Objective: Heavy metals are present in many environmental pollutants, and have cumulative effects on the human body through water or food, which can lead to several diseases, including osteoarthritis (OA). In this research, we aimed to explore the association between heavy metals and OA.Methods: We extracted 18 variables including age, gender, race, education level, marital status, smoking status, body mass index (BMI), physical activity, diabetes mellitus, hypertension, poverty level index (PLI), Lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), manganese (Mn), and OA status from National Health and Nutrition Examination Survey (NHANES) 2011-2020 datasets.Results: In the baseline data, the t test and Chi-square test were conducted. For heavy metals, quartile description and limit of detection (LOD) were adopted. To analyze the association between heavy metals and OA among elderly subjects, multivariable logistic regression was conducted and subgroup logistic by gender was also carried out. Furthermore, to make predictions based on heavy metals for OA, we compared eight machine learning algorithms, and XGBoost (AUC of 0.8, accuracy value of 0.773, and kappa value of 0.358) was the best machine learning model for prediction. For interactive use, a shiny application was made (https://alanwu.shinyapps.io/NHANES-OA/).Conclusion: The overall and gender subgroup logistic regressions all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the elderly population of the United States. Furthermore, XGBoost model was trained for OA prediction.

Objective: To explore the association between depression and blood metal elements, we conducted this machine learning model fitting research.Methods: Datasets from the National Health and Nutrition Examination Survey (NHANES) in 2017–2018 were downloaded (https://www.cdc.gov/nchs/nhanes). After screening, 3,247 aging samples with 10 different metals [lead (Pb), mercury (Hg), cadmium (Cd), manganese (Mn), selenium (Se), chromium (Cr), cobalt (Co), inorganic mercury (InHg), methylmercury (MeHg) and ethyl mercury (EtHg)] were included. Eight machine learning algorithms were compared for analyzing metal and depression. After comparison, XGBoost showed optimal effects. Poisson regression and XGBoost model (a kind of decision tree algorithm) were conducted to find the risk factors and prediction for depression.Results: A total of 344 individuals out of 3247 participants were diagnosed with depression. In the Poisson model, we found Cd (β = 0.22, P = 0.00000941), EtHg (β = 3.43, P = 0.003216), and Hg (β=-0.15, P = 0.001524) were related with depression. XGBoost model was the suitable algorithm for the evaluation of depression, the accuracy was 0.89 with 95%CI (0.87, 0.92) and Kappa value was 0.006. Area under the curve (AUC) was 0.88. After that, an online XGBoost application for depression prediction was developed.Conclusion: Blood heavy metals, especially Cd, EtHg, and Hg were significantly associated with depression and the prediction of depression was imperative.

Background: According to previous reports, obesity especially visceral fat has become an important public health problem, causing an estimation of 20.5 disability-adjusted life years per 1000 inhabitants. Those who exercised for 1 or 2 days per week and reached the recommended 150 minutes of moderate physical activity (PA) per week have been defined as “weekend warriors” (WWs). Although the benefits of PA in suppressing obesity have been widely studied, the association of WWs with the Visceral Adiposity Index (VAI) and the difference between WW activity and regular PA are yet to be explored. Objective: This study aims to explore the association between WW activity and other PA patterns with VAI in US adults. Methods: The National Health and Nutrition Examination Survey 2007-2016 data set was used, and the analytic sample was limited to adults 20 years and older who had complete information about VAI, PA patterns, and other covariates, including demographic characteristics, behavioral factors, and disease conditions. Participants’ characteristics in different PA pattern groups were tested using the Rao and Scott adjusted χ2 test and ANOVA. Univariate and multivariate stepped linear regression models were then used to explore the association between the PA pattern and VAI. Finally, stratified analyses and interaction effects were conducted to investigate whether the association was stable among subgroups. Results: The final sample included 9642 adults 20 years or older, which is representative of 158.1 million noninstitutionalized US adults, with 52.15% (n=5169) being male and 70.8% (n=4443) being non-Hispanic White. Gender, age group, race, education level, income level, marital status, smoking status, alcoholism, VAI, cardiovascular disease, and diabetes were all correlated with the PA pattern, but no relationship between hypertension and PA pattern was observed. After adjusting for demographic covariates, smoking status, alcoholism, cardiovascular disease, diabetes, and hypertension, WW and regularly active adults had a β of .307 (95% CI –0.611 to –0.003) and .354 (95% CI –0.467 to –0.241), respectively, for reduced VAI when compared with inactive adults, but no significant effect of lowering VAI (β=–.132, 95% CI –0.282 to 0.018) was observed in insufficiently active adults when compared with inactive adults. Besides, no significant difference was exhibited between WW adults and regularly active adults (β=.047, 95% CI –0.258 to 0.352), suggesting WW adults had the same benefit of decreasing VAI as regularly active adults. Stratified analyses results exhibited that WW activity was related to reduced VAI in female adults aged 20-44 years who were non-Hispanic Black, other, or multiracial; high school or General Educational Development education; and never married, and the association between PA pattern and VAI remained stable in all demographic subgroups. Conclusions: Compared with inactive adults, WWs could reduce VAI, and there was no significant difference between WWs and regular active adults in decreasing VAI. Our study provides compelling evidence of the beneficial effect of WW activity on visceral obesity.

Tuberculosis (TB) is a single infectious agent disease caused by mycobacterium tuberculosis (Mtb) and is the 13th leading cause of death worldwide, usually affecting the lungs and other organs [1]. It is the second leading single cause of death in the future, with an estimated 10 million TB patients globally in 2020 and 10.6 million […]

Objective: We aimed to investigate the relationship of metal exposure and latent tuberculosis infection (LTBI) among US adults and adolescents.Methods: Participants from the National Health and Nutrition Examination Surveys (NHANES 2011 ~ 2012) were included. Multiple logistic regression models were used to explore the associations between metal exposure and LTBI. A total of 5,248 adults and 1,860 adolescents were included in the present analysis.Results: For adults, we only found a positive association between total mercury and LTBI (OR: 1.411; 95% CI: 1.164 ~ 1.710) when used as a continuous variable. Compared with Q1, Q4 increased the prevalence of LTBI (2.303; 1.455 ~ 3.644) when used as a quartile. The OR of total mercury and LTBI was higher among females (1.517; 1.009 ~ 2.279), individuals aged 45 ~ 64 (1.457; 1.060 ~ 2.002), and non-Hispanic White individuals (1.773; 1.316 ~ 2.388). A relationship was observed among only participants with obesity (1.553; 1.040 ~ 2.319) or underweight (1.380; 1.076 ~ 1.771), with college or above (1.645; 1.184 ~ 2.286), with PIR > 3.0 (1.701; 1.217 ~ 2.376), reported smoking (1.535; 1.235 ~ 1.907) and drinking (1.464; 1.232 ~ 1.739). For adolescents, blood manganese was positively associated with LTBI. The OR and 95% CIs for each one-unit increase in the log-transformed level of blood manganese with LTBI were 9.954 (1.389 ~ 71.344).Conclusion: Significant associations were observed in girls, aged ≥12 years and in the non-Hispanic white population. In conclusion, total mercury is associated with an increased prevalence of LTBI among adults and positive association between blood manganese and LTBI was observed among adolescents. Further studies should be conducted to verify the results and explore potential biological mechanisms.

Understanding the transmission mode of syphilis is essential to prevent and predict its future prevalence and to develop effective control measures. This study aimed to develop a Network Suspected Infectious Disease Model to simulate the syphilis transmission. The number of syphilis cases in Wuhan's fourth hospital, Hubei province from October 2015 to July 2021 was collected. The simulation was carried out by interpersonal network-SI (suspected-infectious) model based on temporal exponential family random graph models (TERGMs). Late latent syphilis and tertiary syphilis are predicted by December 2025. The validity of simulated value and real data was tested, including determination coefficient (R²), root means square error (RMSE), and means relative error (MRE). Moreover, we developed an online app that can more easily predict the number of syphilis infections in different scenarios by setting different parameters. Results showed that R², RMSE and MRE were 0.995, 36.19 and 6.31, respectively. Speed from latent infection to primary syphilis, primary syphilis to secondary syphilis and susceptible group to latent infection decreased rapidly. Speed of transformation from secondary syphilis to early incubation period and early latent to late latent experienced a process from increase to decreased. Late latent to tertiary syphilis patients increased steadily. Number of late latent patients, early latent, invisible infection, primary syphilis and secondary syphilis all increased at first and turn to decreased. However, tertiary syphilis continuously kept rising in the whole process. To better make use of the transmission model, an online application was developed (https://alanwu.shinyapps.io/MD-shiny/). Based on the simulation that late latent and tertiary syphilis were steadily increasing, the prevention and treatment for syphilis were imperative.