The aim of this study was to investigate the association between relative fat mass (RFM) and the risk of arthritis in middle-aged and elderly Chinese adults.

A cross-sectional analysis was performed using 2011 CHARLS data and included a total of 12 921 participants, including 6 144 males (47.55%) and 6 777 females (52.45%), with a mean age of (59.49 ± 9.60) years. Multivariable logistic regression and restricted cubic splines (RCS) were used to analyze the cross-sectional association between RFM and arthritis. A total of 8 350 middle-aged and elderly people who were not diagnosed with arthritis in the 2011 cross-sectional study were included in the longitudinal study and followed up until 2018. Kaplan-Meier curves, multivariable Cox proportional hazards regression models, and RCS were used to analyze the longitudinal association between different baseline RFM levels and the risk of incident arthritis in 2011. Meanwhile, the consistency of this association in different population subgroups was assessed by subgroup analysis, and sensitivity analysis was used to verify the stability of the model results.

In the cross-sectional study, multivariable logistic regression analysis showed that higher RFM was associated with an increased risk of arthritis after adjusting for confounders (OR=1.02, 95% CI: 1.02-1.03). Middle-aged and older adults in the Q3 (OR=1.30, 95% CI: 1.15-1.48) and Q4 (OR=1.60, 95% CI: 1.40-1.82) groups had a higher risk of arthritis compared with the first quartile (Q1). In the longitudinal study, a total of 916 individuals were first diagnosed with arthritis during the follow-up period. Multivariable Cox regression analysis showed that higher RFM was associated with an increased risk of incident arthritis after adjusting for confounders (HR=1.02, 95% CI: 1.02-1.04). The risk of incident arthritis was significantly higher in the Q4 group(HR=1.56, 95% CI:1.23-1.98) compared with Q1. Subgroup analysis showed no significant interactions between RFM and each subgroup variable. The results of the sensitivity analysis were generally consistent with the conclusions of the primary analysis. ROC curves showed that RFM had a better diagnostic efficacy for arthritis than obesity indicators such as BMI.

Higher levels of RFM are associated with an increased risk of arthritis in the middle-aged and elderly Chinese population, paying attention to RFM levels may help in the early identification of populations at risk for arthritis, providing new insights for disease prevention and intervention.

The purpose is to analyze the impact of sedentary behavior and depression and their interaction on the risk of all-cause and cardiovascular mortality in the general population.

The study included adults recruited from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018, with follow-up until December 31, 2019. Cox proportional hazards regression models were used to assess the associations between sedentary behavior, depression, and their interactions with the risks of all-cause and cardiovascular mortality.

A total of 25 836 adults were included, with a median follow-up of 77.0 (44.0-117.0) months. There were 2 312 all-cause deaths, including 688 cardiovascular deaths. After multivariate adjustment, sedentary behavior was associated with increased risks of all-cause mortality (HR=1.3, 95% CI: 1.1-1.4) and cardiovascular mortality (HR=1.4, 95% CI: 1.2-1.7). Depression was also associated with increased risks of all-cause mortality (HR=1.3, 95% CI: 1.1-1.4) and cardiovascular mortality (HR=1.2, 95% CI: 1.0-1.5). Individuals who were both sedentary and depressed had the highest risks of all-cause mortality (HR=1.6, 95% CI: 1.3-1.8) and cardiovascular mortality (HR=1.7, 95% CI: 1.3-2.3). An additive interaction between sedentary behavior and depression was observed for cardiovascular mortality.

Sedentary behavior and depression are independently associated with increased mortality risks, and there is an additive interaction between these factors.

To describe the spatio-temporal distribution of the incidence of colorectal cancer(CRC) in China and provide epidemiological evidence for prevention and treatment.

Based on the data of CRC incidence in the Chinese Cancer Registry Annual Report from 2008 to 2021, the temporal trend was analyzed by the Joinpoint regression model, the spatial autocorrelation analysis was conducted to examine spatial distribution patterns, and the influencing factors were analyzed by the age-period-cohort model.

From 2005 to 2018, the age standardized incidence rate (ASIR) of CRC in China was 23.77/100 000, showing a downward trend (AAPC=-1.1%). The ASIR of males and urban areas was higher than that of females and rural areas. The incidence of CRC exhibited a spatial clustering pattern in the eastern region. The eastern region was identified as a high-risk hotspot area. The age effect indicated that the risk of developing CRC increased with age. Time effect showed that the incidence of CRC in China was on the decline, but the incidence of CRC in rural areas was on the rise. In terms of the cohort effect, the population born between 1925 and 1929 had the highest risk of developing CRC.

From 2005 to 2018, the ASIR of CRC had decreased, but there were population and regional differences. The eastern region had been identified as a hotspot area.

To explore the application of the advanced LSTM-Informer model in predicting and early warning of pulmonary tuberculosis incidence, providing a reference for relevant authorities to formulate prevention and control measures, and offering new methodologies and insights for forecasting other infectious diseases.

Pulmonary tuberculosis incidence data for Jining City from January 1, 2010, to December 31, 2022, were obtained from the National Notifiable Disease Reporting System (NNDRS). Monthly incidence rates were calculated by integrating demographic data from the Jining Statistical Yearbook (2010—2022). The final 12, 18, and 24 months of data were used as test sets to evaluate model performance, with the remaining data constituting the training set. The LSTM-Informer model was constructed and its reliability assessed through comparison with standalone LSTM and Informer models.

The predictive performance of the three models across different forecast horizons was evaluated using error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination(R²). Results demonstrated that the LSTM-Informer model consistently outperformed the single models. In the prediction of the 12-month length, the MSE of the model was 0.028, which was 59.4% lower than that of LSTM, the MAE value of 0.155 was 34.0% lower than that of LSTM, and the R² value was 0.850. When the forecast horizon extended to 18 months, the model maintained a stable MAPE of 0.104 and an R² of 0.808. Even at the 24-month horizon, the LSTM-Informer model retained significant advantages(R²=0.782).

Compared to single models, the LSTM-Informer model, leveraging both local and global feature extraction, significantly enhances prediction accuracy for medium-to-long-term forecasts. It shows considerable promise for providing novel theoretical support and reference for developing pulmonary tuberculosis incidence early-warning systems locally.

The clinical symptoms and ultrasound images of gallstone disease (GSD) and gallbladder polyps (GP) overlap, yet they have distinct prognoses and management strategies. Misdiagnosis can lead to excessive or delayed intervention. This study aims to develop and validate a differential diagnostic model based on multidimensional clinical indicators to enhance the differentiation between GSD and GP.

We included 371 518 individuals from health check-ups at a hospital in Kaizhou District, Chongqing, from 2015 to 2020, selecting 24 307 GSD or GP patients. These were split into a training set (19 445 cases) and an internal validation set (4 862 cases) at an 8:2 ratio. Variables were selected using Lasso regression and XGBoost algorithm, and a diagnostic model was built with Logistic regression. The model’s calibration, discrimination, and clinical applicability were evaluated using the calibration curve, the area under the ROC curve (AUC), and decision curve analysis, respectively.For external validation, 14 341 GSD and GP patients undergoing health checkups during the same period at a hospital in the Liangjiang New Area, Chongqing, were included to assess the model’s discriminative performance.

Women, older individuals, those with high BMI, central obesity, elevated GGT and ALT levels, high blood glucose levels, and fatty liver were more likely to be diagnosed with GSD. Individuals with high HDL-C and LDL-C levels were more likely to have GP. The model demonstrated good calibration and discrimination, with an ROC AUC of 69.4% (95% CI: 67.8%-70.9%). Decision curve analysis further indicated that the model had good clinical applicability, and external validation further confirmed its discriminatory performance. An online tool (https://yitingxu129.shinyapps.io/logisticmodel/) was created to aid in differentiating GSD and GP.

This study shows that gender, age, BMI, and blood biochemistry indicators are valuable in differentiating GSD and GP.The developed model has good predictive performance and clinical applicability and is expected to assist radiologists in making accurate differential diagnoses of GSD and GP, reduce misdiagnoses, and lower the disease burden.

To investigate the relationship between serum perfluorinated and polyfluoroalkyl substances (PFAS) exposure and metabolic abnormalities in overweight and obese children and adolescents.

This study was a cross-sectional design. The study subjects were overweight and obese children and adolescents aged 5-17 years who visited the Department of Child Health Care at the Children’s Hospital Affiliated to Nanjing Medical University from July to October 2022. Logistic regression analysis was used to investigate the association between PFAS exposure and high blood glucose, high triglyceride, high cholesterol, low high-density lipoprotein, hypertension, high uric acid, insulin resistance and ≥2 cardiometabolic abnormalities in overweight and obese children and adolescents. Bayesian kernel machine regression and quantile g-computation were used to analyze the relationship between mixed exposure to PFAS and the risk of metabolic abnormalities in overweight and obese children.

Among the 220 overweight and obese children, 166 were male(75.5%), with an age of 10.7 (8.8, 12.3) years. A total of 131 children (59.1%) had ≥2 cardiometabolic abnormalities. The levels of PFHxA and PFNA were positively correlated with the risk of hypertriglyceridemia in overweight and obese children (OR_PFHxA=1.670, 95% CI: 1.005-2.776; OR_PFNA=1.415, 95% CI: 1.062-1.883). The concentration of PFHpA was positively correlated with the risk of hypertension (OR=1.980, 95% CI: 1.269-3.087). Four serum PFAS concentrations were positively correlated with the risk of insulin resistance. Serum PFHpA and PFNA concentrations were positively correlated with the risk of ≥2 cardiometabolic abnormalities (OR_PFHpA=1.716, 95% CI: 1.155-2.551; OR_PFNA=1.324, 95% CI: 1.003-1.749). In overweight and obese children, the mixed exposure to PFAS was positively correlated with the risk of hyperglycemia, insulin resistance, hypertension, hypertriglyceridemia, and ≥2 cardiometabolic abnormalities, with PFHpA having the largest weight.

Exposure to serum PFAS could increase the risk of abnormal glucose and lipid metabolism, hypertension and ≥2 obesity-related metabolic abnormalities in overweight and obese children and adolescents.

To evaluate the dietary quality of Miao pregnant women in Guizhou in the year before pregnancy and early pregnancy, and to explore the correlation between different dietary intake levels and neonatal adverse birth outcomes.

A prospective cohort study was conducted in Qiandongnan Miao and Dong Autonomous Prefecture of Guizhou Province from June 2022 to March 2023. A total of 511 subjects were included in the study. A food frequency questionnaire was used to collect dietary data of 1 year before pregnancy and early pregnancy, and diet balance index 2022(DBI-22) and diet balance index for pregnancy(DBI-P) were used to evaluate dietary quality. χ² test was used to analyze the difference in the incidence of neonatal adverse birth outcomes between different food intake levels, and binary logistic regression was used to analyze the relationship between different food intake levels and neonatal adverse birth outcomes.

More than 90.0% of pregnant women had dietary imbalance one year before pregnancy and early pregnancy, and the dietary quality in early pregnancy was slightly higher than that in one year before pregnancy. The two stages were mainly characterized by excessive intake of cereals and livestock and poultry meat, and insufficient intake of milk, soybeans and nuts, aquatic products, eggs and food types.Compared with insufficient or excessive intake, women with appropriate intake of fruits in the year before pregnancy and of cereals, vegetables, and soybeans and nuts in early pregnancy had a lower incidence of neonatal adverse birth outcomes(P<0.05). Risk factors for neonatal adverse birth outcomes included excessive intake of cereals (OR=2.261, 95% CI: 1.035-4.938) and insufficient intake of fruits (OR=2.036, 95% CI: 1.354-3.061) in the year before pregnancy. In early pregnancy, excessive intake of cereals (OR=2.814, 95% CI: 1.429-5.544) and fruits (OR=1.988, 95% CI: 1.112-3.555), as well as insufficient intake of vegetables (OR=2.059, 95% CI: 1.305-3.249), and soybeans and nuts (OR=2.367, 95% CI: 1.412-3.969), were risk factors for neonatal adverse birth outcomes.

Pregnant women of Miao nationality in Guizhou generally have unbalanced dietary structure from pre-pregnancy to early pregnancy. Dietary intake level is closely related to the risk of neonatal adverse birth outcomes. Strengthening pregnancy preparation and nutritional intervention during pregnancy can help reduce the incidence of neonatal adverse birth outcomes.

To analyze the epidemiological characteristics of Salmonella foodborne diseases in Hunan Province from 2014 to 2023, and to provide references for disease prevention and control, thereby promoting the rational allocation and targeted intervention of public health resources.

Active surveillance data were collected through the "Foodborne Disease Monitoring and Reporting System", and descriptive analysis was conducted on the epidemiological characteristics of Salmonella foodborne diseases in Hunan from 2014 to 2023. Global and local spatial autocorrelation analyses were performed using ArcGIS 10.8 software.

From 2014 to 2023, sentinel hospitals in Hunan Province reported a total of 2 459 Salmonella-positive cases through active surveillance, with a positivity rate of 7.79%. The lowest number of cases was recorded in 2014 (142 cases), while the lowest positivity rate (5.26%) occurred in 2016. In contrast, 2023 showed the highest figures, with 455 cases and a positivity rate of 10.24%. The incidence exhibited clear seasonality, with higher case numbers in July (426 cases), August (385 cases), and September (354 cases). The positivity rates were also elevated in June (11.92%), July (11.71%), and August (11.17%), indicating that the period from June to September was the peak season for Salmonella infections. The majority of cases (1 572 cases, 66.93%) were concentrated in children aged 5 years and younger. Among the suspected food exposures linked to Salmonella infections, dairy and dairy products accounted for the highest proportion (22.15%). Global spatial autocorrelation analysis revealed a positive spatial correlation in Salmonella positivity rates across Hunan Province from 2015 to 2016. Local spatial autocorrelation analysis further identified "high-high" clustering areas, primarily in Liuyang City, Yuhua District, Tianyuan District, Shifeng District, and Yuetang District.

From 2014 to 2023, Salmonella foodborne diseases in Hunan showed an increasing trend with significant spatial clustering. Prevention and control efforts should be strengthened during high-incidence periods, among high-risk populations, and in key regions.

To investigate the prevalence of human papillomavirus(HPV) infection among females in Nanhai District,Foshan City, and elucidate the distribution patterns of HPV genotypes across different cervical lesions.

HPV genotyping results from 28 592 female patients who underwent physical examinations or medical consultations at the Seventh Affiliated Hospital of Southern Medical University between 2020 and 2024 were collected. Statistical analyses were conducted on HPV subtype infection status, age distribution, and clinical diagnosis.

From 2020 to 2024, HPV infection among females in Nanhai District, Foshan City, exhibited a distinct U-shaped age distribution (higher infection rates in the ≤20 and ≥61 age groups), with high-risk types predominating. High-risk HPV types 52, 16, and 58 were identified as the primary pathogenic variants in this region, with HPV16 infection rates showing a significant increase correlating with lesion severity.The 9-valent vaccine could cover 78.26% of cervical cancer-associated HPV genotypes in this region, but its overall HPV infection coverage rate was 42.80%.

The HPV infection pattern among females in Nanhai District, Foshan City from 2020 to 2024 demonstrated significant age-related disparities, with a U-shaped infection rate trend over the five-year period. It is recommended to enhance screening and education for age-eligible populations, strengthen surveillance and vaccine promotion, and continuously monitor the epidemiological trends of non-vaccine-covered high-risk types (e.g., HPV 51, 68, 39).

To investigate the dynamic development trajectories of internet addiction in children and adolescents after COVID-19, and the influence of factors such as gender, ethnicity, parental education and non-suicidal self-injury, and provide a reference for the formulation of precision psychological intervention programs.

In the Chengdu Positive Child Development Cohort, we conducted follow-up surveys at four time points from December 2019 to June 2022, using the Internet Addiction Scale (IAT) and the Deliberate Self-Injury Scale (DSHI). The study involved 3 865 students from grades 1 to 6. The latent growth mixture model (LGMM) was used to analyze the development trajectory of internet addiction in children and adolescents. Factors associated with changes in each latent category of internet addiction were analyzed using multiple logistic regression.

There was a clear upward trend in internet addiction among children and adolescents over time. The developmental trajectories among children and adolescents with internet addiction could be categorized into four distinct groups, labeled as C1 "low-level-slow-rise group" (77.7%), C2 "low-level-deteriorating group" (9.3%), C3 "high-level-remitting group(8.2%) and C4 "high-level-stable group" (4.8%). Males, non-Han ethnicity, maternal education below junior high school, and non-suicidal self-injury were identified as risk factors associated with adverse trajectory changes in internet addiction among children and adolescents.

Children and adolescents with internet addiction exhibited significant group heterogeneity and individual differences in their development trajectories, so precise psychological intervention programs should be given according to different categories to use limited psychological resources to improve the intervention effect rationally.