To construct a prediction model for metabolic syndrome (Metabolic Syndrome, MetS) in railway employees based on machine learning algorithms (Machine Learning, ML) and evaluate the prediction performance.

The time to the onset of metabolic syndrome was used as the outcome variable, with demographic characteristics and biochemical indicators as predictive variables. Univariate analysis was conducted to select predictive indicators. The study subjects were randomly divided into a training set and a test set in a 7:3 ratio. Cox proportional hazards regression, Random Forest (Random Survival Forest, RSF), and Gradient Boosting Machine (Gradient Boosting Machine, GBM) were used to build metabolic syndrome prediction models. Model performance was assessed using the area under the receiver operating characteristic curve (Area under curve, AUC), concordance index (C-index), sensitivity, specificity, accuracy, and F1 score. A risk calculator was created using the shiny package.

This study included 17 087 subjects and collected 28 indicators. Univariate analysis identified 22 statistically significant indicators. In the training set, the areas under the curve (area under the curve, AUC) of the prediction models constructed by Cox, RSF, and GBM were 0.870,0.938, and 0.891, respectively; C-index values were 0.853,0.935, and 0.843; sensitivity was 0.612,0.968, and 0.628; specificity was 0.933,0.742, and 0.994; accuracy was 0.678,0.788, and 0.703; F1 scores were 0.751,0.839, and 0.749.

The RSF model outperformed the Cox model and the GBM model in predicting metabolic syndrome among railway employees, providing a scientific basis for early identification of metabolic syndrome and aiding in the implementation of primary prevention measures.

To construct a machine learning model based on Insulin-like Growth Factor-1 (IGF-1) and Growth Differentiation Factor-8 (GDF-8, Myostatin) for predicting sarcopenia in lung cancer patients, with the aim of improving early detection and diagnostic capabilities, providing personalized nutrition and treatment recommendations, and enhancing patients’ health status and prognosis.

A total of 263 primary lung cancer patients hospitalized at Karamay Central Hospital between October 2023 and July 2024 were selected as research subjects. Data on gender, age, BMI, IGF-1, GDF-8, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and other variables were collected. Patients were divided into a sarcopenia group and a non-sarcopenia group according to the criteria established by the Asian Working Group for Sarcopenia (AWGSOP). Univariate analysis and LASSO regression analysis were used to identify risk factors for sarcopenia in primary lung cancer patients. The selected risk factors were incorporated into the K-nearest neighbors (KNN) algorithm model, Gaussian Naive Bayes (GNB), and binary logistic regression models, using the R software. Internal validation was performed using the Bootstrap method.

A total of 263 patients were investigated, with 137 in the sarcopenia group and 126 in the non-sarcopenia group. The sarcopenia group had significantly higher proportions of alcohol consumption, IL-4, IL-6, IL-17, TNF-α, GDF-8, blood urea nitrogen, and low-density lipoprotein, while BMI, smoking, PSQI score, IGF-1, and platelet count were significantly lower than those in the non-sarcopenia group (all P<0.05). The ROC curve showed that the C-index of the KNN model was 0.936, the C-index of the GNB model was 0.935, both significantly better than the binary logistic regression model’s C-index of 0.926. The Hosmer-Lemeshow goodness-of-fit test showed that the average prediction error between the predicted and actual values of the KNN model, GNB model, and binary logistic regression model were 0.026 9, 0.018 8, and 0.028 7, respectively, with the GNB model significantly outperforming the KNN model and the binary logistic regression model. The prediction results of the GNB model were highly consistent with the observed outcomes. DCA curves demonstrated that the GNB model outperformed both the KNN model and the binary logistic regression model in predicting sarcopenia risk in lung cancer patients.

High levels of IL-6, TNF-α, and GDF-8, low levels of IGF-1 and albumin, poor sleep quality, and low BMI are independent risk factors for sarcopenia in lung cancer patients. The GNB prediction model constructed in this study significantly outperforms both the KNN model and the binary logistic regression model, providing precise and individualized predictions for sarcopenia risk in lung cancer patients. This model can offer personalized nutrition and treatment recommendations for clinical practice, improving patients’ health status and prognosis.

To analyze the recurrence status and influencing factors of initially treated elderly tuberculosis patients after successful treatment in Kashgar region, and to provide a basis for preventing and controlling tuberculosis recurrence.

A total of 40 527 Patients aged 60 and above who were initially treated for tuberculosis and successfully treated in Kashgar from 2016 to 2022 were selected as the study subjects. Their recurrence rate up to the end of 2023 was analyzed. The Kaplan-Meier method was used for univariate analysis of recurrence influencing factors, the log-rank test was used for inter-group comparison, and the Cox proportional hazards regression model was used for multivariate analysis. Results Among the 40 527 study participants, 6 358 cases of recurrence were recorded by the end of 2023, with a total cumulative recurrence rate of 19.17%. The Cox proportional hazards regression model showed that male gender (aHR=1.176, 95%CI: 1.119-1.235), severe tuberculosis burden in the current residence (aHR=1.354, 95%CI: 1.234-1.486), positive sputum test result at initial diagnosis (aHR=1.831, 95%CI: 1.622-2.068), positive sputum test result at the end of the second month of initial treatment (aHR=2.230, 95%CI: 1.736-2.865), positive sputum culture result at initial diagnosis (aHR=1.533, 95%CI: 1.346-1.745), and a diagnosis and treatment delay of 30 days or more (aHR=1.126, 95%CI: 1.057-1.198) were independent risk factors for recurrence in initially treated elderly tuberculosis patients. In contrast, active case-finding (aHR=0.635, 95%CI: 0.600-0.672), a cured treatment outcome (aHR=0.655, 95%CI: 0.580-0.739), and puretuberculous pleurisy (aHR=0.206, 95%CI: 0.098-0.432) were protective factors against recurrence.

To reduce the recurrence of initially treated elderly tuberculosis, it is necessary to strengthen follow-up and management after treatment, implement targeted interventions for high-risk groups, and enhance public health education to promote early detection and standardized treatment.

To explore the impact of grip strength asymmetry on cognitive function among elderly people in Chengdu, to investigate the mediating role of activities of daily living (ADL) between grip strength asymmetry and cognitive function, and to provide scientific evidence for reducing the risk of cognitive decline.

This study analyzed survey data from Wenjiang District and Longquanyi District in Chengdu in 2023. Handgrip strength asymmetry was calculated from bilateral grip strength measurements, and cognitive function was assessed using the Minimum Mental State Examination (MMSE). Correlation analysis and regression analysis were used to explore the relationship between handgrip strength asymmetry, ADL and cognitive function in the elderly. Bootstrap tested the mediating role of ADL between handgrip strength asymmetry and cognitive function.

The study included 1 110 participants, including both urban and rural residents of Chengdu, with an average age of 72.38±5.82 years, including 660 (59.5%) women. The handgrip strength asymmetry was negatively correlated with cognitive function and ADL (r=-0.121, P<0.001; r=-0.138, P<0.001), ADL was positively correlated with cognitive function (r=0.125, P<0.001). The results of mediating effect test showed that ADL played a partial mediating role between the handgrip strength asymmetry and cognitive function (P<0.05), and the mediating effect accounted for 12.9%.

Our findings suggest that grip strength asymmetry can lead to impairment in activities of daily living, which in turn causes cognitive decline among the elderly population in Chengdu. Thus, ADL plays a partial mediating role between handgrip strength asymmetry and cognitive function. Physical exercise targeting grip strength and ADL may be an effective approach to preventing and delaying cognitive decline.

To investigate the independent and joint effects of long-term exposure to air pollutants on the metabolic syndrome risk in adults.

Data were obtained from the surveillance and investigation of chronic diseases and risk factors in Hubei Province from 2018 to 2020. The daily average concentrations of PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO at 1-kilometre spatial resolution were matched according to participants’ residential addresses, and the average levels of these air pollutants over the 1-year period before the survey conducted were calculated to assign individual-level exposure. Multilevel logistic regression and weighted quantile sum regression models were applied to evaluate the independent and joint effects of air pollutant exposure on the risk for metabolic syndrome, respectively.

A total of 24 322 adults were included in the final analysis, with a metabolic syndrome prevalence of 39.8%. Higher exposure to PM_2.5(OR=1.011, 95%CI:1.001-1.020) and O₃ (OR=1.012, 95%CI: 1.005-1.018) were associated with an increased risk for metabolic syndrome. Subgroup analyses showed that exposure to PM_2.5 was positively associated with risk for metabolic syndrome among rural residents (OR=1.018, 95%CI: 1.006-1.030), and the positive association between exposure to O₃ and metabolic syndrome were found in adults beyond 60 years (OR=1.015, 95%CI: 1.008-1.023) and those physically active (OR=1.015, 95%CI: 1.008-1.022). The WQS regression revealed a positive association between long-term exposure to air pollutant mixtures and the metabolic syndrome risk (OR=1.188, 95%CI: 1.129-1.251), with O₃, CO, and PM_2.5 contributing 44.3%, 34.8%, and 20.8% to the joint effect, respectively.

Both long-term individual and joint exposures to air pollutants are positively associated with metabolic syndrome risk in adults, with the joint exposure showing a stronger adverse effect primarily driven by O₃, CO, and PM_2.5. To mitigate metabolic syndrome risk in adults, comprehensive prevention and control strategies should be implemented to reduce exposure to air pollutants.

To explore the influencing factors of occupational stress and their interrelationships among frontline workers in the secondary industry, so as to provide a reference basis for targeted intervention of occupational stress.

1 227 frontline workers in the secondary industry of Nanchong City were selected as study subjects by stratified random sampling. The Core Occupational stress and the Occupational Health Literacy Questionnaire of National Key Population were used to investigate occupational stress and occupational health literacy. SPSS 27.0 and R 4.1.2 were used to screen the influencing factors, Netica 7.01 constructed a Bayesian network model with inference and sensitivity analyses, and the area under the curve (AUC) assessed the model fit.

370(30.2%) were found to have occupational stress among frontline workers in the secondary industry in Nanchong City. LASSO regression analysis screened out 12 influencing factors, which were gender, marital status, health status, age, education, monthly income, exercise, occupational health literacy, enterprise size, nature of the enterprise, weekly working hours, and night shifts. The occupational stress Bayesian network model fit well (AUC=0.865), and backward inference yielded six key influences, ranked by sensitivity analysis: monthly income (57.9%), weekly working hours (55.4%), health status (52.6%), occupational health literacy (33.4%), enterprise size (20.4%), and exercise (15.5%).

The Bayesian network model constructed in this study has good prediction performance for occupational stress of frontline workers in secondary industry. It should focus on helping small and micro-enterprises, pay attention to low-income and poor health workers, and suggest rationalizing working hours, improving occupational health literacy, and strengthening physical exercise to prevent and control the occurrence of occupational stress.

To investigate the impact of chemical components in multi-size particulate matter on mortality risk from circulatory system diseases, and to provide evidence for establishing refined air pollution control strategies.

Based on mortality data for circulatory system diseases in Tianjin from 2019 to 2022, integrated with multi-size particulate matter component profiles and meteorological parameters, a generalized linear regression model based on quasi-Poisson distribution was constructed to quantitatively assess the differential contributions of particulate matter components across size ranges to mortality risk.

During the study period, 37 416 circulatory system disease deaths were recorded, with a weekly average mortality of 393.85±93.70 cases. The health effects of particulate matter were most pronounced in the 1.1- 2.1 μm size range, where a 1 interquartile range (IQR) increase was associated with a 6.83% (95%CI: 1.25%-12.72%) elevation in circulatory mortality risk. Component-specific analyses identified chloride ions, potassium, chromium, titanium, and cadmium as significantly correlated with circulatory mortality. Smaller particle sizes exhibited greater toxicity, with heightened sensitivity observed in females and individuals over 65 years old.

Particulate matter health effects demonstrate significant size-dependent characteristics, with diminishing particle sizes correlating to increased toxic complexity. Targeted control strategies are recommended, prioritizing metal components and secondary inorganic ions in particles below 2.5μm.

To analyze the epidemiological characteristics and incidence trend of respiratory infectious diseases in children aged 0-14 years in Lanzhou City, and provide reference for scientific prevention and control of children’s infectious diseases.

The surveillance data of respiratory infectious diseases in children aged 0-14 years in Lanzhou from 2014 to 2023 were collected through the China Disease Control and Prevention Information System. Descriptive epidemiological methods and Joinpoint regression model were used to analyze the epidemiological characteristics and incidence trends of respiratory infectious diseases.

A total of 40 347 cases of 9 kinds of respiratory infectious diseases in children aged 0-14 years were reported in Lanzhou from 2014 to 2023, resulting in an average annual reported incidence rate of 743.60 per 100 000. The overall reported incidence rate showed an upward trend (APC=AAPC=14.576 6%, P<0.05).The incidence rate of the disease among male children was 811.70 /100 000, while the incidence rate among female children was 656.74 /100 000, indicating that the rate in male children was significantly higher (χ²=443.64, P<0.001). Among different populations, the number of reported cases was highest among students, totaling 21 043 cases (accounting for 52.40%). The incidence of respiratory infectious diseases had a seasonal pattern in children aged 0-14 years in Lanzhou. The peak of incidence was from November to December (12 031 cases, accounting for 30.2%), followed by May to June (8 113 cases, accounting for 20.36%) and March (4 556 cases, accounting for 11.46%). Except for COVID-19 infection, the top five reported cases were chickenpox (298.94/100 000), influenza (261.34/100 000), mumps (93.99/100 000), scarlet fever (70.7/100 000) and measles (5.99/100 000). The influenza (11 897 cases, accounting for 38.80%) ranked the first in urban area, and chickenpox (4 964 cases, accounting for 54.90%) ranked the first in county (district), the difference was statistically significant (χ²=1 034.46, P<0.05).

The overall incidence of respiratory infectious diseases showed an upward trend, and the incidence had obvious seasonal characteristics in children aged 0-14 years in Lanzhou from 2014 to 2023. Chickenpox and influenza were the main respiratory infectious diseases. Monitoring of pediatric respiratory infectious diseases was emphasized, with a focus on key populations and regions. It is necessary to strengthen the surveillance of respiratory infectious diseases in children, pay attention to key populations and key areas, and effectively control the spread of respiratory infectious diseases in children.

To assess the relationship between the Life’s Crucial 9 (LC9) and stress urinary incontinence (SUI).

Data based on 4 550 participants from the National Health and Nutrition Examination Survey (NHANES) database from 2007 to 2018 were analyzed. Subjects were categorized into T1, T2, and T3 groups based on LC9 tertiles. Logistic regression analysis was used to explore the relationship between LC9 and SUI with parallel trend analysis. Stratified Logistic regression was used to analyze the correlation between LC9 and SUI in different groups. Restricted cubic spline was used to explore the quantitative relationship between LC9 and SUI.

All three models showed a negative correlation between LC9 and SUI incidence (all P<0.001). After adjusting for various covariates, each 10-point increase in LC9 was associated with a 16% reduction in SUI incidence (OR=0.84, 95% CI: 0.79-0.88). T3 was associated with a 40% reduction in SUI incidence compared with T1 (OR=0.60, 95% CI: 0.50-0.72). The results of restricted cubic spline curve (RCS) analysis further showed a significant linear negative association between LC9 and SUI incidence after adjustment for relevant variables (overall P<0.001; nonlinear P=0.261). There was a significant interaction between LC9 score and history of vaginal delivery (P<0.05). Higher LC9 scores in patients with SUI (n=2 047) were significantly associated with mortality from cardiovascular events in SUI (HR=0.10, 95% CI: 0.01-0.79).

LC9 is negatively associated with morbidity and mortality from SUI, and good quality of life management may be a potential strategy to reduce SUI incidence and mortality.

To analyze the vaccination status of hepatitis B vaccine (HepB) among children born to HBsAg positive mothers in Qingdao City, to investigate factors influencing the vaccination timeliness of HepB.

Vaccination data of HepB among children born to HBsAg positive mothers in Qingdao City from 2020 to 2022 was obtained from Shandong immunization information systems. The vaccination rates and timely vaccination rates of HepB were analyzed, and multivariate logistic regression was used to identify factors influencing vaccination timeliness.

A total of 6 232 children born to HBsAg positive mothers were included. The vaccination rate of first dose of HepB (HepB₁) was 100%, and the timely vaccination rate (within 12 hours of birth) of HepB₁ was 84.88%. The full-course vaccination rate of HepB was 99.73%, and the timely full-course vaccination rate (within six months of birth) was 73.38%. Multivariate logistic regression showed that born in 2021 (OR=1.51, 95%CI: 1.26-1.80), born in 2022 (OR=4.92, 95%CI: 3.91-6.20), born in county-level hospitals (OR=1.65, 95%CI: 1.38-1.96) and private hospitals (OR=2.57, 95%CI: 1.88-3.50), birth weight ≥2.5kg (OR=2.89, 95%CI: 1.88-4.44), gestational age at birth ≥37 weeks (OR=1.83, 95%CI: 1.28-2.61), birth time not between 3 PM and 9 PM (OR=12.63, 95%CI: 10.69-14.95) were promoting factors for the vaccination timeliness of HepB₁; Female (OR=1.13, 95%CI: 1.01-1.26) was a promoting factor for the vaccination timeliness of full-course HepB; Gestational age at birth ≥37 weeks (OR=0.73, 95%CI: 0.57-0.95), second birth (OR=0.77, 95%CI: 0.68-0.86), third birth and above (OR=0.57, 95%CI: 0.46-0.70) were risk factors for the vaccination timeliness of full-course HepB.

The vaccination rates of HepB₁ and full-course HepB among children born to HBsAg positive mothers in Qingdao City were maintained at a high level, but the timely vaccination rates were relatively low. We should take targeted measures to improve the vaccination timeliness of HepB.