Objective: To explore the network structure of dietary intake and physical fitness subtypes among junior and senior high school students, so as to provide precise and efficient intervention guidance for improving abnormal physical health issues in adolescents. Methods: Based on the 2020-2021 Database of Youth Health (DYH), a total of 9 730 junior and senior high school students in Shandong Province were included for analysis. Dietary intake was assessed using a modified Chinese Dietary Quality Questionnaire, and physical fitness was evaluated according to the "2014 National Student Physical Fitness Standard". The Kmeans clustering algorithm was employed to identify potential subtypes of physical fitness in junior and high school students. Network analysis was used to construct a network linking dietary intake and physical fitness symptoms and to identify clusters of dietary behaviors and physical fitness symptoms among adolescents of different genders. Results: The physical fitness among junior and senior high school students of different genders were categorized into two subtypes: the baseline steady type (3 427 male students, 4 039 female students) and the morphological deviation type (1 294 male students, 970 female students). Statistically significant differences were observed in network strength and network connectivity strength among different genders and physical fitness subtypes (all P<0.05). Connections between food intake and physical health symptom clusters across different genders and physical health types among junior and senior high school students were primarily achieved through instant noodle intake and physical fitness (males of morphological deviation type, weight=0.06), fruit intake and physical fitness (males of baseline steady type, weight=-0.07), potato intake and vital capacity (females of morphological deviation type, weight=0.09), and processed meat intake and vital capacity (females of baseline steady type, weight=0.05). Conclusions Dietary intake serves as a significant modifiable risk factor for the physical fitness of junior and high school students. Interventions should focus on promoting healthy eating habits.

Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.

Objective:To investigate the influence factors of poor efficacy after flap repair operation in patients with pressure ulcers.Methods:The retrospective case series study was conducted. From January 2011 to June 2021, 125 patients with stage Ⅲ and Ⅳ pressure ulcers treated in Hainan General Hospital met the inclusion criteria. There were 82 males and 43 females, aged 15-90 (57±20) years. According to the postoperative effects, the patients were divided into poor efficacy group (47 cases) and good efficacy group (78 cases). The clinical data of patients in the two groups were collected, including the age, gender, location, stage, size, and bone exposure of pressure ulcers, preoperative microorganism culture results of wound exudate sample, whether combined with osteomyelitis, diabetes, lower limb paroxysmal myospasm, and gatism or not, the number of surgical debridement combined with negative-pressure wound therapy, type of surgical flap, postoperative position, and preoperative albumin, leukocyte, C-reactive protein (CRP), and hemoglobin. Data were statistically analyzed with independent sample t test, Mann-Whitney U test, and chi-square test. The binary multivariate logistic regression analysis was conducted to screen the independent risk factors influencing the poor efficacy after flap repair operation in 125 patients with stage Ⅲ and Ⅳ pressure ulcers. Results:The ratio of patients with lower limb paroxysmal myospasm in poor efficacy group was 22/47, which was significantly higher than 3/78 in good efficacy group ( χ2=33.83, P<0.01). The preoperative hemoglobin level of patients in poor efficacy group was (102±17) g/L, which was significantly lower than (113±20) g/L in good efficacy group ( t=-3.24, P<0.01). The preoperative CRP level of patients was 39.1 (14.1, 91.6) mg/L in poor efficacy group, which was significantly higher than 15.3 (6.6, 42.0) mg/L in good efficacy group ( Z=-3.04, P<0.01). There were no statistically significant differences in other indexes between patients in the two groups ( P>0.05). Multivariate logistic regression analysis showed that age, lower limb paroxysmal myospasm, and preoperative hemoglobin level were the independent risk factors for poor efficacy after flap repair operation in patients with pressure ulcers (with odds ratios of 1.03, 40.69, and 0.97, 95% confidence intervals of 1.00-1.06, 9.18-180.39, and 0.95-1.00, respectively, P<0.05 or P<0.01). Conclusions:Poor efficacy after flap repair operation in patients with pressure ulcers is affected by many factors, among which the age, lower limb paroxysmal myospasm, and preoperative hemoglobin level are the independent risk factors.

Objective To establish and validate a voxel-based method for the quantitative detection of air trapping (AT),and to explore its diagnostic value by preliminarily apply this method in chronic obstructive pulmonary disease (COPD) patients.Methods From March 2015 to February 2016,fifty healthy young volunteers and eighteen COPD patients who underwent both end-inspiratory and end-expiratory CT were included from the Digital Lung Multi-center Study.The quantitative parameters of AT and emphysema were measured by both the voxel-based quantitative method and the conventional threshold method,respectively.All subjects underwent pulmonary function examination within 3 days after CT examination.For healthy volunteers,paired sample rank-sum test was used to compare the difference of quantitative parameters between voxel-based method and threshold method,Spearman rank correlation analysis was used to investigate the correlation between quantitative parameters of the two methods and pulmonary function.For COPD patients,the distribution and extent of AT and emphysema in patients with similar degree of pulmonary function (PFT) injury were observed.Results There were varying degrees of AT in the asymptomatic youth,with a median value of 5.70％ for the voxel-based method and with a median value of 7.96％ for the conventional threshold method,there was significant difference(Z=-4.015,P＜0.001).The correlation between AT and emphysema parameters of the voxel-based method and PFT parameters (r=-0.399 and-0.494,-0.335 and-0.439 separately,P＜0.05) were higher than that of the conventional threshold method,respectively (r=-0.357 and-0.453,-0.284 and-0.391,respectively;all P＜0.05).Furthermore,the voxel-based method can classify COPD patients with similar degree of pulmonary function injury into three subtypes:AT-dominant,emphysema-dominant,and mixed.Conclusions The voxel-based AT quantitative measurement method not only has high sensitivity and accuracy,but also provides imaging phenotype for the diagnosis of COPD and provides assistant decision-making for clinical management.

Objective To analyze the urine trace albumin(mALb)/creatinine(Cr) ratio and blood uric acid(UA),and other various metabolic index level in patients with diabetic nephropathy(DN),combined with clinical data such as patients' age,body mass index(BMI),course of diseases,to explore the related mechanism of occurrence and development of DN.Methods 76 DN patients were selected.The microalbuminuria group(urinary mALb/Cr<300μg/mg) had 46 cases,the clinical albuminuria group(urinary mALb/Cr≥300μg/mg) included 30 cases,another 49 diabetic patients without kidney damage were seleted as control group.The urinary mALb/Cr,blood UA,fasting blood glucose(FBG),triacylglycerol(TG),total cholesterol(TC),high-density lipoprotein(HDL),low density lipoprotein(LDL),glycosylated hemoglobin(HbA1c) levels were determined.The BMI and the length of the course of the disease calculate.Results The patients' age,course of the disease,urinary mALb/Cr,blood UA,FBG,TC,TG,LDL,HbA1c and BMI level in the clinical albuminuria group and microalbuminuria group were significantly higher than those in the control group,the differences were statistically significant (F=6.18,12.48,141.43,12.48,8.49,4.98,6.18,3.89,3.17,3.89,all P<0.05).The high uric acid hematic disease rates of the clinical albuminuria group and microalbuminuria group were 26.09% and 26.09%,which were significantly higher than 10.20% of the control group,the differences were statistically significant(x2=4.074,24.833,all P<0.05).Urinary mALb/Cr was positively correlated with age,duration,BMI,UA,TG,TC,LDL,FBG,HbA1c(r=0.120,0.299,0.148,0.340,0.157,0.149,0.103,0.487,0.103).Multiple linear stepwise regression analysis suggested that duration,blood UA,FBG were independent risk factors of urinary mALb/Cr;TG,BMI,urinary mALb/Cr were independent risk factors for blood UA.Conclusion Urinary mALb/Cr and blood UA are the independent risk factors,high uric acid hematic disease may participate in the development process of DN,and diabetes duration,UA,BMI,TG,TC,LDL,FBG,HbA1C associated with increased urinary mALb/Cr excretory DN patients,the effective monitoring can improve the symptoms of DN and quality of life.