Objective To explore the value of phase contrast(PC)MRI intracranial hemodynamic parameters for predicting acute mountain sickness(AMS).Methods Totally 72 healthy young volunteers were prospectively recruited.Intracranial hemodynamic parameters of internal carotid artery(ICA)and internal jugular vein(IJV)were measured using PC MRI under normal breathing,as well as mild,moderate and severe Valsalva maneuvers(VM)in plain area.The subjects were divided into AMS group(n=9)and non-AMS group(n=63)according to results of Lake Louise score(LLS)10 h after a rapid ascent to plateau area with altitude of 4 411 m.Univariate and multivariate logistic regression analyses were performed to screen independent predictors of AMS under different states and then construct single and combined VM states prediction models.Receiver operating characteristic curves were plotted,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of each model.Results ICA pulsatility index(PIICA)under mild VM,IJV cross-sectional area(SIJV)under moderate VM and IJV resistance index(RIIJV)under severe VM were all independent predictors of AMS(all P＜0.05).The efficacy of combined VM states model(AUC=0.869)for predicting AMS was higher than each single VM state model(AUC=0.698-0.738).Conclusion The model constructed based on PIICA under mild VM,SIJV under moderate VM and RIIJV under severe VM could be used to effectively predict AMS.

Objective To explore the value of phase contrast(PC)MRI intracranial hemodynamic parameters for predicting acute mountain sickness(AMS).Methods Totally 72 healthy young volunteers were prospectively recruited.Intracranial hemodynamic parameters of internal carotid artery(ICA)and internal jugular vein(IJV)were measured using PC MRI under normal breathing,as well as mild,moderate and severe Valsalva maneuvers(VM)in plain area.The subjects were divided into AMS group(n=9)and non-AMS group(n=63)according to results of Lake Louise score(LLS)10 h after a rapid ascent to plateau area with altitude of 4 411 m.Univariate and multivariate logistic regression analyses were performed to screen independent predictors of AMS under different states and then construct single and combined VM states prediction models.Receiver operating characteristic curves were plotted,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of each model.Results ICA pulsatility index(PIICA)under mild VM,IJV cross-sectional area(SIJV)under moderate VM and IJV resistance index(RIIJV)under severe VM were all independent predictors of AMS(all P＜0.05).The efficacy of combined VM states model(AUC=0.869)for predicting AMS was higher than each single VM state model(AUC=0.698-0.738).Conclusion The model constructed based on PIICA under mild VM,SIJV under moderate VM and RIIJV under severe VM could be used to effectively predict AMS.

Purpose To construct the model of differentiating primary central nervous system lymphoma(PCNSL)and atypical glioblastoma(GBM)by combining multi-parameter MRI radiomics and six machine learning algorithms,thus to compare the diagnostic efficacy of different machine learning algorithms.Materials and Methods The clinical and imaging data of 77(125 lesions)PCNSL and 90 atypical GBM(108 lesions)from PLA General Hospital and public databases were retrospectively analyzed from January 2013 to December 2023,and all patients were randomly divided into a training set(163 cases)and a validation set(70 cases)according to 7∶3.T1WI,T2WI and T1-weighted contrast-enhanced sequences were selected for tumor segmentation,and 1 132 radiomics features were extracted from each region of interest.The intraclass correlation coefficient(ICC)was used for the consistency test,and image features with ICC≥0.85 were selected.ICC and recursive feature elimination were used to select the best radiomics features.Six classifiers were used to train and verify three single sequence feature sets,three double-sequence sets and one multi-sequence set.The receiver operating characteristic curve was used to evaluate the diagnostic efficacy of the model.Results The combination model of the support vector machine of radial basis function classifier and multi-sequence feature set were the best model for differentiating PCNSL and atypical GBM.The area under the curve of the training set and the validation set were 0.969 and 0.913,respectively;and the accuracy were both 0.886.Conclusion Noninvasive extraction of multiparametric MRI features combined with machine learning algorithms can effectively differentiate PCNSL and atypical GBM,which provides support for the development of individualized treatment plans for patients.

Objective:To investigate the characteristics of changes in muscle quality and strength in middle-aged and elderly patients with type 2 diabetes mellitus(T2DM).Methods:A total of 670 patients(320 males and 350 females)aged 50 years and over from the endocrinology departments of 9 hospitals in Beijing were recruited as the type 2 diabetes mellitus group(T2DM group)by using systematic random sampling, and 214(54 males and 75 females)age-matched Beijing Hospital retirees without T2DM were randomly enrolled as the control group.Body composition was measured by using bioimpedance analysis.Low muscle mass was defined as appendicular skeletal muscle mass index(ASMI)below 7.18 kg/m 2 in men and 5.73 kg/m 2 in women.Low muscle strength was defined as grip strength below 29.5 kg in men and 21.2 kg in women.Sarcopenia was defined by the presence of low muscle mass with low grip strength.Muscle quality was calculated by grip strength divided by muscle mass of the dominant upper limb, and muscle strength per mass unit was compared.Multivariate Logistic regression was used for correlation analysis. Results:The fasting blood glucose(FPG)level, the waist to hip ratio, the percentages of smokers and drinkers, and the proportions of subjects with concurrent hypertension and coronary heart disease were higher in the T2DM group than in the control group( P<0.05 or 0.01). Compared with the control group, grip strength and muscle quality decreased significantly in male T2DM patients( t=4.408 and 3.972, P<0.01). In male participants, BMI( t=-5.567, 95% CI: -0.375~-0.179, P<0.01)and glycated hemoglobin(HbA1c)( t=-2.322, 95% CI: -0.420~-0.035, P<0.05)were negatively correlated with muscle quality, while old age( OR=1.062, 95% CI: 1.023~1.103, P<0.01; OR=1.074, 95% CI: 1.027~1.122, P<0.01)and increased HbA1c level( OR=1.062, 95% CI: 1.023~1.103, P<0.01; OR=1.074, 95% CI: 1.027~1.122, P<0.01)were risk factors for low muscle strength and sarcopenia. Conclusions:Compared with non-diabetes patients, muscle quality and strength decrease significantly in middle-aged and elderly male T2DM patients.Besides aging, increased levels of HbA1c and BMI are risk factors for low muscle quality and strength.

Objective To investigate the metabolic characteristics,body composition and dietary intake in elderly sarcopenia patients with type 2 diabetes(T2DM).Methods A total of 652 T2DM patients(327 males and 325 females)aged over 60 years were selected from endocrinology department of nine different hospitals in Beijing.Body composition was measured by bioimpedance analysis,and the appendicular skeletal mass index(ASMI)was calculated.Sarcopenia was defined as body height-adjusted skeletal muscle mass (ASMI)below the lower quintile of the young reference group.The diagnostic cutoff points for sarcopenia were 7.18 kg/m2 for men and 5.73 kg/m2 for women.All patients were divided into the sarcopenia group versus the control group(below vs.not below 7.18 kg/m2 for men and 5.73 kg/m2 for women).The anthropometric parameters,body composition,biochemical laboratory results and dietary intake were compared between the two groups.The blood glucose target levels were less than 7 mmol/L of fasting plasma glucose(FPG)or less than 7％ of haemoglobin A1c(HbA1c).The obesity ratio was calculated according to body fat percentage more than 25％ in men and more than 30％ in women as obesity.Results There were 116 (17.8％)patients in the sarcopenia group (men/women,82/34),and 536 (82.2 ％) patients in the control group (men/women,245/291).Age was higher in the sarcopenia group than in the control group(t =4.385,P =0.000),and body mass index and waist hip ratio(WHR)were lower in the sarcopenia group than in the control group(t =11.724 and 4.173,P=0.000 and 0.000).FPG[(7.5±2.4) mmol/L vs.(8.5±2.5)mmol/L,t =-3.450,P=0.001]and HbA1c[(7.0±1.6) ％ vs.(8.2± 1.7) ％,t =-5.745,P =0.000] were higher in male sarcopenia group than in male control group.The control rate of FPG (127.0％ or 51.8％ vs.27.0％ or 32.9％,x2=8.817,P=0.003)and HbA1c(131.0％ or 53.5％ vs.23.0％ or 28.0％,x2 =15.934,P=0.000)were lower in the sarcopenia group than in the control group.The red blood cell counts,hemoglobin and serum albumin levels,estimated glomerular filtr ationrate(eGFR)were lower in male sarcopenia group than in the male control group(P＜0.05).eGFR was lower in female sarcopenia group than in female control group(t =4.090,P =0.000).Both in men and women,ASMI,grip power,fatless circumference on upper arm,bone mineral content and basal metabolic rate were lower in the sarcopenia group than in the control group(P＜0.05).The total daily intake of energy,carbohydrate,protein and fat were lower in male sarcopenia group than in male control group(P＜ 0.05).Conclusions Compared with the control group,sarcopenia patients are older with worse glycemic control and lower levels of BMI,WHR,renal function,muscle mass and muscle strength.Sarcopenia patients are more prone to osteoporosis.Furthermore,they have poorer nutritional status with an imbalance of dietary intake.Appropriate increase of protein especially high quality protein intake should be recommended to sarcopenia patients with T2DM.

Objective To explore the scientificity and feasibility of using weighted rank-sum ratio (RSR) method based on diagnosis-related groups ( DRGs) indicators in nursing performance evaluation. Methods Homepage data of medical records were extracted from inpatients discharged in 2017, and " CN-DRGs" burster was used to obtain the DRGs data. Data of medical safety were obtained from the nursing adverse event management system, while data of nursing grading information and medical expenses were obtained from the hospital information system, and the patient satisfaction was obtained in a questionnaire survey. Based on the indicators available, the weighted RSR was applied to evaluate the nursing performance from the 11 dimensions, namely workload, nursing competence, nursing quality, nursing personnel allocation, patient satisfaction, etc. Results The results of the weighted RSR showed that 43, 39, and 6 wards of total 88 wards of the hospital were rated as excellent, medium and poor grades respectively. The results of ANOVA showed that the difference of the 3 grades was significant (F=170. 391, P<0.001). The nursing performance evaluation results were consistent with the actual situation. Conclusions The evaluation of nursing performance with weighted RSR method based on DRGs indicators prove its practical application value, as this method can not only provide data support for nursing personnel allocation, but also provide reference for nursing quality evaluation and supervision.

With the rapid economic development and dramatic changes in lifestyle, the prevalence of overweight and obesity in China has been increasing significantly and become a serious public health threat. This article introduced the main contents of "China Blue Paper on Obesity Prevention and Control", aiming to facilitate understanding and applications of the "China Blue Paper on Obesity Prevention and Control" by policymakers, researchers and practitioners in related fields. Built upon these, recommendations were made for obesity screening, diagnosis, treatment and management, prevention and control policies and strategies, and future research priorities in China.

With the rapid economic development and dramatic changes in lifestyle, the prevalence of overweight and obesity in China has been increasing significantly and become a serious public health threat. This article introduced the main contents of "China Blue Paper on Obesity Prevention and Control", aiming to facilitate understanding and applications of the "China Blue Paper on Obesity Prevention and Control" by policymakers, researchers and practitioners in related fields. Built upon these, recommendations were made for obesity screening, diagnosis, treatment and management, prevention and control policies and strategies, and future research priorities in China.

With the rapid economic development and dramatic changes in lifestyle, the prevalence of overweight and obesity in China has been increasing significantly and become a serious public health threat. This article introduced the main contents of "China Blue Paper on Obesity Prevention and Control", aiming to facilitate understanding and applications of the "China Blue Paper on Obesity Prevention and Control" by policymakers, researchers and practitioners in related fields. Built upon these, recommendations were made for obesity screening, diagnosis, treatment and management, prevention and control policies and strategies, and future research priorities in China.

Objective To analyze the dietary habits, energy intake and expenditure, anthropometrics, and body composition of the outpatients visiting the weight loss clinic of Beijing Hospital.Methods We pro-spectively enrolled 89 consecutive patients with body mass index ( BMI) ≥24 kg/m2 from November 2014 to August 2015 in the weight loss clinic of Beijing Hospital.There were 35 male and 54 female, with the mean age of (45.8 ±16.4) years.We divided them into two groups:the diabetes group (n=35) and the non-diabetes group (n=54), and compared the dietary habits, energy intake and expenditure, anthropometrics and body composition between the two groups.Results Regardless of diabetes, the overweight and obese patients all ate fast, mostly finishing a meal in about 10 minutes.They preferred Chinese food and meat, and disliked hot food.The frequency of dinning out in the non-diabetes group (3-5 times per week) was higher than that in the diabetes group (1-2 times per week) .Compared with the diabetes group, the non-diabetes group had higher fat-to-energy ratio [(34.9 ±7.6)%vs.(30.8 ±5.9)%], but lower carbohydrate intake [(232.2 ±59.7) g vs.(283.6 ±89.5) g], carbohydrate-to-energy ratio [ (47.9 ±8.3)%vs.(53.4 ±7.1)%], and the ratio of resting metabolic rate to body weight [ (66.9 ±9.6) kJ/(d? kg) vs.(71.1 ±7.9) kJ/(d? kg)] (all P<0.05).There were no statistically significant differences between the two groups in total energy intake, pro-tein intake, high quality protein intake, fat intake, protein-to-energy ratio, and resting metabolic rate (all P>0.05).Anthropometrics showed that the mean BMI of the patients was (32.8 ±4.4) kg/m2, with the maxi-mum being 53.5 kg/m2.The hip circumference [ (117.15 ±9.9) cm vs.(111.1 ±8.2) cm], upper arm circumference [ (36.4 ±3.8) cm vs.(34.0 ±3.3) cm], and triceps skinfold thickness [ (36.1 ±8.9) mm vs.(31.6 ±8.8) mm] were larger in the non-diabetes group than in the diabetes group (all P<0.05), but the mean age was lower in the non-diabetes group [ (41.7 ±16.9) years vs.(52.9 ±13.1) years) (P=0.01).There were no statistically significant differences between the two groups in body weight, BMI, waist circumference, neck circumference, and bilateral hand grip strength (all P>0.05).According to body compo-sition analysis, the body weight [ (94.8 ±18.3) kg vs.(86.9 ±17.2) kg], body fat mass [ (39.7 ± 11.3) kg vs.(33.5 ±8.9) kg], body fat percentage [ (41.7 ±6.5)%vs.(38.5 ±6.7)%], and visceral fat area [ (145.3 ±24.8) cm2 vs.(130.7 ±27.5) cm2 ] were larger in the non-diabetes group than in the di-abetes group ( all P<0.05) .There were no statistically significant differences between the two groups in BMI and skeletal muscle mass (both P>0.05).Conclusion Compared with diabetes patients, overweight and obese non-diabetes patients may be younger, having worse dietary habits, and having larger body fat mass, body fat percentage, and visceral fat area.