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个人简介
Taiwan Lead Worker Cohort:
Main research interest is the association of Lead (Pb) Biomarkers, Oxidative stress, and Chronic Diseases, which tests the hypotheses that lead toxicity to kidney function, blood pressure, cardiovascular diseases, metabolic syndrome, cognitive performance, and other measures of autonomic nervous function. The second part of the research is Gene-lead (Pb) Interactions, which aims to discover and validate novel genes through the Gene Over Expression Tools in the lead (Pb) exposed workers.
莊弘毅教授從事鉛作業勞工研究已有二十年以上,主要為鉛作業勞工職業衛生的第一級預防(健康促進)及第二級預防(健康管理)。莊弘毅長期追蹤與服務鉛作業工人,不僅照顧他們的健康,此世代族群二十多年來持續與其研究合作,發表的論文有50 篇以上,其中以第一作者或通訊作者發表在SCI 期刊的有40 篇以上。莊弘毅為最主要的研究計畫主持人,高雄醫學大學骨鉛實驗室的創辦人與負責人,並領導整個計畫的執行與論文的執筆或為論文執筆潤飾,並為通訊作者。
Environmental Metal Exposure and Genetics:
The main researches are: Metal (Pb, Cd, As, Al,...) Biomarkers, Oxidative stress, and Chronic Diseases. The research interesting is to test hypotheses that study the metal toxicity to chronic diseases, such as kidney diseases, blood pressure, cardiovascular diseases, metabolic syndrome, cognitive performance, and other measures of autonomic nervous function; and the potential modifying influence of candidate genetic polymorphisms related to oxidative stress, enzyme activity, and cholesterol metabolism. The second part of the research is: Gene-Environmental (such as Pb, Cd, As, Al,...) Interactions, which aims to discover and validate novel genes through the Gene Over Expression Tools in the metal workers.
主要研究有:金屬(Pb、Cd、As、Al、...)生物標誌物、氧化壓力(Oxidative stress)和慢性疾病。 研究的興趣是檢驗研究的金屬暴露對慢性疾病的毒性的假設,例如腎臟疾病、血壓、心血管疾病、代謝綜合徵、認知表現和其他自主神經功能測量以及與氧化壓力(Oxidative stress)、酶活性和膽固醇代謝相關的候選基因多態性的潛在修飾影響。 研究的第二部分是 Gene-Environmental (例如Pb, Cd, As, Al,...) Interactions,旨在通過金屬工人中的Gene Over Expression Tools 發現和驗證新基因。
Precision Environmental/Occupational Medicine:
The project compares Taiwan lead worker cohort, a reference group of non-metal workers, and a group of sex- and age-matched Taiwan Biobank (TWB) population group, (total participants are about 1300) with their multi-elements (Pb, Cd, As, Mn, Se, Cu, Zn, and Co) and health outcomes from health examinations to fulfill some aims. The findings from this study lay the groundwork to understand the underlying genetic influences, gene-gene and gene-environment (metals) interactions on indicators of preclinical indicators from health examinations, and subclinical status or chronic diseases. Consequently, this project facilitates effective public health interventions in occupations at high risk, especially the metal workers. In addition, it also conducts cross-discipline collaboration of developing artificial intelligence in health care and precision environmental/occupational medicine.
此研究將以前建立之鉛工人世代和一個(以鉛工人之人口學為基礎的對照組)非金屬工作的普通工人參考群體與一組性別和年齡相匹配的台灣生物資料庫(TWB)之社區世代:比較三組(約1300人)之全基因體定型(whole-genomic typing)以及其血液中全血鉛和血漿中多種元素(鉛,鎘,砷,硒,銅,鋅,和鈷)與健康檢查及問卷的健康關係。這項研究結果將為理解潛在的遺傳影響,基因-基因和基因-環境(金屬)相互作用對健康及亞臨床狀況或慢性疾病奠定基礎。因此,這項研究將促進金屬工人(高危險群群)的有效公共衛生干預。此外,本案還將開展跨學科合作,可開發醫療保健和個別化環境-職業醫學領域的人工智能。
The use of Artificial Intelligence technologies studies the association of chronic diseases and environmental pollutants and whole genomic types. The method combines the environmental/occupational pollutant data and Taiwan Biobank database with the whole genomic types; then uses machine learning methods (such as deep learning, and neural network methods) to effectively identify the risk factors of pollutants and susceptible genotypes.
使用AI 研究重要的慢性病--代謝症候群(Metabolic syndrome, MetS), 慢性腎病(Chronic kidney diseases, CKD), 失智症(Alzheimer's Disease and/or Dementia), 與空氣汙染物和基因型的關聯性。
Based on our previous research, another project focused on the studies of 3 important chronic diseases-Metabolic syndrome (MetS), Chronic kidney diseases (CKD), Alzheimer's Disease and/or Dementia (AD), and their association with air pollutants and whole genomic types. The method will use the air pollutant data from various air monitoring stations of the Environmental Protection Agency and the other resources as the exposure data, and link to the health data of Taiwan Biobank database with the whole genomic types to establish the models which use machine learning methods (such as deep learning, neural network methods) to effectively identify the risk factors of air pollutants and susceptible genotypes for the 3 chronic diseases. Some publications:
The Association of White Blood Cells and Air Pollutants- A Population-Based Study. Int J Environ Res Public Health 2021, 18: 2370. https://doi.org/10.3390/ijerph18052370
Association Pattern between Ambient Temperature Change and Leukocyte Counts. Int J Environ Res Public Health 2021, 18: 6971. https://doi.org/10.3390/ijerph18136971
The Association of Carcinoembryonic Antigen (CEA) and Air Pollutants- A Population-Based Study. Atmosphere 2022, 13: 466. https://doi.org/10.3390/atmos13030466
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Chen–Cheng Yang, Hsiang-Tai Chen,Kuei-Hau Luo,Kazuhiro Watanabe,Hung-Yi Chuang, Chih-Wei Wu,Chia–Yen Dai,Chao-Hung Kuo,Norito Kawakami
JOURNAL OF THE ENDOCRINE SOCIETYno. 5 (2024): bvae035-bvae035
Frontiers in public health (2024): 1303958-1303958
Social Science Research Network (2023): 109832-109832
Environmental Pollution (2023): 121900-121900
Drug and alcohol dependence (2023): 109832-109832
BEHAVIORAL SCIENCESno. 11 (2023): 903-903
Frontiers in public health (2023): 1140615
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