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Toxicological interactions of pesticide mixtures: an update

Overview of attention for article published in Archives of Toxicology, August 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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1 policy source
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Citations

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210 Dimensions

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238 Mendeley
Title
Toxicological interactions of pesticide mixtures: an update
Published in
Archives of Toxicology, August 2017
DOI 10.1007/s00204-017-2043-5
Pubmed ID
Authors

Antonio F. Hernández, Fernando Gil, Marina Lacasaña

Abstract

Pesticides can interact with each other in various ways according to the compound itself and its chemical family, the dose and the targeted organs, leading to various effects. The term interaction means situations where some or all individual components of a mixture influence each other's toxicity and the joint effects may deviate from the additive predictions. The various mixture effects can be greatly determined by toxicokinetic and toxicodynamic factors involving metabolic pathways and cellular or molecular targets of individual pesticides, respectively. However, the complexity of toxicological interactions can lead to unpredictable effects of pesticide mixtures. Interactions on metabolic processes affecting the biotransformation of pesticides seem to be by far the most common mechanism of synergism. Moreover, the identification of pesticides responsible for synergistic interactions is an important issue for cumulative risk assessment. Cholinesterase inhibiting insecticides (organophosphates and N-methylcarbamates), triazole fungicides, triazine herbicides, and pyrethroid insecticides are overrepresented in the synergistic mixtures identified so far. Since the limited available empirical evidence suggests that synergisms at dietary exposure levels are rather rare, and experimentally occurred at unrealistic high concentrations, synergism cannot be predicted quantitatively on the basis of the toxicity of mixture components. The prediction of biological responses elicited by interaction of pesticides with each other (or with other chemicals) will benefit from using a systems toxicology approach. The identification of core features of pesticide mixtures at molecular level, such as gene expression profiles, could be helpful to assess or predict the occurrence of interactive effects giving rise to unpredicted responses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 238 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 238 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 13%
Student > Ph. D. Student 28 12%
Student > Bachelor 24 10%
Student > Master 23 10%
Student > Doctoral Student 22 9%
Other 39 16%
Unknown 71 30%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 14%
Environmental Science 31 13%
Pharmacology, Toxicology and Pharmaceutical Science 15 6%
Chemistry 15 6%
Biochemistry, Genetics and Molecular Biology 10 4%
Other 30 13%
Unknown 104 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2018.
All research outputs
#6,623,429
of 23,815,455 outputs
Outputs from Archives of Toxicology
#854
of 2,680 outputs
Outputs of similar age
#101,874
of 317,667 outputs
Outputs of similar age from Archives of Toxicology
#4
of 22 outputs
Altmetric has tracked 23,815,455 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 2,680 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,667 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.