Objective: To analyze the consumption frequency of major foods among Chinese children aged 6-17 years old, and to provide a basis for optimizing the dietary structure of children in China. Methods: Using data from the China Nutrition and Health System Survey and Application Program for Children 0-18 years old, 56 734 children aged 6-17 years old from North, Norththeast East, Central, South, Southwest and Northwest seven regions in China were selected for the study using stratified cluster random sampling from 2019 to 2021. A food frequency questionnaire was used to investigate the intake frequency of eight food groups in a month, including fresh vegetables, fresh fruits, livestock and poultry meats, aquatic products, eggs, dairy products, legumes, and cereals and potatoes. The foods were grouped according to whether they met the recommended intake criteria outlined in the Dietary Guidelines for Chinese Residents 2022. The〖KG*2〗χ2 test was used to compare the differences in the proportion of childrens intake frequency of each food group meeting the standard in different regions and age groups. Results: The proportions of Chinese children aged 6-17 years who consumed fresh vegetables and cereals and potatoes ≥3 times/d were 12.1% and 67.2%, respectively. The proportions of children who consumed fresh fruits, livestock and poultry meats, eggs and dairy products ≥1 time/d were 50.8%, 58.8%, 36.0% and 54.3%, respectively. The proportion of legumes consumed ≥4 times/week was 37.4%, and the proportion of aquatic products consumed ≥2 times/week was 39.7%. Fresh vegetables (5.5%), fresh fruits (33.1%), and dairy products (36.4%) had the lowest frequency of meeting the recommended standards in South China, and aquatic products (27.4%) and eggs (21.1%) had the lowest frequency of meeting the recommended standards in Northwest (P<0.008 3). Conclusion The overall intake frequency of fresh vegetables, fresh fruits, legumes, and dairy products are insufficient among Chinese children, with significant regional variations.

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

Objective: To analyze the association of eating dining locations and their association with nutritional status among Chinese children aged 6-17 years,so as to provide reference for guiding children s reasonable diet. Methods: Stratified random cluster sampling was used to select children aged 6 to 17 years from 28 cities and rural areas of 14 provinces in East, North, Central, South, Southwest, Northwest, Northeast of China, and a total of 52 535 children were included in the study from 2019 to 2021. Information including dining locations, demographic characteristics, dietary intakes and physical activity were collected through a questionnaire survey. Fasting body height and weight were measured in the morning. Unordered multiclass Logistic regression analysis was conducted to assess the relationship between dining locations and nutritional status in children. Results: Regarding children s dining locations, 66.3% ate breakfast at home,25.8% ate breakfast at school,7.9% ate breakfast outside (small dining tables, restaurants, stalls, etc.); 67.7% ate dinner at home,29.0% ate dinner at school,3.3% ate dinner outside; and 63.6% ate lunch at school,30.8% ate lunch at home,5.7% ate lunch outside. The prevalence rates of overweight/obesity and undernutrition were 28.6% and 9.3%, respectively. The adjusted multiclass Logistic regression analysis (controlling for age, region, parental education, household income, total energy intake, and moderate-to-vigorous physical activity) demonstrated that, compared to eating at home, school based breakfast and dinner consumption was associated with significantly lower overweight/obesity risks for both genders (boys: breakfast OR =0.70, 95% CI =0.65-0.75; dinner OR =0.80, 95% CI = 0.74- 0.86; girls: breakfast OR = 0.89 , 95% CI = 0.82-0.96; dinner OR =0.88, 95% CI =0.81-0.95), whereas eating lunch away from home significantly increased overweight/obesity risks (boys: OR =1.32, 95% CI =1.17-1.48; girls: OR =1.43, 95% CI =1.26- 1.62 ), with all associations being statistically significant ( P <0.05). After adjusting for confounding factors, boys who ate breakfast away from home showed a significantly reduced risk of undernutrition ( OR =0.80,95% CI =0.66-0.97), while those consuming lunch away from home had an increased risk ( OR =1.26, 95% CI =1.01-1.57) ( P <0.05). Conclusions The choice of dining locations for children is becoming more diverse, and a relatively high proportion of children eat meals outside the home and at school. Eating out have a higher risk of malnutrition for children. School feeding may be beneficial to children s physical health.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. Materials and Methods: Male Sprague–Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. Results: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. Conclusions Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: Erectile dysfunction (ED) is a common male sexual dysfunction. Gut microbiota plays an important role in various diseases. To investigate the effects and mechanisms of intestinal flora dysregulation induced by high-fat diet (HFD) on erectile function. Materials and Methods: Male Sprague–Dawley rats aged 8 weeks were randomly divided into the normal diet (ND) and HFD groups. After 24 weeks, a measurement of erectile function was performed. We performed 16S rRNA sequencing of stool samples. Then, we established fecal microbiota transplantation (FMT) rat models by transplanting fecal microbiota from rats of ND group and HFD group to two new groups of rats respectively. After 24 weeks, erectile function of the rats was evaluated and 16S rRNA sequencing was performed, and serum samples were collected for the untargeted metabolomics detection. Results: The erectile function of rats and the species diversity of intestinal microbiota in the HFD group was significantly lower, and the characteristics of the intestinal microbiota community structure were also significantly different between the two groups. The erectile function of rats in the HFD-FMT group was significantly lower than that of rats in the ND-FMT group. The characteristics of the intestinal microbiota community structure were significantly different. In the HFD-FMT group, 27 metabolites were significantly different and they were mainly involved in the several inflammation-related pathways. Conclusions Intestinal microbiota disorders induced by HFD can damage the intestinal barrier of rats, change the serum metabolic profile, induce low-grade inflammation and apoptosis in the corpus cavernosum of the penis, and lead to ED.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.