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Translational toxicology and exposomics for food safety risk management

Journal of Translational Medicine
Springer (Biomed Central Ltd.)
Publication Date
DOI: 10.1186/1479-5876-10-s2-a41
  • Meeting Abstract
  • Medicine
  • Pharmacology


Translational toxicology and exposomics for food safety risk management MEETING ABSTRACT Open Access Translational toxicology and exposomics for food safety risk management Yongning Wu From 2012 Sino-American Symposium on Clinical and Translational Medicine (SAS-CTM) Shanghai, China. 27-29 June 2012 Background China embraces the use of risk analysis in the develop- ment of risk-based approaches for the management of public health hazards in food safety. Risk analysis is made up of three components and Figure 1 illustrates the relationship between the three components of risk analysis [1]. Estimating the magnitude and distribution of benefits and costs of particular risk management options may require addressing a myriad of concerns, e.g., changes in the availability or nutritional quality of foods; impacts on consumer confidence in the safety of the food supply or in the food regulatory system [2]. This is a brief introduction on risk management options for dealing with the new outcome from risk assessment approaches in China. Materials and methods Traditionally, risk assessment is based on deterministic endpoints, i.e., use of the no observed (adverse) effect level (NO(A)EL) and the mean or high level of exposure. Correspondence: [email protected] China National Center for Food Safety Risk Assessment, Beijing, 100050, China Figure 1 Food safety risk analysis framework Wu Journal of Translational Medicine 2012, 10(Suppl 2):A41 © 2012 Wu; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the 21st century, exposure science has increasingly embraced deterministic models to predict levels of diverse exposures based on categorical data and on mea- sured level

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