Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Background: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. Methods: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011–2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. Results: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 μm [PM10 ]), 3.7% (PM2.5 ), and 2.2% (sulfur dioxide [SO2 ]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. Conclusion This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5 , and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.

Purpose: Evidence suggests that long-term air pollution exposures may induce depression; however, the influence of physical activity on this effect is unclear. We investigated modification of the associations between air pollution exposures and depression by the intensity of physical activity. Materials and Methods: This cross-sectional study included 1454 Korean adults. Depression was defined as a Geriatric Depression Scale score ≥8. Concentrations of particulate matter (PM10 and PM2.5: diameter ≤10 μm and ≤2.5 μm, respectively) and nitrogen dioxide (NO2) level at each participant’s residential address were estimated. Based on metabolic equivalents, physical activity intensity was categorized as inactive, minimally active, or health-enhancing physical activity (HEPA). Results: Each 1-part per billion (ppb) NO2 concentration increase was significantly associated with a 6% [95% confidence interval (CI), 4%–8%] increase in depression risk. In older adults (≥65 years), a 1-ppb NO2 increase was associated (95% CI) with a 4% (1%–7%), 9% (5%–13%), and 21% (9%–33%) increase in depression risk in the inactive, minimally active, and HEPA groups, respectively. Compared with the inactive group, the minimally active (p=0.039) and HEPA groups (p=0.004) had higher NO2 exposure-associated depression risk. Associations of PM10 and PM2.5 with depression did not significantly differ by the intensity of physical activity. Conclusion We suggest that older adults who vigorously exercise outdoors may be susceptible to air pollution-related depression.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Background: Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH). Methods: We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant’s home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction. Results: Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m3 increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07–2.97) and periventricular WMH (2.00; 1.20–3.33). A 1 μg/m3 increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08–2.56). These associations did not significantly differ by the level of high sensitivity CRP. Conclusion Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.

Microplastics are environmental pollutants that prevail in the oceans, remote islands, and polar regions. Exposure to microplastics presents a major emerging threat to the ecosystems due to their potential adverse effects. Herein, we reviewed the literature to provide an up-to-date synopsis of the current understanding of the sources, compositions, and adverse effects of microplastics in humans and the environment. Most studies on microplastics have focused on developing standardized methods for monitoring the occurrence, distribution, and movement of microplastics in the environment, as well as developing microplastic substitutes; however, although humans are exposed to microplastics via various routes, research on the adverse effects of microplastics in humans remains limited. Little is known about the impact of microplastics on human health and the toxic effects that may vary depending on the type, size, shape, and concentration of microplastics. Therefore, more research is needed to understand the cellular and molecular mechanisms of microplastic toxicity and related pathologies.

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18 F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.