Silver nanoparticles are recognized as effective antimicrobial agents and have been implemented in various consumer products including washing machines, refrigerators, clothing, medical devices, and food packaging. Alongside the silver nanoparticles benefits, their novel properties have raised concerns about possible adverse effects on biological systems. To protect consumer’s health and the environment, efficient monitoring of silver nanoparticles needs to be established. Here, we present the development of human metallothionein (MT) based surface plasmon resonance (SPR) sensor for rapid detection of nanosilver. Incorporation of human metallothionein 1A to the sensor surface enables screening for potentially biologically active silver nanoparticles at parts per billion sensitivity. Other protein ligands were also tested for binding capacity of the nanosilver and were found to be inferior to the metallothionein. The biosensor has been characterized in terms of selectivity and sensitivity towards different types of silver nanoparticles and applied in measurements of real-life samples—such as fresh vegetables and river water. Our findings suggest that human MT1-based SPR sensor has the potential to be utilized as a routine screening method for silver nanoparticles, that can provide rapid and automated analysis dedicated to environmental and food safety monitoring.FigureSurface plasmon resonance biosensor for rapid detection of silver nanoparticles was developed. Human metallothionein 1A (hMT1A) was immobilized on the sensor chip surface and showed dose dependent binding to the silver nanoparticles with part per billion sensitivity.Electronic supplementary materialThe online version of this article (doi:10.1007/s00216-012-5920-z) contains supplementary material, which is available to authorized users.
This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP).
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