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Predicting low-concentration effects of pesticides
We present a model to identify the effects of low toxicant concentrations. Due to inadequate models, such effects have so far often been misinterpreted as random variability. Instead, a tri-phasic relationship describes the effects of a toxicant when a broad range of concentrations is assessed: i) a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813311/ https://www.ncbi.nlm.nih.gov/pubmed/31649283 http://dx.doi.org/10.1038/s41598-019-51645-4 |
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author | Liess, Matthias Henz, Sebastian Knillmann, Saskia |
author_facet | Liess, Matthias Henz, Sebastian Knillmann, Saskia |
author_sort | Liess, Matthias |
collection | PubMed |
description | We present a model to identify the effects of low toxicant concentrations. Due to inadequate models, such effects have so far often been misinterpreted as random variability. Instead, a tri-phasic relationship describes the effects of a toxicant when a broad range of concentrations is assessed: i) at high concentrations where substantial mortality occurs (LC(50)), we confirmed the traditional sigmoidal response curve (ii) at low concentrations about 10 times below the LC(50), we identified higher survival than previously modelled, and (iii) at ultra-low concentrations starting at around 100 times below the LC(50), higher mortality than previously modelled. This suggests that individuals benefit from low toxicant stress. Accordingly, we postulate that in the absence of external toxicant stress individuals are affected by an internal “System Stress” (SyS) and that SyS is reduced with increasing strength of toxicant stress. We show that the observed tri-phasic concentration-effect relationship can be modelled on the basis of this approach. Here we revealed that toxicant-related effects (LC(5)) occurred at remarkably low concentrations, 3 to 4 orders of magnitude below those concentrations inducing strong effects (LC(50)). Thus, the EC(x-SyS) model presented allows us to attribute ultra-low toxicant concentrations to their effects on individuals. This information will contribute to performing a more realistic environmental and human risk assessment. |
format | Online Article Text |
id | pubmed-6813311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68133112019-10-30 Predicting low-concentration effects of pesticides Liess, Matthias Henz, Sebastian Knillmann, Saskia Sci Rep Article We present a model to identify the effects of low toxicant concentrations. Due to inadequate models, such effects have so far often been misinterpreted as random variability. Instead, a tri-phasic relationship describes the effects of a toxicant when a broad range of concentrations is assessed: i) at high concentrations where substantial mortality occurs (LC(50)), we confirmed the traditional sigmoidal response curve (ii) at low concentrations about 10 times below the LC(50), we identified higher survival than previously modelled, and (iii) at ultra-low concentrations starting at around 100 times below the LC(50), higher mortality than previously modelled. This suggests that individuals benefit from low toxicant stress. Accordingly, we postulate that in the absence of external toxicant stress individuals are affected by an internal “System Stress” (SyS) and that SyS is reduced with increasing strength of toxicant stress. We show that the observed tri-phasic concentration-effect relationship can be modelled on the basis of this approach. Here we revealed that toxicant-related effects (LC(5)) occurred at remarkably low concentrations, 3 to 4 orders of magnitude below those concentrations inducing strong effects (LC(50)). Thus, the EC(x-SyS) model presented allows us to attribute ultra-low toxicant concentrations to their effects on individuals. This information will contribute to performing a more realistic environmental and human risk assessment. Nature Publishing Group UK 2019-10-24 /pmc/articles/PMC6813311/ /pubmed/31649283 http://dx.doi.org/10.1038/s41598-019-51645-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liess, Matthias Henz, Sebastian Knillmann, Saskia Predicting low-concentration effects of pesticides |
title | Predicting low-concentration effects of pesticides |
title_full | Predicting low-concentration effects of pesticides |
title_fullStr | Predicting low-concentration effects of pesticides |
title_full_unstemmed | Predicting low-concentration effects of pesticides |
title_short | Predicting low-concentration effects of pesticides |
title_sort | predicting low-concentration effects of pesticides |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813311/ https://www.ncbi.nlm.nih.gov/pubmed/31649283 http://dx.doi.org/10.1038/s41598-019-51645-4 |
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