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Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue
BACKGROUND: Dengue causes considerable morbidity and mortality in Sri Lanka. Inflammatory mediators such as cytokines, contribute to its evolution from an asymptotic infection to severe forms of dengue. The majority of previous studies have analysed the association of individual cytokines with clini...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346240/ https://www.ncbi.nlm.nih.gov/pubmed/28284213 http://dx.doi.org/10.1186/s12918-017-0415-3 |
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author | Jayasundara, S. D. Pavithra Perera, S. S. N. Malavige, Gathsaurie Neelika Jayasinghe, Saroj |
author_facet | Jayasundara, S. D. Pavithra Perera, S. S. N. Malavige, Gathsaurie Neelika Jayasinghe, Saroj |
author_sort | Jayasundara, S. D. Pavithra |
collection | PubMed |
description | BACKGROUND: Dengue causes considerable morbidity and mortality in Sri Lanka. Inflammatory mediators such as cytokines, contribute to its evolution from an asymptotic infection to severe forms of dengue. The majority of previous studies have analysed the association of individual cytokines with clinical disease severity. In contrast, we view evolution to Dengue Haemorrhagic Fever as the behaviour of a complex dynamic system. We therefore, analyse the combined effect of multiple cytokines that interact dynamically with each other in order to generate a mathematical model to predict occurrence of Dengue Haemorrhagic Fever. We expect this to have predictive value in detecting severe cases and improve outcomes. Platelet activating factor (PAF), Sphingosine 1- Phosphate (S1P), IL-1β, TNFα and IL-10 are used as the parameters for the model. Hierarchical clustering is used to detect factors that correlated with each other. Their interactions are mapped using Fuzzy Logic mechanisms with the combination of modified Hamacher and OWA operators. Trapezoidal membership functions are developed for each of the cytokine parameters and the degree of unfavourability to attain Dengue Haemorrhagic Fever is measured. RESULTS: The accuracy of this model in predicting severity level of dengue is 71.43% at 96 h from the onset of illness, 85.00% at 108 h and 76.92% at 120 h. A region of ambiguity is detected in the model for the value range 0.36 to 0.51. Sensitivity analysis indicates that this is a robust mathematical model. CONCLUSIONS: The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients with high accuracy. However, this model would have to be further improved by including additional parameters and should be validated on other data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-017-0415-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5346240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53462402017-03-14 Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue Jayasundara, S. D. Pavithra Perera, S. S. N. Malavige, Gathsaurie Neelika Jayasinghe, Saroj BMC Syst Biol Research Article BACKGROUND: Dengue causes considerable morbidity and mortality in Sri Lanka. Inflammatory mediators such as cytokines, contribute to its evolution from an asymptotic infection to severe forms of dengue. The majority of previous studies have analysed the association of individual cytokines with clinical disease severity. In contrast, we view evolution to Dengue Haemorrhagic Fever as the behaviour of a complex dynamic system. We therefore, analyse the combined effect of multiple cytokines that interact dynamically with each other in order to generate a mathematical model to predict occurrence of Dengue Haemorrhagic Fever. We expect this to have predictive value in detecting severe cases and improve outcomes. Platelet activating factor (PAF), Sphingosine 1- Phosphate (S1P), IL-1β, TNFα and IL-10 are used as the parameters for the model. Hierarchical clustering is used to detect factors that correlated with each other. Their interactions are mapped using Fuzzy Logic mechanisms with the combination of modified Hamacher and OWA operators. Trapezoidal membership functions are developed for each of the cytokine parameters and the degree of unfavourability to attain Dengue Haemorrhagic Fever is measured. RESULTS: The accuracy of this model in predicting severity level of dengue is 71.43% at 96 h from the onset of illness, 85.00% at 108 h and 76.92% at 120 h. A region of ambiguity is detected in the model for the value range 0.36 to 0.51. Sensitivity analysis indicates that this is a robust mathematical model. CONCLUSIONS: The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients with high accuracy. However, this model would have to be further improved by including additional parameters and should be validated on other data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-017-0415-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-11 /pmc/articles/PMC5346240/ /pubmed/28284213 http://dx.doi.org/10.1186/s12918-017-0415-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Jayasundara, S. D. Pavithra Perera, S. S. N. Malavige, Gathsaurie Neelika Jayasinghe, Saroj Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title | Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title_full | Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title_fullStr | Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title_full_unstemmed | Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title_short | Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
title_sort | mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5346240/ https://www.ncbi.nlm.nih.gov/pubmed/28284213 http://dx.doi.org/10.1186/s12918-017-0415-3 |
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