area under the curve (AUC) of the model in the training set and validation set were 0.773 (95% CI was 0.715-0.831) and 0.793 (95% CI was 0.703-0.884), which was better than APACHEⅡ score (AUC were 0.699 and 0.745) and SOFA score (AUC were 0.644 and 0.650), and the cut-off values were 0.421 and 0.309, the sensitivity were 62.4% and 82.2%, and the specificity were 82.2% and 68.9%, respectively. The results of Hosmer-Lemeshow test and calibration curve showed that the predicted results of the model were in good agreement with the actual clinical observation results, and the DCA showed that the model had good clinical application value. Conclusion:The prediction model based on LAR has a good predictive value for 28-day mortality in patients with sepsis and can guide clinical decision-making.

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.