Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective A scoping review of studies on symptom clusters in patients with chronic heart failure(CHF)was conducted to provide reference for the treatment and management of CHF.Methods According to reporting framework of scoping review put forward by Arksey,the related literatures of Cochrane Library,CINAHL,Web of Science,PubMed,Embase,Wanfang Data Knowledge Service Platform,SinoMed,CNKI and VIP from January 2014 to July 2024 were searched,and the contents of the literatures were screened,extracted and analyzed.Results Ten articles were included,involving many symptom clusters,mainly including mood,ischemia,congestion,digestive tract and fatigue.Symptom group assessment tool mainly adopted Chinese version of Memorial symptom assessment scale-heart failure.Conclusion There are various types of symptom clusters in CHF patients,and they show dynamic changes in each disease stage.It is still necessary to strengthen the research on the evaluation tools,occurrence principles and standardized naming of symptom clusters.Medical staff can give first-class care to the main symptom clusters in each period,formulate personalized nursing intervention measures in advance,and improve the efficiency of symptom management in clinical nursing.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective A scoping review of studies on symptom clusters in patients with chronic heart failure(CHF)was conducted to provide reference for the treatment and management of CHF.Methods According to reporting framework of scoping review put forward by Arksey,the related literatures of Cochrane Library,CINAHL,Web of Science,PubMed,Embase,Wanfang Data Knowledge Service Platform,SinoMed,CNKI and VIP from January 2014 to July 2024 were searched,and the contents of the literatures were screened,extracted and analyzed.Results Ten articles were included,involving many symptom clusters,mainly including mood,ischemia,congestion,digestive tract and fatigue.Symptom group assessment tool mainly adopted Chinese version of Memorial symptom assessment scale-heart failure.Conclusion There are various types of symptom clusters in CHF patients,and they show dynamic changes in each disease stage.It is still necessary to strengthen the research on the evaluation tools,occurrence principles and standardized naming of symptom clusters.Medical staff can give first-class care to the main symptom clusters in each period,formulate personalized nursing intervention measures in advance,and improve the efficiency of symptom management in clinical nursing.

Objective To investigate the MRI characteristics of subventricular zone(SVZ)-associated isocitrate dehydrogenase(IDH)wild-type glioblastoma(GBM)and their correlations with Ki-67 expression and O6-methylguanine-DNA methyltransferase(MGMT)promoter methylation status.Methods A retrospective analysis was conducted on data of 78 patients with IDH wild-type GBM who underwent surgery and received pathological confirmation.Preoperative MRI contrast-enhanced T1 WI sequences were used to assess SVZ involvement,and postoperative molecular testing of tumor markers,including Ki-67 expression and MGMT methylation status,was utilized to categorize the patients accordingly.Results The SVZ involved(+)group(P＜0.001)and the MGMT(+)group(P=0.036)exhibited significantly larger tumor volumes.There were no significant differences between the groups in terms of gender,age,left/right hemispheric lateralization,or specific brain lobe distribution.There was no significant association between Ki-67 expression levels,MGMT methylation status,and SVZ involvement,respectively.Conclusion The SVZ(+)group and the MGMT(+)group demonstrates a wider range of tumor invasion.

Objective: To investigate the prognosis of allogeneic hematopoietic stem-cell transplantation (allo-HSCT) for patients with acute myeloid leukemia and MLL rearrangement. Methods: From September 2009 to May 2016, the clinical data of 47 patients with MLL-rearranged AML undergoing allo-HSCT in the First Affiliated Hospital of Soochow University were retrospectively analyzed. Results: Among 47 MLL-rearranged AML patients, 24 were male and 23 female. The median age was 30 (15-58) years old. There are 36 (76%) patients were FAB-types M4/M5. Two-year overall survival (OS), disease-free survival (DFS), relapse incidence and transplant-related mortality (TRM) were (64.4±8.4)%, (47.3±9.3)%, 41.0% and 17.9%, respectively. Of them, 45 patients were detected with 11q23 translocations, and 2 patients with normal karyotype were MLL partial tandem duplication. According to different chromosome karyotype, 47 patients were divided into three groups: 16 cases of t (6; 11), 15 cases of t (9; 11) and 16 cases of other types. Overall survival was compared between the three groups, there was no significant difference (χ²=1.509, P=0.472). On multivariate analysis, independent risk factor on OS was transplant age >45 years [HR=4.454(95%CI 1.314-15.099), P=0.016]. The multivariate analysis also confirmed the higher TRM in patients at non-CR state when transplanted [HR=10.370(95%CI 1.043-103.110), P=0.046]. Positive minimal residual disease (MRD) before transplantation was a negative prognostic factor on DFS [HR=4.236(95%CI 1.238-14.495), P=0.021] and relapse incidence (RI) [HR=5.491(95%CI 1.371-21.995), P=0.016]. Conclusion Transplant age (>45 years), allo-HSCT in non-CR state adn positive MRD before transplantation were negative prognostic factors in allo-HSCT for MLL-rearranged AML patients.