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A general model of hormesis in biological systems and its application to pest management

Hormesis, a phenomenon whereby exposure to high levels of stressors is inhibitory but low (mild, sublethal and subtoxic) doses are stimulatory, challenges decision-making in the management of cancer, neurodegenerative diseases, nutrition and ecotoxicology. In the latter, increasing amounts of a pest...

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Autores principales: Tang, Sanyi, Liang, Juhua, Xiang, Changcheng, Xiao, Yanni, Wang, Xia, Wu, Jianhong, Li, Guoping, Cheke, Robert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731494/
https://www.ncbi.nlm.nih.gov/pubmed/31431187
http://dx.doi.org/10.1098/rsif.2019.0468
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author Tang, Sanyi
Liang, Juhua
Xiang, Changcheng
Xiao, Yanni
Wang, Xia
Wu, Jianhong
Li, Guoping
Cheke, Robert A.
author_facet Tang, Sanyi
Liang, Juhua
Xiang, Changcheng
Xiao, Yanni
Wang, Xia
Wu, Jianhong
Li, Guoping
Cheke, Robert A.
author_sort Tang, Sanyi
collection PubMed
description Hormesis, a phenomenon whereby exposure to high levels of stressors is inhibitory but low (mild, sublethal and subtoxic) doses are stimulatory, challenges decision-making in the management of cancer, neurodegenerative diseases, nutrition and ecotoxicology. In the latter, increasing amounts of a pesticide may lead to upsurges rather than declines of pests, ecological paradoxes that are difficult to predict. Using a novel re-formulation of the Ricker population equation, we show how interactions between intervention strengths and dose timings, dose–response functions and intrinsic factors can model such paradoxes and hormesis. A model with three critical parameters revealed hormetic biphasic dose and dose timing responses, either in a J-shape or an inverted U-shape, yielding a homeostatic change or a catastrophic shift and hormetic effects in many parameter regions. Such effects were enhanced by repeated pulses of low-level stimulations within one generation at different dose timings, thereby reducing threshold levels, maximum responses and inhibition. The model provides insights into the complex dynamics of such systems and a methodology for improved experimental design and analysis, with wide-reaching implications for understanding hormetic effects in ecology and in medical and veterinary treatment decision-making. We hypothesized that the dynamics of a discrete generation pest control system can be determined by various three-parameter spaces, some of which reveal the conditions for occurrence of hormesis, and confirmed this by fitting our model to both hormetic data from the literature and to a non-hormetic dataset on pesticidal control of mirid bugs in cotton.
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spelling pubmed-67314942019-09-09 A general model of hormesis in biological systems and its application to pest management Tang, Sanyi Liang, Juhua Xiang, Changcheng Xiao, Yanni Wang, Xia Wu, Jianhong Li, Guoping Cheke, Robert A. J R Soc Interface Life Sciences–Mathematics interface Hormesis, a phenomenon whereby exposure to high levels of stressors is inhibitory but low (mild, sublethal and subtoxic) doses are stimulatory, challenges decision-making in the management of cancer, neurodegenerative diseases, nutrition and ecotoxicology. In the latter, increasing amounts of a pesticide may lead to upsurges rather than declines of pests, ecological paradoxes that are difficult to predict. Using a novel re-formulation of the Ricker population equation, we show how interactions between intervention strengths and dose timings, dose–response functions and intrinsic factors can model such paradoxes and hormesis. A model with three critical parameters revealed hormetic biphasic dose and dose timing responses, either in a J-shape or an inverted U-shape, yielding a homeostatic change or a catastrophic shift and hormetic effects in many parameter regions. Such effects were enhanced by repeated pulses of low-level stimulations within one generation at different dose timings, thereby reducing threshold levels, maximum responses and inhibition. The model provides insights into the complex dynamics of such systems and a methodology for improved experimental design and analysis, with wide-reaching implications for understanding hormetic effects in ecology and in medical and veterinary treatment decision-making. We hypothesized that the dynamics of a discrete generation pest control system can be determined by various three-parameter spaces, some of which reveal the conditions for occurrence of hormesis, and confirmed this by fitting our model to both hormetic data from the literature and to a non-hormetic dataset on pesticidal control of mirid bugs in cotton. The Royal Society 2019-08 2019-08-21 /pmc/articles/PMC6731494/ /pubmed/31431187 http://dx.doi.org/10.1098/rsif.2019.0468 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Tang, Sanyi
Liang, Juhua
Xiang, Changcheng
Xiao, Yanni
Wang, Xia
Wu, Jianhong
Li, Guoping
Cheke, Robert A.
A general model of hormesis in biological systems and its application to pest management
title A general model of hormesis in biological systems and its application to pest management
title_full A general model of hormesis in biological systems and its application to pest management
title_fullStr A general model of hormesis in biological systems and its application to pest management
title_full_unstemmed A general model of hormesis in biological systems and its application to pest management
title_short A general model of hormesis in biological systems and its application to pest management
title_sort general model of hormesis in biological systems and its application to pest management
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731494/
https://www.ncbi.nlm.nih.gov/pubmed/31431187
http://dx.doi.org/10.1098/rsif.2019.0468
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