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Depositordc.contributorRyu, Kyungsuk
Funderdc.contributor.otherUniversity of Edinburghen_UK
Data Creatordc.creatorRyu, Kyungsuk
Data Creatordc.creatorStefan, Melanie
Data Creatordc.creatorFitzpatrick, Richard
Date Accessioneddc.date.accessioned2018-05-10T16:46:01Z
Date Availabledc.date.available2018-05-10T16:46:01Z
Citationdc.identifier.citationRyu, Kyungsuk; Stefan, Melanie; Fitzpatrick, Richard. (2018). Computational Modelling of malaria diagnostic test, [dataset]. University of Edinburgh. Biomedical Sciences. https://doi.org/10.7488/ds/2347.en
Persistent Identifierdc.identifier.urihttps://hdl.handle.net/10283/3078
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2347
Dataset Description (abstract)dc.description.abstractEarly diagnosis of malaria is important for the prevention of malaria disease outbreak and providing an affected person with appropriate treatment early on. In malaria-endemic areas, where resources and access to health care are limited, the malaria rapid diagnostic test (mRDT) has widely been used because it is easy to use, cheap and fast. However, mRDT is prone to several limitations, such as inconsistent diagnostic specificity and sensitivity between different brands. Here, we build the first computational chemical kinetic model of mRDT which allows simulation of different parameter sets by using complex pathway simulator software (COPASI). Our model successfully reproduces in-vivo experimental results, confirming its validity. Using our model, we explore the principle of mRDT and demonstrate the potential for enhancing the diagnostic range of mRDT through parameter optimisation. Moreover, we use our model to investigate the prozone effect, the false negative response resulting from hyper-parasitaemia. This has previously been observed when using only the mRDT against Plasmodium histidine-rich protein-2 (pHRP-2) and not Plasmodium lactate dehydrogenase (pLDH). Our model can reproduce this result and is the first to provide a mechanistic explanation for why test differs in their susceptibility to the prozone effect. N.B. The COPAS software is open source and may be downloaded from: http://copasi.org/en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. Biomedical Sciencesen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyKyungsuk Ryu and Melanie I. Stefan (2018), Computational modelling of malaria diagnostic test [Dissertation]en_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Titledc.titleComputational Modelling of malaria diagnostic testen_UK
Typedc.typedataseten_UK

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  • Stefan Lab
    The Stefan Lab is interested in the computational modelling of processes related to learning and memory across scales.

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