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Assessing the Effect of Mycotoxin Combinations: Which Mathematical Model Is (the Most) Appropriate?

Authors: Domagoj Kifer; Daniela Jakšić; Maja Šegvić Klarić;

Assessing the Effect of Mycotoxin Combinations: Which Mathematical Model Is (the Most) Appropriate?

Abstract

In the past decades, many studies have examined the nature of the interaction between mycotoxins in biological models classifying interaction effects as antagonisms, additive effects, or synergisms based on a comparison of the observed effect with the expected effect of combination. Among several described mathematical models, the arithmetic definition of additivity and factorial analysis of variance were the most commonly used in mycotoxicology. These models are incorrectly based on the assumption that mycotoxin dose-effect curves are linear. More appropriate mathematical models for assessing mycotoxin interactions include Bliss independence, Loewe’s additivity law, combination index, and isobologram analysis, Chou-Talalays median-effect approach, response surface, code for the identification of synergism numerically efficient (CISNE) and MixLow method. However, it seems that neither model is ideal. This review discusses the advantages and disadvantages of these mathematical models.

Keywords

mycotoxin interaction ; Loewe additivity ; combination index ; isobologram ; Chou-Talalay method ; MixLow, R, Review, Mycotoxins, mycotoxin interaction, Models, Biological, Loewe additivity, isobologram, Chou-Talalay method, loewe additivity, combination index, chou-talalay method, mixlow, Medicine, Animals, Humans, Drug Interactions, MixLow

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
40
Top 10%
Top 10%
Top 10%
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gold