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Title: Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

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 <10 min, 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 exploredmore » 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.« less

Authors:
 [1];  [2];  [3];  [3];  [1];  [1]; ORCiD logo [4];  [5];  [6];  [7];  [8]
  1. Univ. of New Mexico, Albuquerque, NM (United States)
  2. Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida (Mexico)
  3. Stremble Ventures LTD, Limassol (Cyprus)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. Loyola Univ. Medical Center, Chicago, IL (United States)
  6. Univ. of Pretoria, Pretoria (South Africa); Food and Agriculture Organization of the United Nations, Dar es Salaam (Tanzania)
  7. GAMA Therapeutics LLC, Mansfield, MA (United States)
  8. Univ. of Strasbourg, Strasbourg (France)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1565896
Report Number(s):
LA-UR-18-30736
Journal ID: ISSN 1664-3224
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Immunology
Additional Journal Information:
Journal Volume: 10; Journal ID: ISSN 1664-3224
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Biological Science; personalized prognostics; pathogenesis; infection; inflammation; pattern recognition-based visualization

Citation Formats

Rivas, Ariel L., Hoogesteijn, Almira L., Antoniades, Athos, Tomazou, Marios, Buranda, Tione, Perkins, Douglas J., Fair, Jeanne Marie, Durvasula, Ravi, Fasina, Folorunso O., Tegos, George P., and van Regenmortel, Marc H. V. Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data. United States: N. p., 2019. Web. doi:10.3389/fimmu.2019.01258.
Rivas, Ariel L., Hoogesteijn, Almira L., Antoniades, Athos, Tomazou, Marios, Buranda, Tione, Perkins, Douglas J., Fair, Jeanne Marie, Durvasula, Ravi, Fasina, Folorunso O., Tegos, George P., & van Regenmortel, Marc H. V. Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data. United States. doi:10.3389/fimmu.2019.01258.
Rivas, Ariel L., Hoogesteijn, Almira L., Antoniades, Athos, Tomazou, Marios, Buranda, Tione, Perkins, Douglas J., Fair, Jeanne Marie, Durvasula, Ravi, Fasina, Folorunso O., Tegos, George P., and van Regenmortel, Marc H. V. Wed . "Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data". United States. doi:10.3389/fimmu.2019.01258. https://www.osti.gov/servlets/purl/1565896.
@article{osti_1565896,
title = {Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data},
author = {Rivas, Ariel L. and Hoogesteijn, Almira L. and Antoniades, Athos and Tomazou, Marios and Buranda, Tione and Perkins, Douglas J. and Fair, Jeanne Marie and Durvasula, Ravi and Fasina, Folorunso O. and Tegos, George P. and van Regenmortel, Marc H. V.},
abstractNote = {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 <10 min, 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.},
doi = {10.3389/fimmu.2019.01258},
journal = {Frontiers in Immunology},
number = ,
volume = 10,
place = {United States},
year = {2019},
month = {6}
}

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