Objective: To investigate the association between metabolic dysfunction associated steatotic liver disease (MASLD) and bone mineral density among children and adolescents, so as to provide evidence for the early prevention and intervention of bone health in this population. Methods: In September 2022, a method combining convenience sampling with cluster sampling was used to select 5 089 children and adolescents aged 6-18 years in 9 schools from kindergarten to senior high school in Tongzhou District, Beijing, for physical measurements, ultrasound measurements, blood biochemical index testing, and questionnaire surveys. Participants were categorized into three groups: the normal control group ( n =1 515), the metabolic abnormality group (MA, n = 3 007 ), and the MASLD group ( n =567). Multivariable linear regression model was applied to examine the association between MASLD and bone speed of sound (SOS), while multivariable Logistic regression model was used to assess the association between MASLD and low bone mineral density. Subgroup analysis was conducted by sex and age groups. Results: Compared with the normal control group, the MASLD group showed significantly lower SOS values ( β =-6.31, 95% CI =-9.63 to -2.99), lower SOS Z scores ( β = -0.21, 95% CI =-0.32 to -0.10), and higher susceptibility to low bone mineral density( OR =1.56, 95% CI =1.25-1.96)（all P <0.05）. No significant differences in SOS or odds of low bone density were observed between the MA and normal control groups (all P > 0.05). In sex stratified analyses, males with MASLD exhibited significantly lower SOS Z scores ( β =-0.35, 95% CI =-0.49 to -0.20 , P <0.05), whereas no significant difference was observed in females with MASLD ( β =-0.03, 95% CI =-0.21-0.15; P >0.05). When hepatic steatosis grade (0, 1, 2, and 3) was treated as a continuous variable, each one grade increase was associated with a 31% higher odds of low bone mineral density ( OR =1.31, 95% CI =1.13 to 1.53, P <0.05). Conclusions MASLD is significantly associated with low bone mineral density among children and adolescents, with a stronger association in males. Moreover, children and adolescents with hepatic steatosis have a higher risk of impaired bone health compared with those with metabolic abnormalities alone.

Objective: To investigate the association of the joint effect of body fat levels and dyslipidemia with cardiovascular structural abnormalities in children, so as to provide a scientific reference for the early prevention of cardiovascular damage. Methods: Based on the data from the second follow up (October 2021 to January 2022) of the Huantai Children Cardiovascular Health Follow up Cohort, 1 308 children with complete data were included. The fat mass percentage (FMP), fat mass index (FMI), subcutaneous fat mass (SFM) and visceral fat mass (VFM), left ventricular mass index (LVMI), relative wall thickness (RWT), thickening of carotid intima-media thickening (cIMT) , left ventricular hypertrophy (LVH) and left ventricular geometric remodeling (LVG), triglyceride (TG), total cholesterol (TC) ,high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) were obtained. Multivariable Logistic regression model was used to analyze the associations of FMP, FMI, SFM and VFM with thickening of cIMT, LVH and LVG. The joint effects of these body fat indicators and dyslipidemia on the aforementioned cardiovascular outcomes were further explored. Restricted cubic spline model was used to examine the dose response relationships between body fat levels and cardiovascular structural abnormalities. Results: Elevated body fat levels were significantly associated with an increased risk of cardiovascular structural abnormalities, exhibiting J shaped dose response relationships (all P <0.05). Compared with the group with normal body fat and normolipidemia, the risks of thickening of cIMT, LVH, and LVG in the group with elevated FMP combined with dyslipidemia were higher[ OR (95% CI )=11.70 (6.49-21.27), 5.53 (2.97-10.17), 2.33 (1.30-4.05)]; in the group with elevated FMI combined with dyslipidemia, the corresponding risks were higher[ OR (95% CI )= 11.68 (6.43-21.38), 6.98 (3.73-12.92), 2.65 (1.50-4.61)]; in the group with elevated SFM combined with dyslipidemia, the corresponding risks were higher[ OR (95% CI )=10.55 (5.83-19.24), 5.11 (2.71-9.45), 1.99 (1.11- 3.46 )]; and in the group with elevated VFM combined with dyslipidemia, the corresponding risks were higher[ OR (95% CI )=12.44 (6.76-23.14), 6.17 ( 3.31 -11.38), 2.30 (1.30-3.99)] (all P <0.05). Sex stratified analyses showed that the risk of thickening of cIMT in the combined exposure group of all four body fat indicators and dyslipidemia was significantly higher in girls than in boys (all P <0.01). Conclusions Elevated body fat levels and dyslipidemia have a combined effect in children, collectively increasing the risk of cardiovascular structural abnormalities. Prevention of cardiovascular damage in children should focus on both adiposity management and blood lipid regulation.

Objective: To investigate the association between sleep duration and depressive symptoms in children and adolescents, so as to provide scientific evidence for promoting mental health and preventing depression among relevant populations. Methods: A total of 2 192 children and adolescents aged 10-17 years with complete data from the 2018 China Family Panel Studies Database were included. Eight item Center for Epidemiologic Studies Depressive Scale(CES-D8) was used to assess participants depressive levels, and sleep duration was assessed via questionnaire. Multivariate Logistic regression model was used to analyze the association between different sleep duration categories and depressive symptom occurrence among children and adolescents. A restricted cubic spline(RCS) model analyzed the dose response relationship between sleep duration and the risk of depressive symptoms occurrence and segmented Logistic regression models to identify dose response effects. Results: Among the surveyed children and adolescents, 524(23.91%) exhibited depressive symptoms. Compared to those with sufficient sleep, children aged 10-12 years had a higher risk of depressive symptoms on average per day( OR =1.66, 95% CI =1.19-2.33) and during weekdays( OR =1.76, 95% CI =1.26-2.46), as well as in adolescents aged 13-17 years on a daily basis( OR =1.40，95% CI =1.06-1.85) and during weekdays( OR = 1.48，95% CI =1.12-1.95), and excessive sleep in adolescents on rest days was also significantly associated with higher risk of depressive symptoms( OR =1.65，95% CI =1.11-2.43)(all P <0.05). RCS analysis results indicate that children s sleep duration exhibits a linear negative correlation with the risk of depressive symptoms(all P nonlinear >0.05), while adolescents sleep duration showed a U shaped association with depressive symptoms(all P nonlinear <0.05) on a daily basis, during weekdays and weekends, with potential threshold effects at 10.00, 9.88, and 9.60 hours, respectively. Conclusions Sleep duration among children and adolescents is associated with depressive symptoms, with notable age related differeneces. It is recommended to develop targeted age specific interventions to reduce the risk of depressive symptoms in children and adolescents.

Objective: To explore the impact of adverse childhood experiences (ACEs) on health risk behaviors (HRBs) among college students and the mediating role of psychological symptoms, so as to provide a basis for developing intervention strategies. Methods: From March to April 2023, a convenience cluster sample of 1 801 students from 12 universities in Nanning, Liuzhou, Guilin, Wuzhou of Guangxi completed an online survey. A self designed questionnaire, Adverse Childhood Experiences-International Questionnaire (ACE-IQ) and Symptom Checklist-90 (SCL-90) were used for evaluation tools. Binary Logistic regression, structural equation modeling (SEM) and Bootstrap methods were used to analyze the associations and mediating effects. Results: Overall, 71.2% of college students experienced at least one type of ACE, with emotional neglect (40.3%) and emotional abuse ( 25.2 %) having the highest detection rates. The top three HRBs were unhealthy diet (77.8%), physical inactivity (54.1%), and smoking/alcohol use (18.5%). Logistic regression showed that poor family functioning, abuse, and extra familial violence were each associated with an increased risk of smoking/alcohol use ( OR =1.14, 1.11, 1.18) and deliberate self harm ( OR =1.26, 1.19,1.30) (all P <0.05). Experience of abuse increased the risk of high risk sexual behavior and family dysfunction increaded the risk of physical inactivity, respectively ( OR = 1.07 , 1.04, both P <0.05). Mediation analysis revealed that anxiety ( β =0.20) and depression ( β = 0.09 ) partially mediated the pathway from poor family functioning to deliberate self harm; paranoia ( β =0.02) partially mediated the pathway from abuse to high risk sexual behavior; and obsessive-compulsive symptoms ( β =0.26) and depression ( β =0.10) partially mediated the pathway from extra familial violence to deliberate self harm (all P <0.05). Conclusion Psychological symptoms play a mediating role in the association between ACEs and HRBs, and mental health interventions may reduce the risk of HRBs among college students.

Background: Visual impairment is increasingly prevalent, underscoring the need for effective prevention strategies. This study investigated the association between cardiovascular health and visual impairment using three progressively developed metrics: Life’s Simple 7 (LS7), a cardiovascular health score developed by the American Heart Association based on 7 components, including physical activity, smoking status, body mass index (BMI), diet, blood glucose, blood pressure, and total cholesterol; Life’s Essential 8(LE8), which adds sleep health as an additional component; and Life’s Crucial 9 (LC9), which further incorporates mental health. We aimed to evaluate whether these expanded metrics provide additional explanatory value and to inform future public health strategies. Objective: To investigate the associations among LS7, LE8, and LC9 cardiovascular health scores and visual impairment in National Health and Nutrition Examination Survey 2013 to 2018, assess the added value of sleep and mental health components, and identify key contributing subcomponents using regression and machine learning methods. Methods: We analyzed data from 9891 participants in the 2013 to 2018 National Health and Nutrition Examination Survey. Multivariable logistic regression, restricted cubic spline modeling, and subgroup analyses were conducted to evaluate the association between cardiovascular health and visual impairment. Machine learning methods, including Random Forest and SHapley Additive exPlanations, a model interpretation approach used to assess feature importance, were applied. The Boruta algorithm, a feature selection method, was used to identify key subcomponents of cardiovascular health. Results: Participants with visual impairment were older, had lower poverty-to-income ratios, and exhibited lower cardiovascular health scores (LS7, LE8, and LC9). Higher cardiovascular health scores were negatively associated with visual impairment. Each one standard deviation increase in LS7, LE8, and LC9 was associated with approximately 8%, 2%, and 3% lower odds of visual impairment, respectively. LS7 showed the strongest negative association, whereas LE8 and LC9 emerged as the most important predictors in the machine learning models. Key subcomponents such as blood glucose, blood pressure, and BMI, as well as mental health status, were strongly associated with visual impairment. Conclusion: Higher cardiovascular health scores were negatively associated with visual impairment. LS7 demonstrated the strongest statistical association, whereas LE8 and LC9 were the most influential features in the machine learning models. Blood glucose, blood pressure, BMI, and mental health status were important correlates of visual impairment. From the perspective of the “prevention and treatment of diseases” in traditional Chinese medicine and the holistic concept, the aforementioned research findings confirm that severe visual impairment is indeed an external manifestation of overall health imbalance (such as organ dysfunction and Qi deficiency and blood stasis). The prevention strategy lies in comprehensively adjusting lifestyle, emotional factors, and metabolic indicators (corresponding to the key factors in the study) to restore the body’s internal balance and achieve prevention before disease arises.

Objective: To investigate the prevalence and correlates of five somatic symptoms among junior and senior high school students, so as to provide a reference for the prevention of these symptoms in the population. Methods: From September to November 2024, a stratified cluster random sampling method was used to select 163 802 junior and senior high school students from 158 counties/districts in Henan Province. A questionnaire survey was conducted using the Student Health Status and Influencing Factors Survey (Secondary School Version) and the Special Survey on Influencing Factors of Key Common Diseases. Logistic regression was used to analyze the factors associated with each of the five somatic symptoms. Results: The reported prevalence of fever, cough, sore throat, diarrhea, and vomiting were 7.93%, 23.56%, 13.67%, 18.03%, and 3.17%, respectively. Girls reported (8.20%, 25.26%, 16.54%, 19.14%, 3.68%) higher rates than boys (7.67%, 21.97%, 11.00%, 16.99%, 2.69%) ( χ 2= 16.01, 245.78, 1 063.22, 128.89, 129.48, all P <0.01). Boarding students (8.95%, 25.79%, 15.77%, 20.77%, 3.45%) also had higher rates than nonboarding students (6.63%, 20.75%, 11.02%, 14.56%, 2.80%)( χ 2=298.37, 569.43, 772.23, 1 055.42 , 55.82, all P <0.01). Students from nonintact families reported higher rates (8.87%, 24.89%, 14.57%, 19.20%, 3.60% ) than those from intact families (7.58%, 23.07%, 13.34%, 17.60%, 3.01%) ( χ 2=72.48, 58.98, 41.61, 55.40, 36.00, all P <0.01). After adjusting for sociodemographic variables, multivariate Logistic regression showed that unhealthy dietary habits, daily homework or tutoring duration≥2 hours, daily screen time ≥2 hours, lack of parental encouragement for exercise, exposure to tobacco/alcohol, and substance abuse were associated with increased risk of reporting the five somatic symptoms ( OR =1.27- 1.63 , 1.21-1.27, 1.28-1.40, 1.07-1.13, 1.77-2.08, 1.76-2.17, all P <0.05). Conversely, handwashing habits, twice daily tooth brushing, daily breakfast consumption, engaging in at least 60 minutes of moderate to vigorous physical activity per day, spending outdoor time during breaks, and adequate sleep were associated with lower risk ( OR =0.82-0.90, 0.85 -0.92, 0.44- 0.76 , 0.83-0.93, 0.69-0.75, 0.65-0.78, all P <0.05). Conclusions Disparities exist in the prevalence of fever, cough, sore throat, diarrhea, and vomiting among secondary school students in Henan Province. The risk of experiencing these five somatic symptoms is closely linked to daily health behaviors. Comprehensive intervention strategies and scientific measures should be implemented to prevent somatic symptoms in junior and senior high school students.

BACKGROUND:Apoptosis plays an important role in secondary brain injury.Therefore,to explore the pathophysiological mechanism of promoting nerve cell survival after traumatic brain injury provides a new direction and theoretical basis for the prevention and treatment of traumatic brain injury. OBJECTIVE:To explore the expression changes of Lnx1 molecule in mammalian cortical neurons after brain injury and the possible mechanism involved in secondary brain injury. METHODS:Eighty adult SD rats were divided into 20 male and 20 female mice in sham operation group and 20 male and 20 female mice in traumatic brain injury group.The traumatic brain injury rat model was established by heavy falling method.At 6,12,24,48,and 72 hours after brain injury,the expression of related molecules in damaged cortical neurons was analyzed by RT-qPCR,western blot assay,and immunofluorescence staining. RESULTS AND CONCLUSION:(1)The brain tissue of traumatic brain injury group was bleeding and obvious tissue injury could be observed.Water content of brain tissue increased after traumatic brain injury.(2)Compared with the sham operation group,the expression of Lnx1 in cortical neurons after traumatic brain injury increased significantly at 24 hours after injury.(3)After traumatic brain injury,the expression of PBK and BCR protein decreased,and the pro-survival factor ctgf increased.(4)These findings suggest that after traumatic brain injury,the expression of Lnx1 is up-regulated in neurons,which may be due to the decrease of the expression of its target molecules PBK and BCR,and further promote the expression of living factor ctgf,which has a protective effect on the damaged neurons.

Knee osteoarthritis is a common joint disease within osteoarthritis,characterized by pain,swelling,and limited functionality as the main clinical manifestations.In severe cases,it affects daily life and falls under the category of"impediment syndrome"or"bone impediment"in TCM.The author believes that the theory of"bones,tendons,and muscles"is closely related to this disease.Treatment should focus on simultaneously nourishing the liver,spleen and kidneys,considering tendons,bones and muscles,while also dispelling wind,cold and dampness.The clinical application of Sanbi Decoction has shown good efficacy,and this discussion aimed to provide ideas for the diagnosis and treatment of knee osteoarthritis.

Objective To explore the key environmental factors influencing the distribution and spatially stratified heterogeneity of the suitable habitats of Ranunculus ternatus Thunb.;To predict its potential suitable habitats within China;To provide theoretical guidance for the conservation of its wild resources,artificial introduction and cultivation,and production planning.Methods Based on the MaxEnt model,a distribution prediction model was established using 148 selected species distribution information and 28 environmental factor data.The ENMeval package in R language was used to optimize the model parameters.Quantitative analysis(q-value ranking)of environmental factors affecting spatial differentiation in potential suitable areas using geographic detector factor detection and interaction detection.Results After optimization,the accuracy of model was improved and the degree of overfitting was reduced.The average value of the area under the curve(AUC)was 0.940.The contribution rate of environmental factors and the jackknife test showed that the key environmental factors influencing the distribution of Ranunculus ternatus Thunb.mainly included the precipitation in March,the precipitation in November,elevation,the solar radiation intensity in October,the solar radiation intensity in December,and the average temperature in January.The results of raster calculation showed that the area of highly suitable habitats of Ranunculus ternatus Thunb.was approximately 4.105×104 km2,the area of moderately suitable habitats was approximately 4.243×105 km2,and the area of lowly suitable habitats was approximately 6.385×105 km2,accounting for about 3.8%,38.4%and 57.8%of the total area of suitable habitats,respectively.Conclusion This study can provide a basis for the conservation and sustainabele utilization of the wild resources of Ranunculus ternatus Thunb.

Objective To explore the construction of a prediction model for hypertensive nephropathy(HN)based on machine learning.Methods A total of 318 hypertensive patients who visited Taizhou First People's Hospital from April 2023 to March 2024 were included and divided into a training set and a validation set at a ratio of 7:3.Least absolute shrinkage and selection operator(LASSO)algorithm was used to select clinical features from the training set,and 12 clinically significant variables were obtained from 18 clinical variables.Based on the Python 3.10 programming language,the training set was used to train the model.Taking the 12 clinically significant indicators were used as input variables,and whether the occurrence of HN was used as the outcome variable.Three machine learning algorithms,namely logistic regression,support vector machine,and artificial neural network,were used to construct prediction models.The test set was used for internal validation of three models.The performance of the models was compared through accuracy,area under the receiver operating characteristic curve,recall rate,precision,and F1.Results Among 12 clinically significant variables screened by the LASSO algorithm,cystatin C and urine protein qualitative were found to be the most predictive.The accuracy,area under the receiver operating characteristic curve,recall rate,precision,and F1 values of the Logistic regression,support vector machine,and artificial neural network prediction models constructed by machine learning was 0.94,0.96,0.95,0.87,0.91;0.94,0.97,0.96,0.86,0.91;0.91,0.94,0.93,0.80,0.86,respectively.Conclusion Logistic regression,support vector machine,and artificial neural network based on machine learning all have good predictive effects on the progression of hypertensive patients to HN.Among them,the predictive effects of Logistic regression and support vector machine are similar and better than artificial neural network prediction model.