Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To evaluate the outcomes of intensive conservative treatment compared to conventional conservative treatment in patients with acute aortic syndrome(AAS).Methods:The study prospectively enrolled consecutive patients with AAS who were admitted to Beijing Anzhen Hospital, affiliated with Capital Medical University, and Beijing Dawanglu Emergency Rescue Hospital from January 2024 to December 2024. These patients with surgical contraindications or refused surgery for various reasons opted for conservative treatment. A total of 282 patients were included, and 15 patients with missing data or those who died without any treatment were excluded. Finally, 267 patients were enrolled, of whom 94 received intensive conservative treatment, and 173 received conventional conservative treatment, the inverse probability of treatment weighting (IPTW) was used to reduce the influence of confoundings. After adjusting of baseline datas via IPTW, the survival outcomes of the two groups were compared at 14 days, 30 days, and at the end of follow-up.Results:The results showed significant differences in acute phase survival rates between the enhanced conservative treatment group and the conventional conservative treatment group at 14 days(82.40%vs.53.20%, P<0.0001). Significant survival differences were also observed at 30 days and at 276-day mid-term follow-up (96.29% vs.51.60%, P<0.0001; 78.50% vs.48.50%, P<0.0001). In the subgroup analysis, for type A aortic dissection, the enhanced conservative treatment group had higher survival rates compared to the conventional conservative treatment group at 14, 30 and 276 days (63.46% vs.41.35%, P<0.05; 52.17% vs.37.90%, P<0.05; 50.00% vs. 31.97%, P<0.05). However, for type B aortic dissection, although the enhanced conservative treatment group had higher survival rates than the conventional conservative treatment group, no statistically significant differences were observed (96.29% vs. 80.00%, P=0.054; 95.65% vs.78.37%, P=0.067; 94.12% vs.74.20%, P=0.088). Conclusion:For patients diagnosed with AAS are forced to choose conservative treatment if emergency surgery is not possible in the first place, intensive conservative treatment strategies can significantly reduce the mortality in the acute phase compared with conventional conservative treatment. Mid-term follow-up, intensive conservative treatment still has a significant survival advantage.

Objective:To evaluate the outcomes of intensive conservative treatment compared to conventional conservative treatment in patients with acute aortic syndrome(AAS).Methods:The study prospectively enrolled consecutive patients with AAS who were admitted to Beijing Anzhen Hospital, affiliated with Capital Medical University, and Beijing Dawanglu Emergency Rescue Hospital from January 2024 to December 2024. These patients with surgical contraindications or refused surgery for various reasons opted for conservative treatment. A total of 282 patients were included, and 15 patients with missing data or those who died without any treatment were excluded. Finally, 267 patients were enrolled, of whom 94 received intensive conservative treatment, and 173 received conventional conservative treatment, the inverse probability of treatment weighting (IPTW) was used to reduce the influence of confoundings. After adjusting of baseline datas via IPTW, the survival outcomes of the two groups were compared at 14 days, 30 days, and at the end of follow-up.Results:The results showed significant differences in acute phase survival rates between the enhanced conservative treatment group and the conventional conservative treatment group at 14 days(82.40%vs.53.20%, P<0.0001). Significant survival differences were also observed at 30 days and at 276-day mid-term follow-up (96.29% vs.51.60%, P<0.0001; 78.50% vs.48.50%, P<0.0001). In the subgroup analysis, for type A aortic dissection, the enhanced conservative treatment group had higher survival rates compared to the conventional conservative treatment group at 14, 30 and 276 days (63.46% vs.41.35%, P<0.05; 52.17% vs.37.90%, P<0.05; 50.00% vs. 31.97%, P<0.05). However, for type B aortic dissection, although the enhanced conservative treatment group had higher survival rates than the conventional conservative treatment group, no statistically significant differences were observed (96.29% vs. 80.00%, P=0.054; 95.65% vs.78.37%, P=0.067; 94.12% vs.74.20%, P=0.088). Conclusion:For patients diagnosed with AAS are forced to choose conservative treatment if emergency surgery is not possible in the first place, intensive conservative treatment strategies can significantly reduce the mortality in the acute phase compared with conventional conservative treatment. Mid-term follow-up, intensive conservative treatment still has a significant survival advantage.

Objective To observe the value of MRI radiomics model for predicting postoperative prognosis of moderate carpal tunnel syndrome(CTS).Methods A total of 126 patients with moderate CTS who underwent endoscopic release and fat-suppressed proton density weighted imaging(PDWI)before operation were retrospectively enrolled.The patients were divided into good prognosis group(n=80)and poor prognosis group(n=46)based on postoperative functional evaluation,also randomly divided into training set and validation set at a ratio of 7∶3.Volume of interest(VOI)of the median nerve was obtained through delineating ROI of the affected wrist on fat suppressed PDWI.Radiomics features were extracted,and those associated with postoperative prognosis of CTS were screened in training set.Clinical prediction model,radiomics model and combined model of these two were established,and the predictive efficacy of the models were evaluated and compared according to the area under the curve(AUC)of receiver operating characteristic(ROC)curve.Results Patients in poor prognosis group were older than in good prognosis group(P＜0.05).A clinical model was constructed based on age.The radiomics model was constructed based on 6 radiomics features associated with postoperative prognosis of CTS,with predictive efficacy(AUC=0.872)higher than that of clinical model(AUC=0.604,P＜0.05)but not significantly different with that of the combined model(AUC=0.905,P＞0.05).Conclusion MRI radiomics model could be used to effectively predict postoperative prognosis of moderate CTS.

Objective To observe the value of MRI radiomics model for predicting postoperative prognosis of moderate carpal tunnel syndrome(CTS).Methods A total of 126 patients with moderate CTS who underwent endoscopic release and fat-suppressed proton density weighted imaging(PDWI)before operation were retrospectively enrolled.The patients were divided into good prognosis group(n=80)and poor prognosis group(n=46)based on postoperative functional evaluation,also randomly divided into training set and validation set at a ratio of 7∶3.Volume of interest(VOI)of the median nerve was obtained through delineating ROI of the affected wrist on fat suppressed PDWI.Radiomics features were extracted,and those associated with postoperative prognosis of CTS were screened in training set.Clinical prediction model,radiomics model and combined model of these two were established,and the predictive efficacy of the models were evaluated and compared according to the area under the curve(AUC)of receiver operating characteristic(ROC)curve.Results Patients in poor prognosis group were older than in good prognosis group(P＜0.05).A clinical model was constructed based on age.The radiomics model was constructed based on 6 radiomics features associated with postoperative prognosis of CTS,with predictive efficacy(AUC=0.872)higher than that of clinical model(AUC=0.604,P＜0.05)but not significantly different with that of the combined model(AUC=0.905,P＞0.05).Conclusion MRI radiomics model could be used to effectively predict postoperative prognosis of moderate CTS.

Background With the rise and deepening of positive psychology research, theoretical research on job burnout is also deepening worldwide, and some new theoretical models are proposed, such as the burnout-engagement continuum, but there is still a lack of empirical research and application in China. Objective To analyze the current situation and influencing factors of five profiles in the burnout-engagement continuum in working populations of China: job engagement, ineffective, overextended, disengaged, and burnout. Methods From August to October 2019 and June to September 2020, a total of 27344 subjects of key occupations in six typical industries, including teachers, firefighters, manufacturing workers, medical staff, flight attendants, and traffic police, were selected from 10 provinces (cities) in the eastern, middle, and western regions of China by multistage stratified cluster sampling method for cross-sectional investigation. Maslach Burnout Inventory-General Survey and Core Occupational Stress Scale were used to evaluate job burnout and occupational stress respectively. χ² test was used to compare rates of count data. Binary logistic regression was used for multivariate analysis of the five profiles. Results Among the subjects, 24.4%, 61.9%, 31.9%, 12.7%, and 11.8% were the prevalence rates of job engagement, ineffective, overextended, disengaged, and burnout, respectively. Flight attendants (35.7%), firefighters (29.0%), traffic police (28.5%), and manufacturing workers (26.5%) had high prevalence rates of job engagement profile. Medical stuff (62.9%) and manufacturing workers (61.8%) had high prevalence rates of ineffective profile. Teachers (39.2%), traffic police (37.2%), and medical stuff (35.5%) had high prevalence rates of overextended profile. Traffic police (17.9%), medical staff (14.3%), and teachers (13.4%) had high prevalence rates of disengaged profile. Traffic police (16.9%), medical staff (13.4%), and teachers (13.3%) had high prevalence rates of burnout profile. The results of multivariate analysis showed that gender, age, education level, marital status, weekly working hours, length of service, income per month, shift work, smoking, drinking, industry, and occupational stress entered into the regression equations of job engagement, ineffective, overextended, disengaged, and burnout. The risks of overextended (OR=1.456-2.970), disengaged (OR=1.306-4.092), and burnout (OR=1.302-4.102) among the high rating groups of the four factors of occupational stress were higher than those among the low rating groups. Flight attendants (OR=0.296) and firefighters (OR=0.329) had lower risks of burnout than teachers, and flight attendants (OR=0.392) and firefighters (OR=0.466) had lower risks of disengaged than teachers. Conclusion Among the prevalence rates of the five profiles in the burnout-engagement continuum in the 6 typical occupational populations in China, ineffective profile is the highest, followed by overextended, and burnout is the lowest. Gender, age, education level, marital status, weekly working hours, length of service, income per month, shift work, smoking, drinking, industry, and occupational stress have different effects on the five profiles, but industry and occupational stress have greater impacts on job burnout.

Background Manufacturing industry is the main body of China's national economy, and manufacturing employees provide solid guarantee and support for the vigorous development of China's manufacturing industry. The research on job burnout of manufacturing employees is helpful to prevent or alleviate the health damage and economic loss caused by job burnout. Objective To investigate the status and influencing factors of job burnout among manufacturing employees in China, and evaluate the potential mediating effect of job burnout between job stress and depressive symptoms. Methods A cross-sectional study was performed from August to October 2019 and from June to September 2020 in seven provinces of China. Study participants were recruited from 21 manufacturing companies covering 11 manufacturing subdivisions using multistage stratified cluster sampling. General Burnout Scale, Core Occupational Stress Scale, and Patient Health Questionnaire-9 were used to evaluate job burnout, job stress, and depressive symptoms. Mann-Whitney U test and Kruskal-Wallis H test were used to compare data between two independent samples and multiple independent samples of measurement data; Pearson χ² test was used to compare the rate of enumeration data; Spearman rank correlation analysis was used to evaluate correlation between selected variables; binary logistic regression was used for multivariate analysis; stepwise regression and bias-corrected Bootstrap method were used to test potential mediating effect. Results A total of 10973 valid questionnaires were collected. The M (P₂₅, P₇₅) scores of exhaustions, cynicism, professional efficacy, job burnout, job stress, and depressive symptoms were 1.6 (0.4, 3.0), 1.0 (0.2, 2.2), 4.8 (3.2, 5.7), 1.6 (0.8, 2.5), 45.0 (40.0, 50.0), and 7.0 (4.0, 11.0), respectively. The positive rates of job burnout, job stress, and depressive symptoms were 53.4% (5865/10973), 28.4% (3116/10973), and 29.4% (3231/10973), respectively. The influencing factors of job burnout were gender (versus male; OR for female=0.852), age (versus≤25 years old; ORs for 26-30, 31-40, 41-50, 51-60 years old=0.824, 0.819, 0.738, 0.677), education level (versus junior high school or below; ORs for senior high school/technical secondary school, junior college/vocational college, graduate school or above=1.119, 1.345, 1.331), income per month (versus＜3000 yuan; ORs for 3000-4999, 5000-6999, 7000-8999, 9000-10999, ≥11000 yuan=0.513, 0.470, 0.430, 0.375, 0.411), position (versus assembly line; OR for non-assembly line=0.814), average weekly working hours (versus ≤40 h; ORs for 61-70, ＞70 h=1.199, 1.519), and drinking (versus non-drinking; OR for drinking=1.261). Job burnout was positively correlated with job stress and depressive symptoms (r=0.556, 0.508, P<0.001). Job burnout played a partial mediating role between job stress and depressive symptoms, and its contribution value accounted for 63.2% of the total effect. Conclusion Manufacturing industry employees' job burnout problem is prominent in China, and there are many factors affecting job burnout. Job burnout may play a partial mediating role between job stress and depressive symptoms in the target group.