Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective To explore the clinical features,treatment and prognosis of glioblastomas(GBM)in adults.Methods A retrospective cohort study was performed on 176 adult GBM patients admitted to our department from January 2015 to December 2021.Chi-square test was used to investigate the clinical differences between isocitrate dehydrogenase(IDH)mutant and wild-type GBM.Kaplan-Meier and Log-Rank tests were employed to plot survival curve and compute the survival analysis.Multivariate Cox regression model was applied to identify the independent prognostic factors.Results IDH wild-type GBM account for 89.2％and had significantly differences from the IDH-mutant GBM in terms of age of onset,Karnofsky(KPS)score at admission,symptoms of neurological deficit,and methylation status of O6-methylguanine-DNA-methyltransferase(MGMT)promoter(P＜0.05).For the IDH wild-type GBM patients receiving conventional therapy,univariate Cox hazard analysis showed gross total resection,methylation of MGMT promoter,initiation of radiation within the 5th to 6th week after surgery,and adjuvant temozolomide(TMZ)chemotherapy ≥6 cycles were favorable prognostic factors for overall survival(OS);GBMs in the left hemisphere,involvement of single lobe,methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery were favorable prognostic factors for progression free survival(PFS)(all P＜0.05).Moreover,multivariate Cox hazard regression analysis indicated that methylation of MGMT promoter,and initiation of radiation within the 5th to 6th week after surgery,and adjuvant TMZ chemotherapy ≥6 cycles were independent protective factors for OS,and GBMs in the left hemisphere,involvement of single lobe and methylation of MGMT promoter were independent protective factors for PFS in the GBM patients(all P＜0.05).Conclusion The clinical and prognostic features are totally different between IDH mutant and wild-type GBM,and molecular detections are needed for the further pathological classification.Methylation of MGMT promoter is a primary marker of favorite prognosis for IDH wild-type GBM,and slightly delay in radiotherapy(the 5th to 6th week after surgery)can effectively improve the survival prognosis of IDH wild-type GBM.