Objective: To explore the influencing factors of anxiety symptoms and to construct a predictive model based on machine learning algorithms, so as to provide support for the prevention and management of anxiety symptoms among junior high school students. Methods: From April to May 2023, a stratified random cluster sampling method was adopted to select 8 176 junior high school students from Zhengzhou and Shangqiu citys. All participants completed the Adolescent Self rating Life Events Checklist, the 10item Connor-Davidson Resilience Scale, the School Connectedness Scale, the Parent-Child Cohesion Questionnaire, and the 7 item Generalized Anxiety Disorder Scale. Logistic regression analysis identified the associated factors of anxiety symptoms among junior high school students. Predictive models were constructed using Logistic regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms, with SHapley Additive exPlanations analysis explaining the optimal model. Results: The detection rate of anxiety symptoms among junior high school students was 16.3%. Logistic regression analysis showed that junior high school students who were female ( OR =1.22), in the ninth grade ( OR =1.27), living in urban areas ( OR =1.37), having a father with a college education or above ( OR =1.26), having a mother with a senior high school education ( OR =1.26), and experiencing higher levels of negative life events ( OR =1.05) reported a higher risk of anxiety symptoms(all P <0.05). In contrast, those with moderate family economic status ( OR =0.71), moderate academic burden ( OR =0.59), low academic burden ( OR =0.54), moderate sleep quality ( OR =0.46), good sleep quality ( OR =0.26), excellent sleep quality ( OR =0.15), higher levels of psychological resilience ( OR =0.96), higher levels of school connectedness ( OR =0.96), and higher levels of parent-child cohesion ( OR =0.98) reported a lower risk of anxiety symptoms (all P <0.05). Three machine learning models demonstrated good predictive performance for anxiety symptoms among junior high school students (all AUC>0.8), with the XGBoost model achieving the best predictive performance. SHAP analysis revealed that negative life events, sleep quality, school connectedness, psychological resilience and parent-child cohesion were the top five relevant factors for predicting anxiety symptoms. Conclusions The detection rate of anxiety symptoms among junior high school students is relatively high. The XGBoost model is the optimal predictive model for anxiety symptoms in the population. Negative life events, sleep quality, school connectedness, psychological resilience, and parent-child cohesion are significant correlates of anxiety symptoms among junior high school students.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To investigate the impact of eccentric placement for various types of artificial aortic valves on downstream flow dynamics.Methods A physiological pulsatile circulation simulation system was employed and particle image velocimetry(PIV)was utilized to analyze the downstream flow field variations for bioprosthetic and mechanical valves under two placement conditions:centralized placement(0 mm)and eccentric placement(1 mm).Hemodynamic parameters such as velocity,vorticity,and viscous shear stress were assessed to evaluate the flow field characteristics.Results By analyzing the flow field variations at four characteristic time points,namely,early systole,acceleration phase,peak systole,and deceleration phase,a significant difference in flow field distribution between bioprosthetic and mechanical valves was observed.The bioprosthetic valve exhibited a centrally symmetric jet with a higher flow velocity,whereas the mechanical valve displayed a three-jet structure with a lower central flow velocity.Under eccentric placement,the blood flow in the aortic sinus region was sluggish,with a reduction in average velocity,hindering the formation and maintenance of vortices.During the peak systolic phase,the maximum viscous shear stresses in the sinus region for the bioprosthetic and mechanical valves were 0.45 and 0.67 Pa,respectively,approaching the threshold for endothelial cell damage.Conclusions Eccentric placement of both mechanical and bioprosthetic valves resulted in reduced sinus blood flow velocity and diminished viscous shear stress,creating favorable conditions for thrombus formation.In clinical practice,careful attention should be given to the placement of valve replacement to prevent eccentric placement.

BACKGROUND:Facet joint osteoarthritis is acknowledged as a significant contributor to lower back pain in the geriatric population.The advent of an innovative spinal facet joint fusion device presents a therapeutic option for intervening during the initial stages of facet joint osteoarthritis,and significantly reduces the incidence of a series of complications caused by poor early conservative treatment and late surgical treatment.However,its effect on the biomechanics of the lumbar spine is unknown.OBJECTIVE:To investigate the biomechanical disparities between the novel lumbar zygapophyseal joint fusion device and traditional fusion devices.METHODS:A comprehensive three-dimensional finite element model of the L3-S1 lumbar spine was established and validated.Based on this intact model,three groups of surgical models were constructed:a bilateral pedicle screw fixation model,a bilateral novel facet joint fusion fixation model,and a bilateral facet screw fixation model,with the surgical segment designated as L4-5.Under a load of 500 N,a torque of 7.5 Nm was applied to all lumbar models to calculate the range of motion,displacement values,and intervertebral disc stress values at the L4-5 segment;stress values at the L3-4 and L5-S1 segments were also measured.RESULTS AND CONCLUSION:(1)Compared with the intact model,the range of motion at the L4-5 segment was reduced in all surgical models.(2)The novel device exhibited the smallest range of motion at the L4-5 segment under left and right rotational conditions;the greatest range of motion at the L4-5 segment under extension conditions;and a greater range of motion under other conditions than the bilateral pedicle screw fixation model.(3)The novel device demonstrated the smallest displacement values at the L4-5 segment under left and right rotational conditions;under other conditions,the displacement values at the L4-5 segment were greater than those in the bilateral pedicle screw fixation model.(4)In terms of stress distribution at the L4-5 segment,the novel device consistently exhibited the smallest values across all conditions.(5)For the L3-4 segment,the novel device showed the greatest stress values under extension and left and right rotational conditions,while under other conditions,the values were lower than those in the bilateral pedicle screw fixation model.(6)Compared with pedicle screw fixation,the novel device produced smaller stress values at the L5-S1 segment.(7)This study indicates that,compared with pedicle screw fixation,the novel device impacts the biomechanics of the lumbar spine by fusing the facet joints.It provides stability while preserving the range of motion at the surgical segment and reduces stress on the intervertebral discs of the surgical and adjacent segments,thereby potentially delaying disc degeneration.This suggests that the novel device can achieve biomechanical effects similar to those of pedicle screw fixation in theory.

Objective:To explore patterns of traditional Chinese medicine (TCM) use among patients with inflammatory bowel disease (IBD) in Jiangsu Province, China from 2019 to 2023.Methods:Using data from the IBD health data platform of the National Healthcare Big Data (Eastern) Center, a retrospective cohort study was conducted. We performed descriptive analyses on hospitalised patients diagnosed with IBD between 2019 and 2023, who received TCM treatment.Results:The study included 11 095 case records from 4 760 patients, with TCM diagnoses primarily indicating diarrhoea and abdominal pain. Ulcerative colitis (UC) accounted for 4 782 hospitalizations (3 103 patients), while Crohn's disease (CD) accounted for 6 313 hospitalizations (1 657 patients). Patient demographics showed a trend towards younger age and a higher proportion of males. Treatment utilisation was highest in southern Jiangsu compared with the central and northern regions. In terms of disease burden, all treatment costs showed a downward trend. In terms of external TCM therapies, UC patients tend to prefer plasters and enemas, while CD patients are more inclined to use acupuncture. Regarding herbal medicine, licorice, white atractylodes, and white peony root are commonly used single herbs for IBD patients.Conclusions:The number of IBD patients treated with TCM in Jiangsu Province has steadily increased from 2019 to 2023. It is important to identify effective TCM treatment methods to reduce the burden of patients.

Objective To explore the value of CT-enhanced portal venous phase radiomics combined with machine learning algo-rithms in assessing microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was con-ducted on imaging and clinical data of 132 HCC patients.The patients were randomly divided into training set and test set at a 7︰3 ratio.Independent influencing factors for predicting MVI status in HCC patients were identified through univariate and multivariate logistic regression analyses.Radiomics features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm,and a Radiomics score(Radscore)was calculated to construct the final combined model.Four machine learning algorithms including-logistic regression(LR),naive Bayes(NB),support vector machine(SVM),and K-nearest neighbor(KNN)were applied to evaluate the model.The performance of each machine model was assessed using the receiver operating characteristic(ROC)curves and the area under the curve(AUC).Results Univariate and multivariate logistic regression analyses revealed that venous phase CT values were independentinfluencing factors,and six radiomics features were ultimately selected.After the Radscore was calculated,a combined model was constructed using Radscore and venous phase CT values.Machine learning algorithms showed that the combined model achieved the following AUC in the training set:0.895[95%confidence interval(CI)0.821-0.965]for LR,0.892(95%CI 0.831-0.963)for NB,0.644(95%CI 0.532-0.765)for SVM,and 0.855(95%CI 0.783-0.947)for KNN.In the test set,the respective AUC were 0.845(95%CI 0.712-0.961),0.840(95%CI 0.723-0.964),0.492(95%CI 0.311-0.687),and 0.716(95%CI 0.566-0.871).Conclusion Radiomics based on CT-enhanced portal venous phase combined with machine learning algorithms demonstrates high efficiency in preoperative evaluation of MVI in HCC,with the LR model showing the best performance.

Objective To investigate the impact of eccentric placement for various types of artificial aortic valves on downstream flow dynamics.Methods A physiological pulsatile circulation simulation system was employed and particle image velocimetry(PIV)was utilized to analyze the downstream flow field variations for bioprosthetic and mechanical valves under two placement conditions:centralized placement(0 mm)and eccentric placement(1 mm).Hemodynamic parameters such as velocity,vorticity,and viscous shear stress were assessed to evaluate the flow field characteristics.Results By analyzing the flow field variations at four characteristic time points,namely,early systole,acceleration phase,peak systole,and deceleration phase,a significant difference in flow field distribution between bioprosthetic and mechanical valves was observed.The bioprosthetic valve exhibited a centrally symmetric jet with a higher flow velocity,whereas the mechanical valve displayed a three-jet structure with a lower central flow velocity.Under eccentric placement,the blood flow in the aortic sinus region was sluggish,with a reduction in average velocity,hindering the formation and maintenance of vortices.During the peak systolic phase,the maximum viscous shear stresses in the sinus region for the bioprosthetic and mechanical valves were 0.45 and 0.67 Pa,respectively,approaching the threshold for endothelial cell damage.Conclusions Eccentric placement of both mechanical and bioprosthetic valves resulted in reduced sinus blood flow velocity and diminished viscous shear stress,creating favorable conditions for thrombus formation.In clinical practice,careful attention should be given to the placement of valve replacement to prevent eccentric placement.

Objective To explore the functional connectivity(FC)changes of hippocampus and amygdala in type 2 diabetes mellitus(T2DM)patients with erectile dysfunction(DMED),and the central pathological neural mechanisms underlying DMED.Methods 61 T2DM patients who visited Department of Endocrinology,Nanjing First Hospital,Nanjing Medical University from January 2020 to December 2021 were selected and divided into a simple T2DM group(n=30)and a combined DMED group(n=31).Another 47 healthy individuals were selected as control group(NC).The international erectile function scale(IIEF-5)was used to evaluate the erectile function.Resting-state functional magnetic resonance imaging(rs-fMRI)data were acquired from all participants.MRI data were preprocessed by the DPABI software package.Bilateral hippocampus and amygdala were selected as regions of interest(ROI)and the whole brain FC values were calculated.The FC values of brain regions between groups were tested by two-sample t-test with REST software package.Results Left hippocampus as ROI:compared with the NC group,FC values of the left superior temporal gyrus increased in the T2DM group,FC values of the left superior frontal gyrus,left inferior temporal gyrus,left posterior central gyrus and rectus gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left inferior parietal gyrus,left supramarginal gyrus,left middle occipital gyrus and right posterior central gyrus decreased in the DMED group.Right hippocampus as ROI:compared with the NC group,FC values of the right middle temporal gyrus and right rolandic operculum increased while FC values of the right calcarine fissure decreased in the T2DM group;FC values of bilateral anterior cingulate gyrus,right middle temporal gyrus and left rectus gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus,left inferior parietal gyrus and right inferior temporal gyrus decreased in the DMED group.Left amygdala as ROI:compared with the NC group,FC values in the left parahippocampal gyrus,left fusiform gyrus and right insula increased in the T2DM group;FC value of the left middle temporal gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus and left supramarginal gyrus decreased in the DMED group.Right amygdala as ROI:compared with the NC group,FC values of the left insula,right parahippocampal gyrus,right superior temporal gyrus and right supramarginal gyrus increased while FC values in the right caudate decreased in the T2DM group;FC values of the right middle frontal gyrus,left rectus gyrus and left middle occipital gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus and left inferior parietal gyrus decreased in the DMED group.Conclusions DMED patients have abnormalities in the hippocampus,amygdala and other brain regions,especially the frontal lobe functional cortex,which may be related to changes in their brain function.

Objective To explore the value of CT-enhanced portal venous phase radiomics combined with machine learning algo-rithms in assessing microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was con-ducted on imaging and clinical data of 132 HCC patients.The patients were randomly divided into training set and test set at a 7︰3 ratio.Independent influencing factors for predicting MVI status in HCC patients were identified through univariate and multivariate logistic regression analyses.Radiomics features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm,and a Radiomics score(Radscore)was calculated to construct the final combined model.Four machine learning algorithms including-logistic regression(LR),naive Bayes(NB),support vector machine(SVM),and K-nearest neighbor(KNN)were applied to evaluate the model.The performance of each machine model was assessed using the receiver operating characteristic(ROC)curves and the area under the curve(AUC).Results Univariate and multivariate logistic regression analyses revealed that venous phase CT values were independentinfluencing factors,and six radiomics features were ultimately selected.After the Radscore was calculated,a combined model was constructed using Radscore and venous phase CT values.Machine learning algorithms showed that the combined model achieved the following AUC in the training set:0.895[95%confidence interval(CI)0.821-0.965]for LR,0.892(95%CI 0.831-0.963)for NB,0.644(95%CI 0.532-0.765)for SVM,and 0.855(95%CI 0.783-0.947)for KNN.In the test set,the respective AUC were 0.845(95%CI 0.712-0.961),0.840(95%CI 0.723-0.964),0.492(95%CI 0.311-0.687),and 0.716(95%CI 0.566-0.871).Conclusion Radiomics based on CT-enhanced portal venous phase combined with machine learning algorithms demonstrates high efficiency in preoperative evaluation of MVI in HCC,with the LR model showing the best performance.

AIM:To analyze differential proteins associated with the pathogenesis of dry eye(DE)using bioinformatics methods, in order to reveal their potential molecular mechanisms.METHODS: Articles published in PubMed and EMBASE databases from the inception of the database to August 31, 2023, that used proteomic methods to detect protein expression in clinical samples of dry eye were searched. Differential proteins were selected and further analyzed using the STRING database and Cytoscape software for hub gene screening and module analysis. Protein-protein interaction(PPI)analysis, gene ontology(GO)functional annotation, and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analysis were performed.RESULTS: A total of 21 articles were included, identifying 74 differentially expressed proteins. The most frequently occurring differential proteins were calgranulin A(SA1008), lipocalin-1(LCN1), lysozyme C(LYZ), mammaglobin-B(SCGB2A1), proline-rich protein 4(PRR4), transferrin(TF), and calgranulinB(S100A9). The top 10 hub genes were serum albumin(ALB), tumor necrosis factor(TNF), interleukin 6(IL6), IL1B, IL8, matrix metalloproteinase 9(MMP9), alpha-1-antitrypsin(SERPINA1), IL10, complement component 3(C3), and lactotransferrin(LTF). Module analysis suggested MMP9 and PRR4 as seed genes. KEGG analysis showed that differential proteins were mainly enriched in the IL17 signaling pathway(61.9%).CONCLUSION: The results reveal potential molecular targets and pathways for DE and confirm the association between the pathogenesis of DE and inflammation. Further in-depth research is needed to confirm the significance of these biomarkers in clinical practice.