Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Objective:To investigate the active ingredients and targets of Compound Gastritis Mixture(CGM)in the treatment of chronic atrophic gastritis(CAG)by network pharmacology method,and to validate the potential mechanism combined with molecular docking technology and cellular experiments.Methods:The Traditional Chinese Medicine System Analysis Platform(TCMSP)and Swiss Target Prediction databases were used to select the herbal ingredients of CGM and the corresponding targets;the GeneCards and Online Mendelian Inheritance in Man(OMIM)database were used to screen the targets of CAG;the common targets of CGM and CAG were analyzed from the Venny2.1.0 platform;STRING online platform was used to construct protein-protein interaction(PPI)networks for common drug-disease targets and screen the core targets.Cytoscape 3.9.1 software was used to construct the drug-disease-target network and screen the drug core components;Gene Ontology(GO)fuctional,Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were used to analyze the common targets of CGM and CAG;and AutoDock analysis software was used to perform molecular docking analysis of predicted main components of the drugs and core targets.The gastric mucosal epithelial cells GES-1 were induced by lipopolysaccharide(LPS)to construct CAG cell model.The GES-1 cells were divided into blank group(10%serum complete medium),model group(10 mg·L-1 LPS),and different concentrations of CGM groups(50,100,200,400,800 and 1 600 g·L-1 CGM+10 mg·L-1 LPS),and cells were incubated for 12,24,and 48 h.The cell counting kit-8(CCK-8)assay was used to detect the proliferation activities of GES-1 cells.The GES-1 cells were divided into blank group(10%serum complete medium),model group(10 mg·L-1 LPS)and CGM group(1 600 g·L-1 CGM+10 mg·L-1 LPS).Real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the expression levels of interleukin(IL)-6,tumor necrosis factor(TNF),serine/threonine protein kinase 1(AKT1),IL-1β,and epidermal growth factor receptor(EGFR)mRNA in the cells in various groups.Results:A total of 198 ingredients of CGM were screened,and 128 common targets with CAG were identified.The main herbal ingredients of CGM in treatment of CAG were quercetin,kaempferol,and lluteolin,which mainly acted on the core targets of IL-6,TNF,AKT1,IL-1β,and EGFR.The GO function enrichment analysis results showed that the top 15 targets mainly focused on biological processes(BP)such as apoptosis,inflammatory response and cell proliferation,mainly included cellular components(CC)such as cytoplasm,cell surface and macromolecular complexes,and mainly exerted molecular functions(MF)such as proteins,enzymes and ubiquitin-protein ligases.A total of 158 pathways were obtained from KEGG signaling pathway enrichment analysis,mainly involved cancer-related pathways,TNF signaling pathways,viral infection,programmed cell death-ligand 1(PD-L1)/programmed cell death protein-1(PD-1)pathways,apoptosis,NOD-like receptor signaling pathways,Toll-like receptor signaling pathways,EGFR,and IL-17 signaling pathways.The binding energies of the core targets IL-6,TNF,IL-1β,AKT1,and EGFR with main herbal ingredients quercetin,kaempferol,and luteolin were<-5 kcal·mol-1.The CCK-8 assay results showed that compared with blank group,after 24 and 48 h of cell culture,the proliferation activities of the cells in model group were significantly decreased(P<0.01),and the inhibition of the proliferation activity was more obvious after 48 h;therefore,48 h was selected for the modeling time;compared with model group,the proliferation activities of cells in 800 and 1 600g·L-1 GCM groups were significantly decreased(P<0.01),and the promotion of cell proliferation activity was more obvious in 1 600g·L-1 GCM group,so the intervening concentration of this drug was selected for the subsequent experiments.The RT-qPCR method results showed that compared with blank group,the expression levels of IL-6,TNF,IL-1β,AKT1,and EGFR mRNA in the cells in model group were significantly increased(P<0.01);compared with model group,the expression levels of IL-6,IL-1β,AKT1 and EGFR mRNA in the cells in CGM group were significantly decreased(P<0.01).Conclusion:CGM may play a role in the prevention and treatment of CAG through multiple ingredients such as quercetin,kaempferol and lignocerol,acting on the multiple target proteins such as IL-6,TNF,AKT1,IL-1β,and EGFR,as well as involving a variety of"inflammatory-cancer-related"pathways.

ObjectiveTo develop and validate a predictive model to individually predict the risk of patients with stroke in the eICU Collaborative Research Database for early clinical identification and intervention. MethodIndividual patient data （200 859 cases） from a national multicenter cohort study （eICU database） were selected， and the patients with stroke in neurological diseases （9 037 cases） were selected for statistical analysis. The main outcome was hospital mortality. The Glasgow Coma scale （GCS） was used to divide all patients with stroke into stroke in meridian and stroke in viscera （GCS≤14 for stroke in viscera and GCS=15 for stroke in meridian）. The patients were then divided into a training set and a test set according to 7∶3， respectively， to evaluate the differences in hospital mortality between the two groups. The multivariate logistic regression was used to analyze the related factors affecting the prognosis of the two groups， and a predictive model was established. Receiver operator characteristic （ROC） curves were used to assess the discrimination of the predictive model. ResultThe predictive model based on 9 037 patients with stroke was established. The predictors of the stroke in meridian （4 475 cases） included pulmonary infection， mechanical ventilation， acute physiology， and chronic health status scoring system Ⅳ （APACHE Ⅳ） score. The predictors of the stroke in viscera （4 562 cases） included anticoagulation therapy （AT）， mechanical ventilation， acute physiology， and APACHE Ⅳ score. According to the predictors， the predictive models of the stroke in meridian and the stroke in viscera were constructed， respectively. The areas under the curve （AUC） of ROC of the training set and the test set of the predictive models of the stroke in meridian were 0.845 ［95% confidence interval （CI） （0.811， 0.879）］ and 0.807 ［95% CI （0.751， 0.863）］， respectively. The areas under the ROC curve of the training set and test set of the predictive models of the stroke in viscera were 0.799 ［95% CI （0.781， 0.817）］ and 0.805 ［95% CI （0.778， 0.832）］， respectively. The AUC of the predictive model of the training set and the test set were both above 0.7. ConclusionThe model established in this study can conveniently， directly， and accurately predict the hospital mortality risk of patients with stroke. Physicians and other healthcare professionals can use this predictive approach to provide early care planning and clinical interventions for patients with stroke during their hospital stay.