Bio: Su Zekang, who obtained his Ph.D. from the School of Public Health at Peking University in the field of Occupational and Environmental Health, specializes in researching the health effects of exposure to harmful occupational environmental factors, specifically chromates. He has published 18 academic papers and has an H-index of 7. Dr. Su serves as a youth editorial board member for the Eco-Environment & Health journal and acts as a reviewer for the journals Frontiers in Public Health, New Medicine Insights, and the Journal of Mathematical Medicine.
Abstract: Exposure to hexavalent chromium damages DNA and chromosomes, increasing cancer risk, but immunological mechanisms involved are rarely studied. This research in 120 chromate-exposed workers examined the relationship between blood Cr levels and genetic damage biomarkers (urinary 8-OHdG and blood MNF), alongside immune regulatory mechanisms like costimulatory molecules and immune checkpoints. Higher blood Cr was linearly correlated with increased genetic damage. Exploratory factor analysis showed both positive and negative immune regulation patterns positively associated with blood Cr. Specifically, elevated PD-1 (4.12%), PD-L1 (5.22%), LAG-3 (2.11%), and their constitutive positive immune regulation pattern (5.86%) indirectly enhanced the relationship between blood Cr and urinary 8-OHdG. NLRP3 positively influenced the association between blood Cr and inflammatory immune responses. Using machine learning, this study highlights the complex interactions among environmental toxins, genetic damage, and immune regulation, stressing the need for further research in this area.
10
0
0
Date | Time | Local Time | Room | Forum | Session | Role | Topic |
---|---|---|---|---|---|---|---|
2025-10-17 | 14:30-14:50 | 2025-10-17,14:30-14:50 | Room 3 - Guocui Hall | Workshop |
Workshop 05: Understanding and Mitigating Occupational Heavy Metal Exposure: A Comprehensive Approach |
Speaker | Immune regulation patterns in response to environmental pollutant chromate exposure-induced genetic damage: A cross-sectional study applying machine learning methods |