Objective To investigate the regulatory mechanism of the central nervous system in patients with refractory overactive bladder (rOAB) using diffusion tensor imaging (DTI) and graph theory analysis. Methods A total of 43 rOAB patients (rOAB group) and 46 matched healthy controls (HC group) were recruited during May and Nov.2024. All participants were scanned with DTI, and surveyed with the overactive bladder symptom score (OABSS), and overactive bladder questionnaire (OAB-q). Their age, gender, height, weight, and educational years were collected.DTI plus graph theory analysis was employed to explore the alterations in global and local topological properties of the brain structural network in rOAB patients. Brain regions showing significant group differences in structural metrics [specifically, the right paracentral lobule (PCL.R) ]were further used as seed points for functional connectivity (FC) analysis. Correlations between the nodal clustering coefficient (NCp) of the identified region, FC strength, OABSS, and OAB-q score were investigated. Results The OABSS [8 (6,10) vs.0 (0,1) ]and OAB-q [71 (53,80) vs.20 (19,24) ]were higher in the rOAB group than the HC group (P<0.001). Graph theory analysis revealed no statistically significant differences in global network metrics between the two groups (P>0.05). However, the NCp was significantly higher in the PCL.R of rOAB group compared to HC group (P<0.05, FDR-corrected).FC analysis using the PCL.R as a seed region demonstrated significantly reduced FC value in the left cerebellar crus Ⅱ (Cerebelum_Crus2_L) of the rOAB group (P<0.05, FDR-corrected). Partial correlation analysis showed that the NCp of PCL.R was positively correlated with both OABSS (r=0.255, P=0.018) and OAB-q score (r=0.257, P=0.017). Conversely, the FC of Cerebelum_Crus2_L was significantly negatively correlated with OABSS (r=-0.545, P<0.001) and OAB-q score (r=-0.535, P<0.001). Conclusion Patients with rOAB exhibit distinct brain structural network alterations compared to healthy individuals, primarily manifestation in the NCp value of PCL.R increased, and the FC intensity of Cerebelum_Crus2_L is significantly weakened. These alterations in the topological properties of the structural network may be implicated in the pathogenesis of rOAB.

Objective:To analyze the epidemiological characteristics of wasting, spinal curvature abnormalities and multimorbidity among children and adolescents aged 6-18 in Inner Mongolia and explore the related factors of these two health problems.Methods:In September 2022, a stratified random cluster sampling method was employed to select 188 635 children and adolescents aged 6-18 in Inner Mongolia for physical examinations and questionnaire surveys. Data on height, weight, as well as dietary behavior, physical activity, classroom environment, academic tasks, writing posture, and screen behavior were collected. The epidemiological characteristics of wasting, spinal curvature abnormalities and multimorbidity were analyzed. Additionally, a multivariate logistic regression model was used to analyze the factors associated with wasting, spinal curvature abnormalities and multimorbidity.Results:A total of 188 635 children and adolescents aged 6-18 years participated in this study, including 95 393 boys (50.6%) with an average age of (11.53±3.32) years. The detection rate of wasting was 3.79%, with a higher detection rate in boys (4.18%) than in girls (3.38%) ( P<0.001). The detection rate of spinal curvature abnormalities was 3.64%, with a higher detection rate in girls (4.04%) than in boys (3.25%) ( P<0.001). The detection rate of multimorbidity between wasting and spinal curvature abnormalities was 0.17%, and there was no statistically significant difference between genders ( P>0.05). The detection rates of wasting, spinal curvature abnormalities, and multimorbidity all increased with age ( P t<0.001). The multivariate logistic regression analysis showed that, after adjusting for gender, age, urban/rural status, and school grade, compared to children and adolescents who exercised ≥1 hour of moderate-to-vigorous physical activity (MVPA) for at least 5 days per week and had daily screen time <2 hours, those who exercised <5 days per week ( OR=1.28, 95% CI: 1.19-1.37) and had daily screen time ≥2 hours ( OR=1.11, 95% CI: 1.03-1.19) had a higher risk of wasting. Compared to children and adolescents who had ≥5 physical education (PE) classes per week, adjusted desk and chair height,<1 hour of after-school study/writing time, and whose parents or teachers rarely or never reminded them about posture, those with <5 PE classes per week ( OR=1.11, 95% CI: 1.02-1.21), unadjusted desk and chair height ( OR=1.08, 95% CI: 1.01-1.15),≥1 hour of after-school study/writing time ( OR=1.15, 95% CI: 1.07-1.24), frequent reminders from parents ( OR=1.16, 95% CI: 1.09-1.23), and frequent reminders from teachers ( OR=1.10, 95% CI: 1.04-1.16) had a higher risk of spinal curvature abnormalities. Compared to children and adolescents who did not consume sugary drinks daily, exercised ≥1 hour of MVPA for at least 5 days per week, and whose teachers rarely or never reminded them about posture, those who consumed sugary drinks daily ( OR=1.61, 95% CI: 1.00-2.46), exercised <5 days per week ( OR=1.33, 95% CI: 1.01-1.79), and had teachers who frequently reminded them about posture ( OR=1.35, 95% CI: 1.05-1.75) had a higher risk of multimorbidity between wasting and spinal curvature abnormalities. Conclusion:The detection rates of wasting, spinal curvature abnormalities and multimorbidity among children and adolescents aged 6-18 in Inner Mongolia are generally low, with an increasing trend observed with age. Both lifestyle and school environmental factors are associated with wasting, spinal curvature abnormalities and multimorbidity.

Objective: To analyze the association between altitudes and nutritional status of children and adolescents, and to explore the moderating effects of dietary behaviors and physical activity, so as to provide a scientific basis for developing lifestyle interventions tailored to local conditions. Methods: From September to November 2023, physical examinations and questionnaire surveys were conducted among children and adolescents aged 7-17 in two autonomous regions, Inner Mongolia and Xizang, with a final sample of 156 511 participants by the stratified cluster random sampling method. Height and weight were measured to calculate body mass index (BMI). Sociodemographic characteristics, dietary behaviors, and physical activity were collected via questionnaires, while the altitude of each participant s school was obtained using Amap. Logistic regression was performed to examine the relationship between altitudes and nutritional status. Interaction terms and stratified analyses were applied to assess the moderating effects of dietary behaviors and physical activity. Restricted cubic spline (RCS) were used for visualization. Results: In 2023, the prevalence of wasting and overweight/obesity among children and adolescents in Xizang were 9.7% and 9.0%, respectively, compared to 2.9% and 22.0% in Inner Mongolia. Logistic regression analysis results showed that for every 1 km increase in altitude, the risk of wasting increased, while the risk of overweight/obesity decreased ( OR =1.43, 0.19, both P <0.05). The results of the stratified analysis showed that compared to those living at altitudes <1 km, children and adolescents with healthy diets showed no significant association between altitudes (1-<2 and 2-<3 km) and wasting ( OR =1.22, 0.75, both P >0.05), whereas significant associations were observed at 3-<4 and ≥4 km altitudes ( OR =2.25, 2.89, both P <0.05). In contrast, unhealthy dietary groups showed statistically significant associations across altitudes ( OR =1.18-4.04, all P <0.05), consistent with RCS results. No moderating effects were observed for physical activity on the altitude wasting association or for dietary behaviors and physical activity combined on the altitude overweight/obesity association ( P interaction =0.63, 0.10, 0.53). Conclusion Healthy dietary behaviors play a critical role in improving the nutritional status of children and adolescents and reducing regional disparities, providing a scientific foundation for public health policy formulation and implementation.

Objective To construct an enhancer-based prognostic risk prediction model for non-small cell lung cancer (NSCLC) by integrating DNA methylome data and transcriptome data. Methods The weighted gene co-expression network analysis (WGCNA) was used to identify NSCLC related genes from the differentially methylated positions (DMPs) of enhancers. Based on the transcriptome data,the prognostic risk prediction model was constructed using LASSO-Cox regression algorithm. Results Through the analysis on DNA methylome data of NSCLC,19784 DMPs were obtained and their distribution patterns were characterized,including 6089 DMPs of enhancers. WGCNA analysis screened 79 highly correlated DMPs of enhancer with NSCLC from the 6089 DMPs. After analyzing the target genes of 79 DMPs with LASSO-Cox regression based on the transcriptome data,10 genes were used to construct a prognostic risk prediction model. The prognostic risk prediction model was evaluated by calculating the areas under the curve (AUC) of 3-,5-,and 10-year time-dependent receiver operating characteristic (ROC) curves in training set and validation set;and the results showed that the 3-,5-,and 10-year AUC in training set and validation set were all higher than 0.7. Finally,a nomogram was constructed to predict the 3-,5-,and 10-year survival probabilities of NSCLC. Conclusion This study provides new insights into the role of enhancers in NSCLC and has the potential to improve the prognosis by guiding personalized treatment decisions.

Objective To construct an enhancer-based prognostic risk prediction model for non-small cell lung cancer (NSCLC) by integrating DNA methylome data and transcriptome data. Methods The weighted gene co-expression network analysis (WGCNA) was used to identify NSCLC related genes from the differentially methylated positions (DMPs) of enhancers. Based on the transcriptome data,the prognostic risk prediction model was constructed using LASSO-Cox regression algorithm. Results Through the analysis on DNA methylome data of NSCLC,19784 DMPs were obtained and their distribution patterns were characterized,including 6089 DMPs of enhancers. WGCNA analysis screened 79 highly correlated DMPs of enhancer with NSCLC from the 6089 DMPs. After analyzing the target genes of 79 DMPs with LASSO-Cox regression based on the transcriptome data,10 genes were used to construct a prognostic risk prediction model. The prognostic risk prediction model was evaluated by calculating the areas under the curve (AUC) of 3-,5-,and 10-year time-dependent receiver operating characteristic (ROC) curves in training set and validation set;and the results showed that the 3-,5-,and 10-year AUC in training set and validation set were all higher than 0.7. Finally,a nomogram was constructed to predict the 3-,5-,and 10-year survival probabilities of NSCLC. Conclusion This study provides new insights into the role of enhancers in NSCLC and has the potential to improve the prognosis by guiding personalized treatment decisions.

Objective:To analyze the epidemiological characteristics of wasting, spinal curvature abnormalities and multimorbidity among children and adolescents aged 6-18 in Inner Mongolia and explore the related factors of these two health problems.Methods:In September 2022, a stratified random cluster sampling method was employed to select 188 635 children and adolescents aged 6-18 in Inner Mongolia for physical examinations and questionnaire surveys. Data on height, weight, as well as dietary behavior, physical activity, classroom environment, academic tasks, writing posture, and screen behavior were collected. The epidemiological characteristics of wasting, spinal curvature abnormalities and multimorbidity were analyzed. Additionally, a multivariate logistic regression model was used to analyze the factors associated with wasting, spinal curvature abnormalities and multimorbidity.Results:A total of 188 635 children and adolescents aged 6-18 years participated in this study, including 95 393 boys (50.6%) with an average age of (11.53±3.32) years. The detection rate of wasting was 3.79%, with a higher detection rate in boys (4.18%) than in girls (3.38%) ( P<0.001). The detection rate of spinal curvature abnormalities was 3.64%, with a higher detection rate in girls (4.04%) than in boys (3.25%) ( P<0.001). The detection rate of multimorbidity between wasting and spinal curvature abnormalities was 0.17%, and there was no statistically significant difference between genders ( P>0.05). The detection rates of wasting, spinal curvature abnormalities, and multimorbidity all increased with age ( P t<0.001). The multivariate logistic regression analysis showed that, after adjusting for gender, age, urban/rural status, and school grade, compared to children and adolescents who exercised ≥1 hour of moderate-to-vigorous physical activity (MVPA) for at least 5 days per week and had daily screen time <2 hours, those who exercised <5 days per week ( OR=1.28, 95% CI: 1.19-1.37) and had daily screen time ≥2 hours ( OR=1.11, 95% CI: 1.03-1.19) had a higher risk of wasting. Compared to children and adolescents who had ≥5 physical education (PE) classes per week, adjusted desk and chair height,<1 hour of after-school study/writing time, and whose parents or teachers rarely or never reminded them about posture, those with <5 PE classes per week ( OR=1.11, 95% CI: 1.02-1.21), unadjusted desk and chair height ( OR=1.08, 95% CI: 1.01-1.15),≥1 hour of after-school study/writing time ( OR=1.15, 95% CI: 1.07-1.24), frequent reminders from parents ( OR=1.16, 95% CI: 1.09-1.23), and frequent reminders from teachers ( OR=1.10, 95% CI: 1.04-1.16) had a higher risk of spinal curvature abnormalities. Compared to children and adolescents who did not consume sugary drinks daily, exercised ≥1 hour of MVPA for at least 5 days per week, and whose teachers rarely or never reminded them about posture, those who consumed sugary drinks daily ( OR=1.61, 95% CI: 1.00-2.46), exercised <5 days per week ( OR=1.33, 95% CI: 1.01-1.79), and had teachers who frequently reminded them about posture ( OR=1.35, 95% CI: 1.05-1.75) had a higher risk of multimorbidity between wasting and spinal curvature abnormalities. Conclusion:The detection rates of wasting, spinal curvature abnormalities and multimorbidity among children and adolescents aged 6-18 in Inner Mongolia are generally low, with an increasing trend observed with age. Both lifestyle and school environmental factors are associated with wasting, spinal curvature abnormalities and multimorbidity.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To summarize the best evidence for perioperative exercise in renal transplant patients and provide evidence-based evidence for clinical staff to perform exercise in renal transplant patients.Methods Evidence-based questions were identified according to the PIPOST model,and computerized searches were conducted on BMJ Best Practice,UpToDate,the World Health Organization,the US National Guidelines,the website of the Chinese Society of Nephrology,the website of the British Renal Association,the website of the American Society of Nephrology,CNKI,the knowledge service platform of Wanfang Data,the Wipo database and other databases for all evidence on perioperative exercise in renal transplant recipients,including clinical guidelines,clinical decision-making,systematic evaluations,evidence summaries,and expert consensus,with a timeframe for searching from the establishment of the database to August 2023.Quality assessment and evidence extraction of the literature were performed independently by 2 nursing graduate students.Results A total of 13 publications were included,including 1 clinical decision,1 guideline,3 expert consensus,and 8 systematic evaluations.A total of 24 pieces of best evidence were summarized in 7 areas:exercise benefits,exercise assessment,preoperative exercise strategies,postoperative exercise strategies,precautions,exercise instruction monitoring,and exercise support.Conclusions This study summarizes the best evidence about perioperative exercise exercise in renal transplantation and provides a reference for standardizing perioperative exercise exercise in renal transplantation.Clinical staff should fully identify the impediments and facilitators of exercise exercise in renal transplant patients,and develop personalized exercise exercise programs based on medical scenarios,patient conditions,and combined with patients'exercise wishes.

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.