Table_2_Health damage assessment of commuters and staff in the metro system based on field monitoring—A case study of Nanjing.DOCX

IntroductionThe metro has emerged as a major mode of transportation. A significant number of commuters and staff in the metro system are exposed to air pollutants because of its shielded environment, and substantial health damage requires quantitative assessment. Previous studies have focused on comparing the health impacts among different transportation modes, overlooking the specific population characteristics and pollutant distribution in metro systems.MethodsTo make improvements, this study implements field monitoring of the metro's air environment utilizing specialized instruments and develops a health damage assessment model. The model quantifies health damage of two main groups (commuters and staff) in metro systems at three different areas (station halls, platforms, and metro cabins) due to particulate matter 10 and benzene series pollution.ConclusionA case study of Nanjing Metro Line 3 was conducted to demonstrate the applicability of the model. Health damage at different metro stations was analyzed, and the health damage of commuters and staff was assessed and compared. This study contributes to enhancing research on health damage in the metro systems by providing a reference for mitigation measures and guiding health subsidy policies.</p

Table_1_Health damage assessment of commuters and staff in the metro system based on field monitoring—A case study of Nanjing.DOCX

IntroductionThe metro has emerged as a major mode of transportation. A significant number of commuters and staff in the metro system are exposed to air pollutants because of its shielded environment, and substantial health damage requires quantitative assessment. Previous studies have focused on comparing the health impacts among different transportation modes, overlooking the specific population characteristics and pollutant distribution in metro systems.MethodsTo make improvements, this study implements field monitoring of the metro's air environment utilizing specialized instruments and develops a health damage assessment model. The model quantifies health damage of two main groups (commuters and staff) in metro systems at three different areas (station halls, platforms, and metro cabins) due to particulate matter 10 and benzene series pollution.ConclusionA case study of Nanjing Metro Line 3 was conducted to demonstrate the applicability of the model. Health damage at different metro stations was analyzed, and the health damage of commuters and staff was assessed and compared. This study contributes to enhancing research on health damage in the metro systems by providing a reference for mitigation measures and guiding health subsidy policies.</p

Allelic interaction effects of DNA damage and repair genes on the predisposition to age-related cataract

<div><p>Purpose</p><p>Age-related cataract (ARC) is a leading cause of visual impairment and blindness worldwide. DNA damage and malfunction of DNA repair are believed to contribute to the pathogenesis of ARC. Aside from increasing age, the risk factors for ARC appear to be rather complex, and one or more gene variations could play critical roles in the diverse processes of ARC progression. This study aimed to investigate the combined effects of different genetic variants on ARC risk.</p><p>Methods</p><p>A cohort of 789 ARC patients and 531 normal controls from the Jiangsu Eye Study was included in this study. Genotyping of 18 single-nucleotide polymorphisms (SNPs) in 4 DNA damage/repair genes was performed using TaqMan SNP assays. SNP-SNP interactions were analyzed via multifactor dimensionality reduction (MDR), classification and regression tree (CART) and genetic risk score (GRS) analyses.</p><p>Results</p><p>Based on single-locus analyses of the 18 SNPs examined, <i>WRN-rs11574311 (</i></p><p><i>T>C</i></p><i>)</i> was associated with ARC risk. However, in MDR, the gene-gene interaction among the five SNPs (<i>WRN-rs4733220 (</i><p><i>G>A</i></p><i>)</i>, <i>WRN-rs1801195 (</i><p><i>T>G</i></p><i>)</i>, <i>OGG1-rs2072668 (</i><p><i>G>C</i></p><i>)</i> and <i>OGG1-rs2304277 (</i><p><i>A>G</i></p><i>)</i>) on ARC risk was significant (OR = 5.03, 95% CI: 3.54~7.13). CART analyses also revealed that the combination of five SNPs above was the best polymorphic signature for discriminating between the cases and the controls. The overall odds ratio for CART ranged from 4.56 to 7.90 showing an incremental risk for ARC. This result indicated that these critical SNPs participate in complex interactions. The GRS results showed an increased risk for ARC among individuals with the SNPs in this polymorphic signature.<p></p><p>Conclusion</p><p>The use of multifactorial analysis (or an integrated approach) rather than a single methodology could be an improved strategy for identifying complex gene interactions. The multifactorial approach used in this study has the potential to identify complex biological relationships among ARC-related genes and processes. This approach will lead to the discovery of novel biological information, ultimately improving ARC risk management.</p></div

Association of higher-order interactions with overall ARC risk based on MDR Analysis.

<p>Association of higher-order interactions with overall ARC risk based on MDR Analysis.</p

Risk estimates of CART Terminal Nodes for patients with the M subtype of ARC.

<p>Risk estimates of CART Terminal Nodes for patients with the M subtype of ARC.</p

Genotype distribution of SNPs in control and ARC subjects.

<p>Genotype distribution of SNPs in control and ARC subjects.</p

Risk estimates of CART Terminal Nodes for patients with the N subtype of ARC.

<p>Risk estimates of CART Terminal Nodes for patients with the N subtype of ARC.</p

Demographic characteristics of the study participants.

<p>Demographic characteristics of the study participants.</p

Allele distribution of SNPs in control and ARC subjects.

<p>Allele distribution of SNPs in control and ARC subjects.</p

Risk estimates of CART Terminal Nodes for patients with the C subtype of ARC.

<p>Risk estimates of CART Terminal Nodes for patients with the C subtype of ARC.</p