Objective To investigate the neural basis of glucagon-like peptide-1(GLP-1)in regulating glucose homeostasis and elucidate the molecular mechanisms.Methods Male Glp1r-IRES-Cre,Glp1r-KO,and wild-type mice were used in this study.Fiber photometry was employed to record Ca2+signals of neurons in ventromedial hypothalamus(VMH)and patch-clamp was used to analyze electrophysiological properties of GLP-1 receptor-positive(GLP-1RVMH)neurons.Viral stereotaxic injections,chemogenetics,plasma hormone assays,and routine glucose metabolism assessments were combined to determine the regulatory role of GLP-1RVMH neurons in glucose homeostasis.Tissue and cell mitochondrial respiratory function assays,transmission electron microscopy,and conventional molecular biology methods were used to explore the mechanism by which GLP-1R agonists regulate glucose homeostasis.Results When the glucose concentration decreased from 5.0 mmol/L to 0.5 mmol/L,the action potential frequency of GLP-1RVMH neuron decreased significantly[(4.51±0.80)Hz vs.(1.43±0.51)Hz,P<0.01].Activation of GLP-1RVMH neuron significantly enhanced insulin secretion[(7.60±0.56)μU/mL vs.(11.34±0.93)μU/mL,P<0.01],while inhibition of these neuronal activities impaired the hypoglycemic efficiency of GLP-1 agonists[(32.03%±0.91%)vs.(25.77%±1.09%),P<0.001)].Mechanistically,GLP-1 regulated glucose homeostasis through Drp1 phosphorylation-mediated mitochondrial fission and improved mitochondrial energy metabolism.Conclusion GLP-1RVMH neurons are a class of glucose-excited neurons,and which activated directly promotes secretion of insulin.The hypoglycemic effect of GLP-1R agonists depend on the neuronal activity of GLP-1RVMH.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Objective To investigate the neural basis of glucagon-like peptide-1(GLP-1)in regulating glucose homeostasis and elucidate the molecular mechanisms.Methods Male Glp1r-IRES-Cre,Glp1r-KO,and wild-type mice were used in this study.Fiber photometry was employed to record Ca2+signals of neurons in ventromedial hypothalamus(VMH)and patch-clamp was used to analyze electrophysiological properties of GLP-1 receptor-positive(GLP-1RVMH)neurons.Viral stereotaxic injections,chemogenetics,plasma hormone assays,and routine glucose metabolism assessments were combined to determine the regulatory role of GLP-1RVMH neurons in glucose homeostasis.Tissue and cell mitochondrial respiratory function assays,transmission electron microscopy,and conventional molecular biology methods were used to explore the mechanism by which GLP-1R agonists regulate glucose homeostasis.Results When the glucose concentration decreased from 5.0 mmol/L to 0.5 mmol/L,the action potential frequency of GLP-1RVMH neuron decreased significantly[(4.51±0.80)Hz vs.(1.43±0.51)Hz,P<0.01].Activation of GLP-1RVMH neuron significantly enhanced insulin secretion[(7.60±0.56)μU/mL vs.(11.34±0.93)μU/mL,P<0.01],while inhibition of these neuronal activities impaired the hypoglycemic efficiency of GLP-1 agonists[(32.03%±0.91%)vs.(25.77%±1.09%),P<0.001)].Mechanistically,GLP-1 regulated glucose homeostasis through Drp1 phosphorylation-mediated mitochondrial fission and improved mitochondrial energy metabolism.Conclusion GLP-1RVMH neurons are a class of glucose-excited neurons,and which activated directly promotes secretion of insulin.The hypoglycemic effect of GLP-1R agonists depend on the neuronal activity of GLP-1RVMH.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Background::Pathological scars are a disorder that can lead to various cosmetic, psychological, and functional problems, and no effective assessment methods are currently available. Assessment and treatment of pathological scars are based on cutaneous manifestations. A two-photon microscope (TPM) with the potential for real-time non-invasive assessment may help determine the under-surface pathophysiological conditions in vivo. This study used a portable handheld TPM to image epidermal cells and dermal collagen structures in pathological scars and normal skin in vivo to evaluate the effectiveness of treatment in scar patients. Methods::Fifteen patients with pathological scars and three healthy controls were recruited. Imaging was performed using a portable handheld TPM. Five indexes were extracted from two dimensional (2D) and three dimensional (3D) perspectives, including collagen depth, dermo-epidermal junction (DEJ) contour ratio, thickness, orientation, and occupation (proportion of collagen fibers in the field of view) of collagen. Two depth-dependent indexes were computed through the 3D second harmonic generation image and three morphology-related indexes from the 2D images. We assessed index differences between scar and normal skin and changes before and after treatment.Results::Pathological scars and normal skin differed markedly regarding the epidermal morphological structure and the spectral characteristics of collagen fibers. Five indexes were employed to distinguish between normal skin and scar tissue. Statistically significant differences were found in average depth ( t = 9.917, P <0.001), thickness ( t = 4.037, P <0.001), occupation ( t= 2.169, P <0.050), orientation of collagen ( t = 3.669, P <0.001), and the DEJ contour ratio ( t = 5.105, P <0.001). Conclusions::Use of portable handheld TPM can distinguish collagen from skin tissues; thus, it is more suitable for scar imaging than reflectance confocal microscopy. Thus, a TPM may be an auxiliary tool for scar treatment selection and assessing treatment efficacy.

ObjectiveTo determine the serum antibody level and risk factors in the adolescent population in a county in Zhejiang Province， following the immunization with inactivated COVID-19 vaccine， and to construct a prediction model for antibody concentration. MethodsWe conducted the study in a county in Zhejiang Province， employing a stratified cluster random sampling strategy in school children who had received the inactivated COVID-19 vaccine. Data on gender， age， type of vaccine， and time of vaccination was collected. Serum samples were also collected to test for anti-S and N IgG antibody against the SARS-CoV-2 by using chemiluminescent immunoassay （CLIA）. Risk factors were determined to construct a prediction model for antibody concentration. ResultsThe IgG antibody concentration was significantly higher in girls， those who received two doses， and those who had simply received the KX vaccine . It decreased with age and time interval between the sampling and last vaccination. The prediction model constructed by random forest regression in the study had a better model fit and predictive ability than that by the multivariable linear stepwise regression. ConclusionGender， age， vaccination dose， type of vaccine， and time of vaccination are associated with vaccination effectiveness of inactivated COVID-19 vaccines in adolescents. Prediction model could predict the antibody level in the vaccinated population， which can provide a new tool for better evaluation of vaccination effectiveness against emerging infectious diseases in future.

ObjectiveAntibiotic resistance genes （ARGs） have received wide attention all over the world. The purpose of this study was to explore the bacterial community structure， the types and levels of antibiotic resistance genes in a water body in east China， and to compare and analyze the characteristics of microbial species distribution and antibiotic resistance gene distribution in various water environments. MethodsA total of 10 households in Haimen City， Jiangsu Province were selected and their surrounding water environment samples were collected. 21 water samples including river water （4）， Mingou water （9） and well water （8） were collected for metagenomics sequencing， assembled with MetaWRAP， annotated with CARD database， and analyzed with R software. ResultsIn various water bodies， the dominant bacteria phyla was Proteobacteria， the dominant bacteria genera were Deuterostomia， Pseudomonas， Flavobacteriales and Streptomycetaceae. The ARGs annotated were mainly composed of quinolones， aminoglycosides， macrolides and beta-lactams antibiotic resistance genes. The top four relative abundance of resistance genes were macB， RanA， evgS and TxR， The average absolute abundance and expression of resistance genes in well water and Mingou water were higher than those in river water. ConclusionMultiple ARGs are detected to varying degrees in well water， river water， and Mingou water bodies， and the expression of resistance genes in well water and Mingou water bodies is higher than that in river water bodies， possibly due to human production and living activities.

Objective:To explore the spatial and temporal distribution patterns of emerging snail-infested sites in different environmental types in Yunnan Province.Methods:The data of snail-infested sites in Yunnan Province from 1950 to 2014 (from Yunnan Institute for Endemic Diseases Control and Prevention), were collected and sorted out, a spatial and temporal database on the distribution of emerging snail-infested sites were established, and the changes in the spatial and temporal distribution of emerging snail-infested sites in different environments types (ditches, tangerines, paddy fields, dry land, beaches and other environments) were studied by using spatial autocorrelation analysis and scanning statistics analysis.Results:From 1950 to 2014, the annual number of emerging snail-infested sites in Yunnan Province reached a peak (1 730) in 1955 and then showed a fluctuating downward trend. From 1993 to 2014, the number of emerging snail-infested sites remained below 100, and increased to 160 and 131, respectively, in 2004 and 2013. The longest mean duration of 43.85 years was recorded for the beaches environment for emerging snail-infested sites, followed by the paddy fields environment with a mean duration of 37.01 years, and the shortest mean duration of 20.44 years for the tangerines environment. Spatial autocorrelation analysis showed that there was a positive spatial correlation between the duration of emerging snail-infested sites of different environmental types (global Moran's I ranged from 0.43 to 0.64, P < 0.05). Scanning statistics analysis showed that emerging snail-infested sites of different environmental types had spatial and temporal aggregation ( P < 0.001), with 3- 6 clusters of statistically significant aggregation detected respectively. Conclusion:The emerging snail-infested sites in different environments types in Yunnan Province have spatial and temporal aggregation, and it is necessary to strengthen monitoring and prevention and control of the aggregation areas of different environment types to prevent further spread of the snail.

ObjectiveTo obtain the status of latent tuberculosis infection （LTBI） among tuberculosis （TB）-related health-care workers （HCWs） in Shanghai， and to explore the risk factors related to TB infection. MethodsA multi-center cross-sectional study was conducted by recruiting medical workers from multiple designated TB hospitals， centers for disease control and prevention， and community health service centers in Shanghai. Each subject was required to complete a questionnaire and to provide a blood sample for TB infection test. Univariate and multivariate analysis ware made in order to find risk factors relating to TB infection. ResultsA total of 165 medical workers were recruited， and the proportion of TB infection was 16.36% （95%CI： 11.49%‒22.76%）. Multivariate logistic analysis showed that clinical doctors and nurses （adjusted OR=9.756， 95%CI： 1.790‒53.188）， laboratory staffs （adjusted OR=78.975， 95%CI： 8.749‒712.918）， and nursing and cleaning workers （adjusted OR=89.920， 95%CI： 3.111‒2 598.930） had higher risk of TB infection. ConclusionThe overall LTBI prevalence among TB-related HCWs is low. However， working as doctors， nurses， laboratory staffs， nursing workers and cleaning workers are risk factors of TB infection. TB-related HCWs who work at hospitals are at risk of TB infection comparing to medical staffs who work outside hospitals.

Objective:To investigate the relationship between serum glial cell line-derived neurotrophic factor (GDNF) levels and neuroimaging changes and cognitive impairment in patients with cerebral small vascular disease (CSVD).Methods:135 patients with CSVD recruited from the Department of Neurology of the First Affiliated Hospital of Xinxiang Medical University from September 2021 to July 2022 were assessed by cranial multimodal magnetic resonance imaging and Montreal cognitive function assessment (MoCA), and the basic data were analyzed at the same time.The serum GDNF concentration of all patients was detected by enzyme-linked immunosorbent assay (ELISA). According to the median GDNF concentration, the patients were divided into low GDNF group and high GDNF group. The baseline data, MoCA score and imaging markers of the two groups were compared by Mann-Whitney U test, chi-square test, logistic regression, Kruskal-Wallis H test and Jonckheere-Terpstra trend test, and the correlation between serum GDNF level and imaging markers and cognitive function of patients with CSVD was analyzed. Results:The median serum GDNF concentration of all CSVD patients was 16.66 pg/mL. Multivariate logistic regression analysis showed that low serum GDNF level was a risk factor for white matter hyperintensity and total image load in patients with CSVD. Serum GDNF level was a protective factor of cognitive impairment in patients with CSVD in multiple logistic regression analysis. The area under the curve of ROC curve analysis of cognitive impairment after CSVD predicted by serum GDNF level was 0.735, the sensitivity was 66.4%, and the specificity was 71.4%. The level of serum GDNF was positively related with visual space and executive function, attention and computational power, delayed recall and orientation( r=0.267, 0.187, 0.219, 0.215, all P<0.05). Conclusion:The serum GDNF level is related to white matter hyperintensities, total imaging load and cognitive impairment in patients with CSVD. Serum GDNF level may play a predictive role in CSVD and cognitive impairment.