Assessing the dynamics and complexity of disease pathogenicity using 4-dimensional immunological data
- 1Center for Global Health, University of New Mexico, United States
- 2Centro de Investigacion y de Estudios Avanzados - Unidad Mérida, Mexico
- 3Stremble Ventures LTD, Cyprus
- 4Department of Pathology, University of New Mexico, United States
- 5Biosecurity and Public Health, Los Alamos National Laboratory (DOE), United States
- 6Loyola University Chicago, United States
- 7Food and Agriculture Organization of the United Nations (Tanzania), Tanzania
- 8GAMA Therapeutics LLC, United States
- 9Centre National de la Recherche Scientifique (CNRS), France
Investigating disease pathogenesis and personalized prognostics are critical biomedical priorities. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need to prognosticate specific outcomes, at personalized bases. Similarly, the rapid differentiation of several inflammatory phases matters in several syndromes. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in less than 10 minutes, 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 the ambiguity associated with data circularity and false results– 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.
Keywords: personalized prognosis, Infection, Inflammation, Pathogenesis, pattern recognition-based visualization
Received: 19 Jan 2019;
Accepted: 17 May 2019.
Edited by:José R. Mineo, Federal University of Uberlandia, Brazil
Reviewed by:Carlo J. Oliveira, Universidade Federal do Triângulo Mineiro, Brazil
Helioswilton Sales-Campos, Universidade Federal de Goiás, Brazil
Copyright: © 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). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Ariel L. Rivas, University of New Mexico, Center for Global Health, Albuquerque, United States, firstname.lastname@example.org
Dr. Marc H. Van Regenmortel, Centre National de la Recherche Scientifique (CNRS), Paris, France, email@example.com