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dc.contributor.author | Rivas, Ariel L.![]() |
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dc.contributor.author | Hoogesteijn, Almira L.![]() |
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dc.contributor.author | Antoniades, Athos![]() |
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dc.contributor.author | Tomazou, Marios![]() |
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dc.contributor.author | Buranda, Tione![]() |
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dc.contributor.author | Perkins, Douglas J.![]() |
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dc.contributor.author | Fair, Jeanne M.![]() |
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dc.contributor.author | Durvasula, Ravi![]() |
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dc.contributor.author | Fasina, Folorunso Oludayo![]() |
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dc.contributor.author | Tegos, George P.![]() |
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dc.contributor.author | Van Regenmortel, Marc H.V.![]() |
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dc.date.accessioned | 2020-08-18T07:03:01Z | |
dc.date.available | 2020-08-18T07:03:01Z | |
dc.date.issued | 2019-06-12 | |
dc.description.abstract | Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10min, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted. | en_ZA |
dc.description.department | Veterinary Tropical Diseases | en_ZA |
dc.description.librarian | am2020 | en_ZA |
dc.description.sponsorship | Conacyt of Mexico (Consejo Nacional de Ciencia y Tecnología | en_ZA |
dc.description.uri | http://www.frontiersin.org/Immunology | en_ZA |
dc.identifier.citation | Rivas AL, Hoogesteijn AL, Antoniades A, Tomazou M, Buranda T, Perkins DJ, Fair JM, Durvasula R, Fasina FO, Tegos GP and van Regenmortel MHV (2019) Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data. Frontiers in Immunology 10:1258. DOI: 10.3389/fimmu.2019.01258 | en_ZA |
dc.identifier.issn | 10.3389/fimmu.2019.01258 | |
dc.identifier.issn | 1664-3224 (online) | |
dc.identifier.uri | http://hdl.handle.net/2263/75781 | |
dc.language.iso | en | en_ZA |
dc.publisher | Frontiers Media | en_ZA |
dc.rights | © 2019 Rivas, Hoogesteijn, Antoniades, Tomazou, Buranda, Perkins, Fair, Durvasula, Fasina, Tegos and van Regenmortel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). | en_ZA |
dc.subject | Personalized prognostics | en_ZA |
dc.subject | Pathogenesis | en_ZA |
dc.subject | Infection | en_ZA |
dc.subject | Inflammation | en_ZA |
dc.subject | Pattern recognition-based method (PRM) | en_ZA |
dc.title | Assessing the dynamics and complexity of disease pathogenicity using 4-dimensional immunological data | en_ZA |
dc.type | Article | en_ZA |