Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

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 investigate the relationship between Helicobacter pylori (Hp) infection and disease severity and pathological type of intestinal polyps in inpatients. Methods The data of 303 inpatients with intestinal polyps in the hospital from August 2022 to February 2025 were collected and analyzed. The clinical characteristics of patients were analyzed, and the influencing factors of pathological types of polyps were explored. Results Among the 303 inpatients with intestinal polyps, there were 135 Hp positive cases and 168 Hp negative cases. The number of polyps, maximum polyp diameter, number of ileocecus/ascending colon/transverse colon polyps, number of descending colon/sigmoid colon/rectal polyps and adenomatous polyps in the Hp-positive group were higher than those in the Hp-negative group (P<0.05). Multivariate logistic regression analysis indicated that age [OR (95%CI)=1.03 (1.01-1.05)] and positive Hp[OR (95%CI)=2.61 (1.62-4.20)] were independent risk factors of occurrence of adenomatous polyps (P<0.05). ROC curve results revealed that the AUCs of age, positive HP and combination in the diagnosis of adenomatous polyps were 0.574, 0.608 and 0.646, and the 95%CI values were 0.509-0.638, 0.545-0.672 and 0.584-0.708 respectively. The efficiency of combination of the two indexes was higher than that of single diagnosis. Conclusion Hp infection is associated with disease severity in inpatients with intestinal polyps, and it may be involved in the occurrence and development of adenomatous polyps.

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.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

OBJECTIVE: This study aimed to evaluate the association between susceptibility genes and non-alcoholic fatty liver disease (NAFLD) in children with obesity. METHODS: We conducted a two-step case-control study. Ninety-three participants were subjected to whole-exome sequencing (exploratory set). Differential genes identified in the small sample were validated in 1,022 participants using multiplex polymerase chain reaction and high-throughput sequencing (validation set). RESULTS: In the exploratory set, 14 genes from the NAFLD-associated pathways were identified. In the validation set, after adjusting for sex, age, and body mass index, ECI2 rs2326408 (dominant model: OR = 1.33, 95% CI: 1.02-1.72; additive model: OR = 1.22, 95% CI: 1.01-1.47), C6orf201 rs659305 (dominant model: OR = 1.30, 95% CI: 1.01-1.69; additive model: OR = 1.21, 95% CI: 1.00-1.45), CALML5 rs10904516 (pre-ad dominant model: OR = 1.36, 95% CI: 1.01-1.83; adjusted dominant model: OR = 1.40, 95% CI: 1.03-1.91; and pre-ad additive model: OR = 1.26, 95% CI: 1.04-1.66) polymorphisms were significantly associated with NAFLD in children with obesity ( P < 0.05). Interaction analysis revealed that the gene-gene interaction model of CALML5 rs10904516, COX11 rs17209882, and SCD5 rs3733228 was optional ( P < 0.05), demonstrating a negative interaction between the three genes. CONCLUSION In the Chinese population, the CALML5 rs10904516, C6orf201 rs659305, and ECI2 rs2326408 variants could be genetic markers for NAFLD susceptibility.
Humans ; Non-alcoholic Fatty Liver Disease/genetics* ; Child ; Male ; Female ; Genetic Predisposition to Disease ; Case-Control Studies ; Exome Sequencing ; Adolescent ; Polymorphism, Single Nucleotide ; Obesity/complications* ; Pediatric Obesity/complications* ; China

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.Methods Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected,including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital.After data augmentation,a total of 60 275 images were obtained.The DAEUnet esophageal cancer intelligent segmentation network was built,and on this basis,3 classification networks,ConvNeXt,Swin Transformer,and ResNet were constructed for T-stage diagnosis of esophageal cancer.Results The Dice similarity coefficient(DSC)for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82,and the DSC value of the esophagus,aorta,normal esophagus,mediastinal lymph nodes,and heart was 72.4％,87.5％,79.3％,60.5％ and 96.8％,respectively.Among the 3 T-stage diagnosis models for esophageal cancer,the ConvNeXt model performed the best,with a precision value for T1～T4 stages of 0.65,0.727,0.889 and 0.92,respectively,and an AUC value of 0.892,which were superior to the ResNet and Swin Transformer networks.Conclusion The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.