Objective: To analyze the features of unhealthy lifestyle patterns among primary and secondary school students in the nutrition improvement program for rural compulsory education students (NIPRCES) areas in China in 2021 and 2023, so as to provide data support for lifestyle promotion and healthy development among primary and secondary school students. Methods: Adopting a cluster random sampling method, data on primary and secondary students aged 7-15 years from nutrition and health surveillance of China NIPRCES in 2021 and 2023 were collected. The prevalence of unhealthy lifestyles among primary and secondary students such as physical inactivity, outdoor inactivity, excessive screen time, and sleep deprivation by gender, school section, urban/rural, and region were analyzed. The reporting rates of the above indicators among primary and secondary students were compared by Chi-square test. Results: In 2021 and 2023, the rates of moderate to vigorous physical inactivity among primary and secondary school students were 79.2% and 80.4%, the rates of outdoor inactivity were 42.8% and 49.3%, the rates of excessive video time were 2.6% and 2.9%, the rates of sleep deprivation were 32.9% and 22.6%, and the differences were statistically significant( χ 2=51.86,1 071.48,18.36,3 296.99, P <0.05). In 2023, the rate of outdoor inactivity for primary and secondary students increased by 6.5 percentage points compared with 2021, and the rate of sleep deprivation decreased by 10.3 percentage points compared with that in 2021. In 2021 and 2023, the reporting rates of moderate to vigorous physical inactivity, outdoor inactivity, and sleep deprivation among girls and junior high school students were higher than those among boys ( χ 2=174.41,180.11; 175.75, 85.46 ;92.22,151.35) and elementary school students ( χ 2=136.64,5.75; 40.55,4.71;162.80,3 291.61); the reporting rates of moderate to vigorous physical inactivity( χ 2=194.43,118.60) and sleep deprivation ( χ 2=969.66,983.72) among urban students were higher than those among rural students; the reporting rates of excessive video time for boys and junior high school students were higher than those for girls ( χ 2=103.62,84.85) and elementary school students ( χ 2=810.09,626.51)( P <0.05). From a regional distribution perspective, the reporting rates of moderato to vigorous physical inactivity, outdoor inactivity, and excessive video time among primary and seconday school students in the central and western regions were lower than those in the eastern region ( χ 2= 663.44,302.78; 356.97,82.10;50.89,81.83) ( P <0.05). Conclusions Unhealthy lifestyles remain prevalent among primary and secondary students in NIPRCES areas of China. These findings underscore the need to strengthen policy implementation for promoting healthy lifestyles among primary and secondary school students.

Objective: To understand the prevalence of elevated blood pressure and its association with dietary patterns in children and adolescents in China, providing evidence for developing dietary intervention of hypertension in children and adolescents. Methods: Data were derived from the China Children s Nutrition and Health System Survey and Application Project(2019-2021). A stratified cluster random sampling method was used to include 7 933 participants from 28 survey sites in seven major regions of Northeast, North, Northwest, East, Central, South and Southwest China. Multivariate Logistic regression models were used to analyze associations between demographic characteristics, nutritional status and elevated blood pressure. Exploratory factor analysis identified dietary patterns, which were divided into three quartile groups (T3, T2, T1) based on factor scores (compliance for dietary pattern) from high to low, and multivariate Logistic regression model assessed the correlation between elevated blood pressure and dietary patterns. Results: The prevalence of elevated blood pressure was 15.4% among Chinese children aged 7-17 years. Significant differences were observed across nutritional status (reference: underweight; normal weight: OR =1.57; overweight: OR = 2.61 ; obesity: OR =3.85), urban/rural residence (reference: rural; urban: OR =0.86), and paternal education (reference: junior high school and below; bachelor degree or above: OR =0.68) ( P <0.05). The detection rates of high blood pressure in T3 group children and adolescents with four dietary patterns (staple food, animal based food, snacks, vegetables and fruits) were 15.7%, 14.6%, 16.8%, and 15.8%, respectively. After adjusting for residence, paternal education, and nutritional status, the "snack dietary pattern" (mainly candy, sugar sweetened beverages, and processed snacks) showed positive associations with elevated blood pressure in T2 ( OR =1.21) and T3 ( OR =1.19) tertiles ( P <0.05). Conclusions The snack dietary pattern is a related factor for elevated blood pressure in children and adolescents. Restricting unhealthy snack intake may promote cardiovascular health.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

OBJECTIVE To analyze the characteristics of pediatric medicines with priority review and approval for marketing in China， providing a reference for promoting enterprise R&D and production， as well as improving the supply guarantee mechanism for pediatric medicines. METHODS Based on publicly available data sources such as List of Approved Information for Pediatric Medications Subject to Priority Review and Approval， Pharnexcloud biomedical database， and National Medical Insurance Drug Directory， this study conducted a comprehensive analysis of the main characteristics of pediatric medicines with priority review and approval for marketing. RESULTS As of June 30， 2024， a total of 68 pediatric medicines had been approved through the priority review and approval process， covering 12 therapeutic areas， with oral dosage forms accounting for 64.71%. The median time from application to inclusion in priority review was 35.50 days， with an average of 41.69 days. The median time from inclusion in priority review to market approval was 1.24 years， with an average of 1.42 years. This included 12 domestic new medicines， 21 domestic generic medicines， 35 imported medicines， as well as 29 pediatric-specific medicines and 21 orphan medicines. Additionally， 31 of these medicines had been included in the medical insurance catalog， representing a proportion of 45.59%. CONCLUSIONS Currently， a trend of differentiated competition is emerging between domestic and imported pediatric medicines. The therapeutic areas for pediatric medicines are continuously expanding， and the dosage forms are becoming more tailored to children’s needs. However， there are still issues such as slow progress in new medicine development， insufficient stability in the medicine review and approval process， and a need to increase the proportion of medicines included in medical insurance.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Background Per- and polyfluoroalkyl substances (PFASs) are a class of persistent organic pollutants that pose potential health risks to humans. Infants and young children have higher requirements for food safety due to the underdeveloped detoxification and immune systems. Therefore, developing a comprehensive method for determination of PFASs and their novel alternatives in infant complementary food is of great significance. Objective To develop an analytical method using liquid chromatography high-resolution mass spectrometry technology for determination of 55 PFASs in plant- and animal-derived infant complementary fruit purees. Methods Oasis WAX (200 mg, 6 CC) solid-phase extraction columns were used for sample enrichment and purification. The pH of the acetonitrile extract was adjusted using 0%, 1%, 1.5%, and 2% formic acid aqueous solutions to evaluate its impact on the recovery rate of target compounds. Additionally, the impact of a 2 mL methanol wash during the purification process on the recovery of target compounds was assessed to determine the optimal pretreatment conditions. Three types of chromatographic columns—Agilent Poroshell 120 EC-C₁₈, Thermo InfinityLab Poroshell 120 Aq-C₁₈, Acquity Waters BEH-C₁₈, and changes in mobile phase, were compared for their effects on retention time, peak shape, and response of target compounds. The method was validated in terms of selectivity, linear range, detection limit, and precision. The established method was applied to 49 commercial samples of infant complementary fruit purees. Results Adjusting the sample pH using 1.5% formic acid water and incorporating a 2 mL methanol wash during purification achieved satisfactory recovery rates. The target compounds were chromatographically separated using an Agilent Poroshell 120 EC-C₁₈ column with a gradient elution system. The mobile phase consisted of methanol-water (methanol/water: 2/98, v/v) containing 5 mmol·L⁻¹ ammonium formate as mobile phase A, and methanol as mobile phase B. Good separation was achieved within 15 min, resulting in optimal chromatographic peak shapes. The 55 target compounds exhibited good linearity across the standard curve range, with correlation coefficients (R²) greater than 0.99. The method detection limits ranged from 0.02 to 0.05 µg·L⁻¹. In the plant- and animal-based fruit puree samples, the spiked recovery rates ranged from 60% to 112% and 57% to 119%, respectively, with relative standard deviations (RSD) ≤ 30%. A total of 9 traditional PFASs and 5 novel PFASs were positive in 49 samples of infant complementary fruit purees. Conclusion This method enables comprehensive detection of 55 traditional and emerging PFASs, offering wide coverage, high accuracy, and excellent sensitivity. It provides technical support for characterizing contamination by traditional and emerging PFASs in food matrices.

Objective: To understand the prevalence and related factors of depressive symptoms among primary and secondary school students in the in depth monitoring counties of China s Rural Compulsory Education Nutrition Improvement Program, so as to provide a basis for prevention and psychological intervention of depressive symptoms among children and adolescents in rural areas. Methods: In November 2022, a stratified random sampling method was adopted to collect height and weight data, basic personal and family information of 7 949 primary and secondary school students from grade three to grade nine through physical measurements and questionnaires in 56 key monitoring schools implementing the Student Nutrition Improvement Program in 7 in depth monitoring counties (Jalaid Banner in Inner Mongolia, Jinzhai County in Anhui, Mao Xian in Sichuan, Tiandeng County in Guangxi, Mian County in Shaanxi, Zhaozhou County in Heilongjiang and Youxi County in Fujian), and to obtain the information related to their depressive symptoms through the self assessment questionnaire on depression. Multivariate Logistic regression analysis was conducted to analyze the prevalence of depressive symptoms among primary and secondary school students, as well as their related factors. Results: The detection rate of depressive symptoms among primary and secondary school students in the in depth monitored counties was 23.5%. Logistic regression analysis showed that the probability of detecting depressive symptoms was higher among female students, middle school students, students whose video screen duration per day was >2 h, and students whose parents marital status was divorced or widowed ( OR =1.40, 1.64, 1.60, 1.24), and students whose sleep duration reached the recommended standard, whose parents usually accompanied them daily for time was 60-<120 min and ≥120 min, and students whose mothers literacy level was middle school graduation had lower probability of detecting depressive symptoms ( OR =0.85, 0.84, 0.71, 0.76) ( P < 0.05 ). Conclusion The detection rate of depressive symptoms among students in the in depth monitoring area is high, and targeted interventions need to be developed for students to reduce the risk of mental health problems.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.