To assess the association between the cardiometabolic index (CMI) and the risk of kidney stone prevalence, and to analyze its role across different population subgroups.

This cross-sectional study included health examination data from 76 624 adults. The association between CMI and the risk of kidney stones was evaluated using multivariable logistic regression models. Stratified analyses were conducted to explore the effect of different population characteristics. A piecewise linear regression model was used to assess the nonlinear relationship between CMI and kidney stone risk.

CMI levels were positively associated with the prevalence of kidney stones, with the prevalence increasing from 4.33% in the lowest tertile to 9.94% in the highest tertile (P<0.001). Even after adjusting for potential confounders, CMI remained significantly associated with an increased risk of kidney stones (OR = 1.15, 95% CI = 1.10-1.20, P<0.001). Stratified analyses showed that the effect of CMI on kidney stone risk was more pronounced in individuals aged ≥60 years (OR = 1.19, 95% CI = 1.08-1.32, P<0.001) and in males (OR = 1.16, 95% CI = 1.10-1.21, P<0.001), while no significant association was found in females (OR = 1.11, 95% CI = 0.96-1.28, P = 0.157). A nonlinear relationship was observed between CMI and kidney stone risk. Risk significantly increased when CMI was <0.73 (OR = 1.15, 95% CI = 1.10-1.20, P<0.001), while the risk plateaued for CMI ≥0.73 (OR = 1.09, 95% CI = 1.03-1.15, P = 0.002).

Elevated CMI is significantly associated with a higher risk of kidney stones, demonstrating a nonlinear threshold effect. As a comprehensive marker reflecting metabolic burden, CMI may be useful for screening and targeting interventions in high-risk populations for kidney stones.

To analyze the trends and characteristics of the number of patients, prevalence, incidence, disability-adjusted life years (DALY), and years lived with disability (YLD) of depressive disorders among the Chinese population from 1990 to 2021, with the hope of providing a theoretical basis for early prevention, intervention, and clinical decision-making regarding depressive disorders.

Depressive disorders data from the GBD 2021 database (Global Burden of Disease Study 2021 Data Resources) were extracted to analyze the number of patients, incidence, prevalence, DALY and YLD in the Chinese population. The software STATA 14.0 and Joinpoint Regression Program 4.8.0.1 were utilized to analyze the incidence, prevalence, DALY, and YLD of depressive disorders across different genders and age groups. Furthermore, the average annual percent change (AAPC) in depressive disorders was calculated.

In 2021, there were 42.3602 million new cases of depressive disorders in China, an increase of 38.9% from 30.4910 million in 1990. The number of patients was 53.1147 million, an increase of 54.0% from 34.4794 million in 1990. Both the standardized incidence and prevalence showed a slow downward trend (the AAPC was -0.57% and -0.44%, respectively, P<0.001). The incidence of depressive disorders in people aged 10 to 24 showed a sudden increase in different years, and the incidence of depressive disorders in people over 65 also showed an accelerating upward trend. The DALY in 1990 and 2021 were 5.4267 million and 7.8659 million person-years, respectively, with a cumulative increase of 44.9%. The DALY rate increased from 461.27 /100 000 to 552.87 /100 000, with an increase of 19.8%. The standardized DALY and YLD rates decreased slowly with each year (the AAPC was -0.53% and -0.53%, respectively, P<0.001).

Depressive disorder is still one of the main causes of global disease burden and a major public health issue facing our country. There is an urgent need to actively explore and implement effective prevention and treatment strategies to reduce the disease burden of depressive disorders.

To explore the correlation between complex multimorbidity and their influencing factors and to reveal the interactions between diseases and factors using network inference methods to identify high-risk populations.

Based on longitudinal data from 2016 to 2022 in the Urumqi public health surveillance database and electronic medical record information database, this study collected information on the occurrence of complex multimorbidity and related variables. The structure of the Bayesian network was learned using the maximum-minimum hill-climbing algorithm combined with prior knowledge, and parameter learning was conducted using Bayesian estimation. Directed acyclic graphs were employed to identify confounding factors and guide the construction of regression models.

A total of 6 938 participants were included in the study, of which 12.96% (899/ 6 938) developed complex multimorbidity over the seven-year period. After screening influencing factors, six predictors were selected for model construction, resulting in a model with 7 nodes and 10 directed edges. The results indicated that age, gender, source, and BMI weredirectly related to the occurrence of complex multimorbidity, all serving as parent nodes in the model. The results of logistic regression based on DAG guidelines showed that the risk of complex multimorbidity would increase by 8.70% [OR=1.087 (95%CI:1.077-1.098)] for each year of age increase in patients with chronic diseases; the OR of complex multimorbidity for rural residents compared to urban residents=0.274 (95%CI: 0.237-0.317); the OR of complex multimorbidity for obese people compared with thenormal weight people=1.019 (95%CI: 1.008-1.504).

Bayesian networks effectively identify the relationships between complex multimorbidity and influencing factors, as well as the interactions among these factors, thus enabling inference about the risk of complex multimorbidity occurrence. Prevention and control of complex multimorbidity requires attention to aging, urban environments, and obesity management.

Toconstruct a Genetic Algorithm optimized Support Vector Machine (GA-SVM) model based on multi-source data predicting acute respiratory infectious diseases and toevaluate its predictive effectiveness, providing a reference for establishing an early warning system for respiratory infectious diseases.

Symptom surveillance cases, meteorological and atmospheric pollution, data and stringency index obtained from 2020 to 2022 were used as modeling and forecasting samples, respectively. By picking up the optimum lagging week number of the potential predictive variables and filter out the most important variables successively, the independent variables were obtained. Then the full time series data were divided into validation set and training set in a 1:4 ratio. The parameters were optimized by genetic algorithm. We used the weekly number of new cases of respiratory infectious diseases as the dependent variable to structure the GA-SVM model. The performance was evaluated based on the following metrics: root mean square error (RMSE), meansabsolute percentage error (MAPE), predictive correlation coefficient (PCC) and R-squared (R²).

The most important variables were stringency index with 2-weeks-lag, symptom surveillance cases with 1-week-lag, maximum temperature with 1-week-lag, school activities with 2-weeks-lag and O₃ index with 1-week-lag. The GA-SVM model performed best when C=18.04, γ=0.175 4 while average RMSE=6.362, average MAPE=24.59%, average PCC=0.896 and average R²=0.804.

The model showsgood predictive performance for the reported cases of acute respiratory infectious diseases in Xuhui District, which confirms the feasibility of applying GA-SVM to multi-source data based on symptom monitoring for predicting respiratory infectious diseases, providing methodological references for the application of multi-source data in the early warning of infectious diseases.

To study the protective effect of melatonin on PM2.5-induced inflammation and lymphangiogenesis in the lung of ApoE^-/- mice.

Twenty-eight male ApoE^-/- mice were randomized into: control group, PM2.5 group, melatonin group and PM2.5+melatonin group. All mice were fed with western diet for 24 weeks. From the 25th week, mice in the melatonin group and PM2.5+melatonin group were daily orally gavage with melatonin (20 mg/kg·bw) for 8 weeks; mice in the PM2.5 group and PM2.5+melatonin group were exposed to PM2.5 by tracheal instillation (5mg/kg); and mice in the control group and melatonin group were instilled with saline at the same time. After 24 h of PM2.5 exposure, mice were euthanized and weight gain and lung weight/body weight ratio in four groups were analyzed. The concentrations of inflammatory cytokines (TNF-α and IL-6) in the lung tissue of mice were measured. Immunofluorescence staining of lung tissue was visualized the lymphatic marker LYVE1 expression. Western Blot was used to assess the protein expression levels of lymphangiogenesis markers PROX1 and LYVE1, lymphangiogenesis regulatory proteins VEGF-C and VEGFR-3 in lung tissues.

The levels of TNF-α and IL-6 and the protein expressions of PROX1, LYVE1, VEGF-C and VEGFR-3in lung tissues of PM2.5 group were significantly higher than the control group (P<0.05). Moreover, the levels of TNF-α, IL-6 and the protein expressions of PROX1, LYVE1, VEGF-C and VEGFR-3 in lung tissues of the PM2.5+melatonin group were significantly lower than the PM2.5 group (P<0.05).

Ambient PM2.5 exposure obviously increases lung inflammation of ApoE^-/- mice, and may increase lymphangiogenesis in lung through regulating the VEGF-C/VEGFR-3 signaling pathway in the lung tissues; melatonin markedly improves PM2.5-induced lung inflammation and reduces lymphangiogenesis in lung.

To explore the relationship between occupational physical activity and dyslipidemia among coal mine employees, providing reference for improving the prevention and control of dyslipidemia among coal mine employees and formulating health strategies.

This study was conducted in 2023 at Xishan coal electricity corporation occupational disease prevention and control institute. It was a cross-sectional study, with a total of 9 864 coal mine employees included in the analysis. We collected physical activity data using the International Physical Activity Questionnaire, and diagnosed dyslipidemia using the "Chinese Guidelines for the Prevention and Treatment of Adult Blood Lipid Disorders". We analyzed the relationship between occupational physical activity and dyslipidemia among coal mine employees using a multiple factor logistic regression model.

The proportion of low, medium, and high intensity occupational physical activities among coal mine employees were 38.42%, 35.75%, and 25.83%, respectively. There were intergroup differences (P<0.05) in the incidence of high TG and dyslipidemia among occupational physical activities of different intensities, but this difference was not observed in the incidence of high TC, low HDL, and high LDL (P>0.05). After adjusting for factors such as gender, age, and leisure time physical activity, the incidence of dyslipidemia was lower among coal miners engaged in moderate to high-intensity occupational physical activity compared to those engaged in low-intensity physical activity (OR=0.84, 95%CI:0.76-0.94; OR=0.77, 95%CI:0.69-0.87); The incidence of low HDL was also lower (OR=0.85, 95%CI:0.77-0.95; OR=0.83, 95%CI:0.73-0.93).

There is a negative correlation between occupational physical activity and dyslipidemia among coal mine employees. For coal mine workers engaged in low-intensity occupational physical activities, it is recommended to intervene in other modifiable behavioral risk factors related to dyslipidemia to maintain healthy blood lipid levels.

To explore the relationship between oral hygiene behavior and depression level in elderly people.

This article used the 2018 data from the Chinese Elderly Health Longevity Factors Survey (CLHLS) to select 10 148 eligible research subjects. Pearson correlation analysis was used to explore the correlation between oral hygiene behavior, dietary diversity, life satisfaction, and depression levels in the elderly. The Process4.1 plugin model in SPSS was used to analyze the mediating role of dietary diversity and life satisfaction between oral hygiene behavior and depression levels in the elderly.

The direct impact of oral hygiene behavior on depression levels in the elderly was significant (β=-0.206,P<0.01); Dietary diversity and life satisfaction significantly affected the level of depression in elderly people (β=-0.270, P<0.01); (β=-2.992,P<0.01); The mediating effects of dietary diversity and life satisfaction on the relationship between oral hygiene behavior and depression levels in elderly individuals were-0.069 (95%CI:-0.088 - -0.051) and -0.099 (95%CI:-0.145 - -0.055), respectively, with a chain mediation effect of -0.060 (95%CI:-0.071 - -0.049).

Dietary diversity and life satisfaction have a mediating effect between oral hygiene behavior and depression levels in the elderly, and there is a chain mediating effect between dietary diversity and life satisfaction and depression levels in the elderly.

To evaluate the associations between circulating monounsaturated fatty acids(cMUFAs) and cardiovascular-specific death using epidemiological methods.

This study included 36 254 patients with cardiovascular disease from the UK Biobank. Cox proportional hazards models were employed to examine the associations between cMUFAs and various fatalities, including cardiovascular death, ischaemic heart disease death, stroke death, cancer death, and all-cause death. Moreover, the study examined the dose-response relationship between cMUFAs and outcomes using quartiles, deciles, and restricted cubic splines. A subgroup analysis was conducted to validate the effects of cMUFAs on cardiovascular-specific deaths across different subgroups.

A positive correlation was observed between cMUFAs and all-cause death (HR=1.556, 95% CI: 1.419-1.705), as well as CVD (HR=1.504, 95% CI: 1.261-1.794), IHD (HR=1.572, 95% CI: 1.265-1.953), and cancer death (HR=1.291, 95% CI: 1.109-1.502). However, no significant association was found between cMUFAs and stroke death (P=0.582). The study revealed the existence of non-linear relationships between cMUFAs and cardiovascular-specific death. Subgroup analyses indicated that the association with all-cause death was more pronounced in females and current smokers. Furthermore, individuals aged over 50 years demonstrated a more pronounced correlation with IHD death.

In patients with cardiovascular disease, elevated levels of cMUFAs are associated with an increased risk of CVD death, IHD death, cancer death, and all-cause death. This association demonstrates a non-linear correlation trend.

To investigate the effect of tea intake on cognitive function in the elderly and the mediating role of relationship of impaired activities of daily living (ADLs) and depressive states.

The data for this study came from the 2018 China Longitudinal Health and Longevity Survey (CLHLS), which included a total of 8,818 valid samples. The logistic regression was used to analyze the effects of frequency and type of tea drinking on cognitive function in the elderly, and the possible mechanism of this effect was further explored using a chain multiple mediation model.

The elderly who regularly consumed tea were less likely to develop cognitive impairment (OR=0.80, 95% CI: 0.68-0.95), in which only green tea consumption was observed to be a protective factor for the development of cognitive impairment (OR=0.81, 95%CI: 0.65-1.00), and no significant evidence was found for other types of tea. Impaired activities of daily living and depressive state mediated the effect of regular tea consumption on cognitive functioning, with beta values and 95%CIs of -0.005 (-0.008--0.001) and -0.005(-0.007--0.003), while the continuous mediating effects of the two mediating variables, although significant, had β-values that were too small and effects that accounted for too little to be of practical significance.

Regular tea consumption is associated with a reduced risk of developing cognitive impairment in the elderly, especially among those who consume green tea. Impairment in activities of daily living and depression levels almost exclusively mediated the association between tea consumption and cognitive function.

To investigate the associations between leisure screen time and theinsomnia symptoms among adolescents.

A random cluster sampling method was used to assess leisure screen time among adolescents aged 12-18 attending schools in Pidu District, Chengdu. Participants reported their screen time on school days and weekends via self-administered questionnaires. Insomnia symptoms were collected using the Insomnia Severity Index (ISI). Adolescents were categorized into two groups based on their ISI scores: the non-insomnia group (ISI score <7) and the insomnia group (ISI score ≥7). Logistic regression models, adjusted for multiple confounders, were used to estimate the association between leisure screen time and the occurrence of insomnia symptoms.

A total of 13 240 adolescents participated, comprising 6 581 boys (49.7%) and 6 659 girls (50.3%), with a mean age of 15.4 years (±1.57). Of these, 51.8% reported an average daily leisure screen time exceeding 2 h, and the prevalence of insomnia symptoms was 35.3%. After adjusting for all confounding factors, logistic regression analysis indicated that leisure screen time >2 h was a significant risk factor for insomnia symptoms (OR=1.13, 95%CI=1.03-1.23). Further analysis by quartiles of leisure screen time revealed that adolescents in Q3 (OR=1.15, 95%CI=1.02-1.28) and Q4 (OR=1.22, 95%CI=1.08-1.39) had significantly increased risks of insomnia, whereas no significant association was observed in the Q2 group. A clear dose-response relationship was observed between leisure screen time and insomnia (P_trend<0.001). Subgroup analyses by sex, region, economic status, single child, and caretaker did not reveal significant interactions (P_interaction>0.05).

Prolonged leisure screen time significantly increases the risk of insomnia symptoms among adolescents. Parents and schools should encourage physical activity and reduce electronic device usage to promote their well-being.