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- Cao, Huiming, et al.
(author)
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Effect of Enterohepatic Circulation on the Accumulation of Per- and Polyfluoroalkyl Substances : Evidence from Experimental and Computational Studies
- 2022
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In: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 56:5, s. 3214-3224
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Journal article (peer-reviewed)abstract
- The pharmacokinetic characteristics of per- and polyfluoroalkyl substances (PFAS) affect their distribution and bioaccumulation in biological systems. The enterohepatic circulation leads to reabsorption of certain chemicals from bile back into blood and the liver and thus influences their elimination, yet its influence on PFAS bioaccumulation remains unclear. We explored the role of enterohepatic circulation in PFAS bioaccumulation by examining tissue distribution of various PFAS in wild fish and a rat model. Computational models were used to determine the reabsorbed fractions of PFAS by calculating binding affinities of PFAS for key transporter proteins of enterohepatic circulation. The results indicated that higher concentrations were observed in blood, the liver, and bile compared to other tissues for some PFAS in fish. Furthermore, exposure to a PFAS mixture on the rat model showed that the reabsorption phenomenon appeared during 8-12 h for most long-chain PFAS. Molecular docking calculations suggest that PFAS can bind to key transporter proteins via electrostatic and hydrophobic interactions. Further regression analysis adds support to the hypothesis that binding affinity of the apical sodium-dependent bile acid transporter is the most important variable to predict the human half-lives of PFAS. This study demonstrated the critical role of enterohepatic circulation in reabsorption, distribution, and accumulation of PFAS.
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| 2. |
- Dimitrakakis, Christos, 1975, et al.
(author)
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The reinforcement learning competition
- 2014
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In: AI Magazine. - : Wiley. - 0738-4602 .- 2371-9621. ; 35:3, s. 61-65
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Journal article (peer-reviewed)abstract
- Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.
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