Objectives:To explore the risk factors for death from ruptured acute aortic dissection during emergency treatment,construct and validate a risk prediction model for death from ruptured acute aortic dissection during emergency treatment. Methods:A total of 301 cases of acute aortic dissection patients who were admitted to Chinese Academy of Medical Sciences Fuwai Hospital from January 2018 to August 2021 were included in this study.Patients were divided into survival subgroup(n=239)and death subgroup(n=62)according to whether dissection rupture occurred in the acute stage of the disease.Univariate and multivariate analyses were performed.Logistic regression analysis was used to establish the risk prediction model.The Hosmer-Lemeshow test was conducted to assess the model's goodness of fit,and the receiver operating characteristic curve(ROC curve)was used to evaluate the model's predictive performance.A prospective validation was performed on 129 cases of acute aortic dissection patients admitted to our hospital's emergency department from September 2021 to September 2022. Results:Among the 301 cases of acute aortic dissection patients,there were 62 cases of rupture and death,with an incidence rate of 20.6%.The results of multivariate analysis showed that age(OR=1.066,95%CI:1.034-1.099),type A dissection(OR=0.045,95%CI:0.006-0.364),history of hypertension(OR=0.377,95%CI:0.167-0.850),and concomitant hypotension(OR=4.424,95%CI:1.467-13.340)were determinants of deaths.The model formula was Z=-5.624+0.064×age-0.976×history of hypertension(yes=1,no=0)-3.104×type(Type A=0,Type B=1)+1.487×concomitant hypotension(yes=1,no=0).The Hosmer-Lemeshow test result showed χ2=9.328,df=8,P=0.315,the area under the ROC curve was 0.874,sensitivity was 79.0%,specificity was 81.6%,and the maximum Youden index was 0.606.The model validation result showed that the area under the ROC curve was 0.722,sensitivity was 73.7%,specificity was 69.1%,and accuracy was 89.9%. Conclusions:Age,history of hypertension,dissection type,and combined hypotension are predictors of the risk prediction model for death from dissection rupture in patients with acute aortic dissection during emergency treatment.The model constructed in this study has good predictive performance,which can provide reference for medical staffto quickly identify high-risk patients for death from ruptured acute aortic dissection and timely predictive measures could be highlighted in indicated cases.

Objective:To develop an imaging-assisted diagnostic tool for schizophrenia based on multimodal magnetic resonance imaging and artificial intelligence techniques.Methods:Three independent datasets were utilized. For each subject, four brain structural metrics including grey matter volume (GMV), white matter volume (WMV), cortical thickness (CT) and deformation-based morphometry (DBM) indicators were extracted from the structural magnetic resonance imaging (sMRI) data, and three brain functional metrics including amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were extracted from the functional magnetic resonance imaging (fMRI) data. To distinguish patients with schizophrenia and healthy controls, single-metric classification models and multi-metrics-fusion classification models were trained and tested using a within-dataset and a between-dataset cross-validation strategy.Results:The results of within-dataset cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 86.18% (FC), while the multi-metric-fusion classifications could reach an accuracy of 90.21%. The results of between-datasets cross-validation showed that the highest accuracy of the single-metric classifications for schizophrenia diagnosis was 69.02% (ReHo), while the multi-metric-fusion classifications could reach an accuracy of 71.25%.Conclusion:The functional metrics generally outperforms the structural metrics for the classification between patients with schizophrenia and heathy controls. Additionally, fusion of multi-modal brain imaging metrics can improve the classification performance. Specifically, the fusion of CT, DBM, WMV, FC and ReHo demonstrates the highest classification accuracy, which is a potential tool for imaging-assisted diagnosis of schizophrenia.

Objective To explore the effect of century cloud weight loss(CCWL)follow-up system on the dietary behavior compliance,follow-up rate and weight loss effect of patients after bariatric surgery.Methods A total of 222 obese patients undergoing Beijing Shijitan Hospital Affiliated to Capital Medical University in 2022 were selected as the study subjects.A total of 108 patients from January to June 2022 were included in control group,and routine follow-up management mode was adopted.A total of 114 patients from July to December 2022 were included in intervention group and were followed up by CCWL follow-up system.The dietary behavior compliance,postoperative follow-up rate,percentage of excess weight loss(%EWL),glucose and lipid metabolism indexes were compared between two groups.Results At 1 month,3 months and 6 months after surgery,the eating behavior after bariatric surgery(EBBS)score,follow-up rate and%EWL of patients in intervention group were significantly higher than those in control group(P<0.05).At 1 month after surgery,there was no significant difference in triacylglycerol(TG),high-density lipoprotein cholesterol(HDL-C),fasting blood glucose(FBG)levels in two groups(P>0.05),and 2-hour postprandial blood glucose(2hBG)in intervention group was significantly lower than that in control group(P<0.05).At 3 months and 6 months after surgery,TG,FBG and 2hBG in intervention group were significantly lower than those in control group,while HDL-C was significantly higher than that in control group(P<0.05).Conclusion The application of CCWL follow-up system in patients with bariatric surgery can improve postoperative eating behavior,increase postoperative follow-up rate,and strengthen the effect of postoperative weight loss.

Objectives:To investigate the predictive effect of regional homogeneity (ReHo) from resting-state functional magnetic resonance imaging (rs-fMRI) in acute phase on memory function of chronic phage in ischemic stroke patients and the effects of residual learning (REL) on the predictive performance of machine learning models.Methods:From June 2019 to June 2021, rs-fMRI data of one-week after stroke (acute phase) were collected from 35 first-time ischemic stroke patients, and their memory scores were assessed by the Rey auditory verbal learning test (RAVLT) at 6 months after stroke (chronic phase). Using ReHo from rs-fMRI data in acute phase of ischemic stroke patients, the support vector regression (SVR) and the REL-based SVR (REL-SVR) were constructed to predict the patients’ memory scores at 6 months after stroke, and the performance of the two models was compared using Pearson correlation coefficient.Results:Based on the ReHo from acute phase, the correlation coefficient between the predicted values and the true scores from the SVR model was r=0.524, P=0.001, while the correlation coefficient obtained by the REL-SVR model was r=0.671, P<0.001. Brain regions with relatively higher weights such as Temporal_Pole_Mid_R (weight value: 1.03), Temporal_Mid_R(weight value: 1.03), Temporal_Inf_R (weight value: 1.03), Occipital_Mid_R (weight value: 0.57), Frontal_Mid_L (weight value: 0.32), Frontal_Sup_Medial_L (weight value: 0.53), SupraMarginal_L (weight value: 1.54), Calcarine_L (weight value: 0.65), Lingual_L (weight value: 0.58), Cuneus_L (weight value: 0.65), Precuneus_L (weight value: 0.83), cerebellum(weight value>1.0) made larger contributions to the prediction model. Conclusions:ReHo in the acute-phase can effectively predict memory in the chronic phase of ischemic stroke patients. Furthermore, REL can improve the performance of the traditional SVR model and achieve higher predictive accuracy.

Objective:To construct a Bayesian network risk prediction model for delirium during recovery from general anesthesia. To explore the network relationship between awakening delirium of general anesthesia and its related factors, and to reflect the influence intensity of each factor on awakening delirium of general anesthesia through network reasoning.Methods:This is a cross-sectional study. From February to May 2022, the Chinese version of the four rapid delirium diagnosis protocols for general anesthesia patients admitted to the department of Anesthesia, the First Hospital of Shanxi Medical University were adopted as research subjects through convenience sampling method to carry out the delirium screening program during awakening, and general information and blood sample laboratory test results of the subjects were collected. The single factor analysis was used to screen the correlative factors of awakening delirium and a Bayesian network model based on the maximum minimum climb method (MMHC) was constructed.Results:A total of 480 patients were included in the study, and the delirium rate during the recovery period of general anesthesia was 12.9%(62/480). The Bayesian network of awakening delirium consisted of 11 nodes and 18 directed edges. The Bayesian network showed that age, sodium, cerebral infarction and hypoproteinemia were the direct factors related to awakening delirium, while ASA grade, hematocele and hemoglobin were the indirect factors related to awakening delirium. The area under its ROC curve was 0.80(0.78-0.83).Conclusions:Bayesian networks can well reveal the complex network connections between awakening delirium and its related factors, and then prevent and control awakening delirium accordingly.

Objective:To explore the predictive value of single high-sensitivity cardiac troponin I (hs-cTnI) concentration of 30-day cardiovascular adverse events in patients with suspected acute coronary syndrome (ACS).Methods:This is a multicenter, prospective and observational clinical study. Patients with suspected ACS who were admitted into the emergency department of Fuwai Hospital, the First Affiliated Hospital of Sun Yat-sen University and Nanjing First Hospital from January 2017 to September 2020 were enrolled. hs-cTnI result at the time of visit was obtained from patients with suspected ACS. Patients were followed up for 30 days and patients were divided into no events group and events group according to the presence or absence of 30-day cardiovascular adverse events (acute myocardial infarction (including index), unplanned revascularization and cardiovascular death). The predictive value of single Hs-cTnI at different concentration thresholds on the adverse event was evaluated in terms of sensitivity, negative predictive value (NPV) and 95% confidence interval ( CI). The best threshold was defined as: missed diagnosis rate <2% and NPV >99%. Patients were sub-grouped according to the confounders of hs-cTnI (sex, age, chest pain duration, estimated glomerular filtration rate), and Chi-square test was used to compare sensitivity and NPV among various subgroups. Results:A total of 1 461 patients were included. Among them, 387 patients (26.5%) had 30-day adverse cardiovascular events and 1 074 patients (73.5%) had no adverse cardiovascular events. Mean age was (62±12) years old and 905 were males (61.9%). When the concentration of hs-cTnI was less than 2 ng/L (limit of detection), the missed diagnosis rate of 30-day cardiovascular adverse events was 0.8% (3/387), the sensitivity was 99.2% (95% CI 97.6%-99.8%), and NPV was 98.7% (95% CI 96.0%-99.7%). When hs-cTnI concentration was less than 6 ng/L, the missed diagnosis rate was 1.8%, the sensitivity was 98.2% (95% CI 96.1%-99.2%), and NPV was 99.0% (95% CI 97.9%-99.6%). Subgroup analysis showed that the sensitivity and NPV of single hs-cTnI concentration <6 ng/L for 30-day cardiovascular adverse events were lower in patients with chest pain less than 3 h than those with chest pain time>3 hours ( P<0.05). Conclusions:Single hs-cTnI concentration less than 6 ng/L can predict the risk of 30-day cardiovascular adverse events in suspected ACS patients, but continuous monitoring is recommended for patients with chest pain onset≤3 hours.

Objective:To understand the research ability, cognition, and training needs of clinical medical staff in a grade A tertiary hospital in Xinjiang and to analyze the influencing factors.Methods:A convenience sampling method was applied to survey the clinical medical staff of our hospital with a questionnaire including general information, a self-assessment scale of research ability, and a survey of research cognition and training needs. A total of 618 questionnaires were collected with 609 valid returned responses, resulting in an effective return rate of 98.54%. Univariate and multiple linear regression analysis were applied to analyze the influencing factors of the total score of clinical medical staff's research ability.Results:The total score of research ability of 609 clinical medical personnel was 60.73±13.59. The results of multiple linear regression showed that participation in scientific research conferences, enthusiasm for scientific research activities, and the need for scientific research training all had positive effects on the self-assessment of scientific research ability, which together explained 52% of the total variance (adjusted R2=0.520, P<0.001). The top three " very important" options for medical staff research training were data analysis, research design, and research topic selection. Conclusions:Medical staff research skills need to be improved and there is a strong need for research training. Managers should refine scientific research management initiatives and provide hierarchical and targeted scientific research training to improve the overall medical staff's scientific research literacy and research ability, thereby promoting the progress of medical care in hospitals.

Objective: To analyze the relationship between school bullying and parent child separation of left behind children, and to provide a theoretical basis for preventing and controlling school bullying of left behind children. Methods: A total of 4 945 children aged 7 to 18 in Shangrao City were selected by stratified cluster random sampling to complete the Chinese version of the School Bullying Experience Questionnaire(C-SBEQ), and the differences of school bullying between left behind and non left behind children were compared. The parent child separation data of 1 791 left behind children was obtained by self designed questionnaire, and the influence of parent child separation characteristics on school bullying of left behind children was analyzed by binary Logistic regression. Results: The rates of school bullying, bully victimization and perpetration of left behind children were 21.3%, 18.3% and 3.0% respectively, which were higher than those of non left behind children(15.4%, 12.7%, 2.7%). And there were statistical significance in the detection rates of school bullying among left behind children in different schooling stages( χ 2=9.82, P < 0.05), the detection rates ranked as follows:21.4% in primary school, 18.9% in junior high school and 14.7% in senior high school. The rate of bullying perpetration among left behind children was significantly higher in boys (4.8%) than in girls (1.0%)( χ 2= 14.69, P <0.05). The rate bully victimization among former left behind children (children with left behind experience) in the younger than 7 years group ( 20.3 %) was higher than that in the older than 7 years group(13.4%)( χ 2=4.79, P =0.03). There was no significant differences in the detection rate of bullying perpetration among the left behind children with different parent child separation experiences ( P >0.05). Control schooling stages, Logistic regression analysis showed that taking former school age left behind children as reference, bully victimization risk of former pre school left behind children was 1.64 times( OR=1.64, 95%CI= 1.04 -2.59, P <0.05). Conclusion School bullying of left behind children is more severce than that of non left behind children. Early occurrence of parent child separation is associated with higher risk of bullying victimization among left behind children.

Objective: To explore the relationship between family structure with adolescents physical and mental health, and to provide a reference for promoting healthy development of adolescents in the family with particular structure. Methods: The stratified random sampling method was used to select 3 941 middle school students aged 13 to 18 years in Shangrao City of Jiangxi Province. Self designed questionnaire, Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Scale (GAD-7) were used to assess family structure, depressive symptoms and anxiety symptoms, participants were divided into underweight or overweight and obesity according to screening for underweight of school age children and screening for overweight and obesity of school age children. Results: The proportion of adolescents with special family structure was 7.0%. Univariate analysis showed that underweight rate of adolescents with divorced parents (31.2%) was higher than that of adolescents whose parents were still married (25.3%) ( χ 2= 3.55 , P <0.05), the detection rate of depressive symptoms in adolescents with special family structure(40.9%) was higher than that in adolescents with typical family structure(34.5%) ( χ 2=4.60, P <0.05). Multivariate analysis showed that the risk of depressive symptoms in adolescents with special family structure was 1.41 times higher than that in adolescents with typical family structure( 95% CI= 1.02-1.79, P <0.05). Conclusion No significant relationships between special family structure with underweight, overweight and obesity, and anxiety symptoms of adolescents are observed,however,special family structures are associated with increased risk of depressive symptoms in adolescents.

A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.