简要描述

教育经历

工作经历

2007/07-2008/02，国家高新技术企业武汉佳成技术中心副主任

2012/04-至今，武汉轻工大学，生物与制药工程学院，讲师、副教授

2017/08-2018/08, Texas A&M   University, Department of Nutrition and Food Science,访问学者，合作导师   Chaodong Wu

研究方向：

学科：生物化学与分子生物学、微生物学、药理学、微生物与生化药学、生物数据学（流行病与卫生统计学）、多组学、分析化学

模式动物、人的个体（早期）发育、脂肪肝/高脂血症、糖脂代谢（组）、转录及蛋白组、(表观)遗传、微生物（组），健康大数据、流行病调查，南瓜活性成分开发。

科研项目：

国家重点研发计划、国家自然科学基金、湖北省教育厅重点项目、转化项目等

代表性论文与著作

发育健康营养多组学大数据领域发表论文46篇，其中通讯或第一作者22篇，参与10篇顶尖期刊(The   Lancet, Nature )论文；6个特邀报告。

代谢组NMR分析与药物毒理研究.波谱学杂志,   2011, 28 (1), 1-17. 1st & Correspondence.

Dynamic   metabolic response of mice to acute mequindox exposure. Journal of   Proteome Research, 2011, 10, 5183-5190. 1st.

Systems   responses of rats to mequindox revealed by metabolic and transcriptomic   profiling. Journal of Proteome Research, 2012, 11, 4712-4721.   1st.

ECR-MAPK   regulation in liver early development. BioMed Research International,2014:   850802. 1st & Correspondence.

Intervention   of pumpkin seed oil on metabolic disease revealed by metabonomics and   transcript profile. Journal of the Science of Food and Agriculture,   2017, 97(4): 1158-1163. 1st & Correspondence.

Dynamic   Lipid Profile of Hyperlipidemia Mice. Journal of Chromatography B,   2017, 1055–1056: 165–171. Correspondence.

Risk   Factors and Biomarkers of Non-alcoholic Fatty Liver Disease: an observational   cross-sectional population survey. BMJ open.   2018;8:e019974. Correspondence.

以中国中部地区1479人横断面观察型临床试验为对象，运用代谢组检测、多变量Logistic回归和接受者操作特征曲线下面积（AUROC），发现非酒精性脂肪肝病（NAFLD）新的高发年龄段50-60岁和新的危险因素与生物标志物油酸羟化油酸酯。

Population   and fertility by age and sex for 195 countries and territories, 1950–2017: a   systematic analysis for the Global Burden of Disease Study 2017. The Lancet   2018; 392: 1995–2051. Coauthor.

Prediction   of cause-specific disability-adjusted life-years in China 2018-2021: a   systematic analysis. Public Health. 2020,180:90-99. Correspondence.

Global,   regional, and national burden of cirrhosis by aetiology in 195 countries and   territories, 1990–2017: a systematic analysis for the Global Burden of   Disease Study 2017.The Lancet Gastroenterology & Hepatology 2020;5(3):245-266. Coauthor.

NMR-based plasma metabonomics in Hyperlipidemia Mice. Analytical   Methods 2020, 12, 1995 - 2001. Correspondence.

Mapping   local patterns of childhood overweight and wasting in low- and middle-income   countries between 2000 and 2017. Nature Medicine 2020, 26:   750–759. coauthor

Mapping   geographic inequalities in childhood diarrhoeal morbidity and mortality in   low- and middle-income countries, 2000–17: analysis for the Global Burden of   Disease Study 2017. The Lancet 2020, 395(10239)：1779-1801.   Coauthor.

Mapping   geographical inequalities in oral rehydration therapy coverage in low-income   and middle-income countries, 2000–17. The Lancet Global Health   2020, 8(8):e1038-e1060. Coauthor.

Measuring   universal health coverage using the UHC effective coverage index in 204   countries and territories, 1990–2019: a systematic analysis from the Global   Burden of Disease Study 2019. The Lancet DOI: 10.1016/S0140-6736(20)30750-9. Coauthor.

Global   burden of 302 diseases, injuries, and impairments, 1990–2019: a systematic   analysis for the Global Burden of Disease Study 2019. The Lancet Coauthor.

The   unfulfilled promise of prevention: the global burden of 86 risk factors,   1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.   The Lancet Coauthor.

Five insights from the Global Burden of Disease Study 2019. The   Lancet Coauthor.

Prediction   of cause-specific disability-adjusted life-years in China 2018-2021: a   systematic analysis. Public Health 2020,180:90-99.

Intervention   of 4% salmon phospholipid on metabolic syndrome in mice based on colonic   lipidomics analysis. Journal of the Science of Food and Agriculture,   102 (8), 3088-3098 (2022).

Linking   research of biomedical datasets. Briefings in Bioinformatics   2022, 23 (6): bbac373. DOI:   10.1093/bib/bbac373. Review. 数学与计算生物学 1/57。1st   & Correspondence.

提出生物数据学（biodatatics），概括其类脑生态理论，即全流程数据活动机制DCSAE（多样兼顾，环节衔接，安全共享，权责一致，生态使能）多样化弱关联数据集通过原生类脑方式（动力引擎和思想知识双轮驱动，算法和算力协调匹配）预测发现。数据环节，概括为设计、采样、预处理与分析和治理，通过技术导则、标准化、应用程序接口、软件开发包、模型参数等衔接。匿名化和去标识化，对抗训练，群体学习，高性能图计算，图查询语言，混合事务分析处理等，兼顾深度计算、海量存储和高并发通讯，实现数据安全共享。以比例原则分配不同数据参与方的权利与义务、权力与职责。数据在使用中升值，不同数据参与方都能从中获益，形成数据生态系统（物料平衡、能量守恒、简单优先）。

Dietary   interventions of salmon and silver carp phospholipids on mice with metabolic   syndrome based on lipidomics. Cells, 2022, 11, 3199.

其他

Journal of Cell   Science, International Journal of   Obesity, Bioscience Reports, Analytical Methods, European   Journal of Epidemiology特邀审稿人。

发起“婴幼儿发育健康与营养研发小组”(Team   of neonatal/infant development, health and nutrition, NDHN)，指导学生发表8篇SCI期刊论文。

湖北省首届中小微企业“科技副总”（2020）