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Particularly, it allows to focus attention and efforts on those cases for which mixture effects are of potential concern, and it initially uses only the available toxicity information of the individual components for this purpose.
These, and similar, mixture effects are thought to derive from mixture interactions in odor coding.
The following two articles on mixture effects are laboratory-based studies.
Mixture effects are expected in environmental waters because many other organic and inorganic contaminants are discharged in conjunction with different anti-infectives.
The biological reality of combination effects from exposure to multiple agents at low doses highlights the potential for underestimating risks when mixture effects are not taken into account.
The present review demonstrates unequivocally that assuming that default uncertainty factors are overly conservative worst-case scenarios that could account both for the lack of statistical power in animal experiments and protect against potential mixture effects are ill-founded.
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Based on these concentration-response data, mixture effects were predicted by applying the model of concentration addition.
For the EC1, EC50 and equimolar ratio, prediction errors from the IFCA IA model at the 50% experimental mixture effects were 0.3%, 6%and0.6%6%, respectively; while for the TSP model, the corresponding errors were 2.8%, 19 % and 24, respectively.
The intention of using assessment factors for mixture effects was abandoned thirty years ago.
The mixtures were tested using a fixed-ratio design, and the resulting mixture effects were compared to the predictions.
For all end points, the observed mixture effects were stronger than the responses attributable to any one single mixture component.
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