| Autor: |
Rivas AL; School of Medicine, Center for Global Health-Division of Infectious Diseases, University of New Mexico, Albuquerque, NM, United States., Hoogesteijn AL; Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, Mexico., Antoniades A; Stremble Ventures LTD, Limassol, Cyprus., Tomazou M; Stremble Ventures LTD, Limassol, Cyprus., Buranda T; Department of Pathology, School of Medicine, University of New Mexico, Albuquerque, NM, United States., Perkins DJ; School of Medicine, Center for Global Health-Division of Infectious Diseases, University of New Mexico, Albuquerque, NM, United States., Fair JM; Biosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, United States., Durvasula R; Loyola University Medical Center, Chicago, IL, United States., Fasina FO; Department of Veterinary Tropical Diseases, University of Pretoria, Pretoria, South Africa.; Food and Agriculture Organization of the United Nations, Dar es Salaam, Tanzania., Tegos GP; GAMA Therapeutics LLC, Mansfield, MA, United States., van Regenmortel MHV; Centre National de la Recherche Scientifique (CNRS), School of Biotechnology, University of Strasbourg, Strasbourg, France. |
| Abstrakt: |
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. |
