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Kempaiah P, Libertin CR, Chitale RA, Naeyma I, Pleqi V, Sheele JM, Iandiorio MJ, Hoogesteijn AL, Caulfield TR, Rivas AL. Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes. Biomedicines 2024; 12:871. [PMID: 38672225 PMCID: PMC11048687 DOI: 10.3390/biomedicines12040871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 03/14/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND METHODS Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. RESULTS While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.
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Affiliation(s)
- Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Rohit A. Chitale
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Islam Naeyma
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
| | - Vasili Pleqi
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL 32224, USA; (P.K.); (V.P.)
| | | | - Michelle J. Iandiorio
- Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA;
| | | | - Thomas R. Caulfield
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA; (I.N.); (T.R.C.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ariel L. Rivas
- Center for Global Health, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87131, USA
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Zhang G, Shao F, Yuan W, Wu J, Qi X, Gao J, Shao R, Tang Z, Wang T. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers. Eur J Med Res 2024; 29:156. [PMID: 38448999 PMCID: PMC10918942 DOI: 10.1186/s40001-024-01756-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/28/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND This study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis. METHODS We enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model's output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality. RESULTS 3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55-77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model. CONCLUSIONS This study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.
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Affiliation(s)
- Guyu Zhang
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Fei Shao
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Wei Yuan
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Junyuan Wu
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Xuan Qi
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Jie Gao
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Rui Shao
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China
| | - Ziren Tang
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China.
| | - Tao Wang
- Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing, 100020, China.
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Satashia PH, Franco PM, Rivas AL, Isha S, Hanson A, Narra SA, Singh K, Jenkins A, Bhattacharyya A, Guru P, Chaudhary S, Kiley S, Shapiro A, Martin A, Thomas M, Sareyyupoglu B, Libertin CR, Sanghavi DK. From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets. Front Med (Lausanne) 2023; 10:1240426. [PMID: 38020180 PMCID: PMC10664024 DOI: 10.3389/fmed.2023.1240426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery. Materials and methods Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources. Results Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission. Discussion/conclusion Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.
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Affiliation(s)
| | - Pablo Moreno Franco
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Ariel L. Rivas
- Center for Global Health-Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - Shahin Isha
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Abby Hanson
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sai Abhishek Narra
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Kawaljeet Singh
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Anna Jenkins
- Mayo Clinic Alix School of Medicine, Jacksonville, FL, United States
| | | | - Pramod Guru
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sanjay Chaudhary
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sean Kiley
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Anna Shapiro
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Archer Martin
- Division of Cardiovascular and Thoracic Anesthesiology, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Mathew Thomas
- Department of Cardiothoracic Surgery, Mayo Clinic, Jacksonville, FL, United States
| | - Basar Sareyyupoglu
- Department of Cardiothoracic Surgery, Mayo Clinic, Jacksonville, FL, United States
| | - Claudia R. Libertin
- Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States
| | - Devang K. Sanghavi
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
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Libertin CR, Kempaiah P, Gupta Y, Fair JM, van Regenmortel MHV, Antoniades A, Rivas AL, Hoogesteijn AL. Data structuring may prevent ambiguity and improve personalized medical prognosis. Mol Aspects Med 2022; 91:101142. [PMID: 36116999 DOI: 10.1016/j.mam.2022.101142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 01/17/2023]
Abstract
Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.
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Affiliation(s)
- Claudia R Libertin
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Yash Gupta
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Jeanne M Fair
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Marc H V van Regenmortel
- School of Biotechnology, Centre National de la Recherche Scientifique (CNRS), University of Strasbourg, France
| | | | - Ariel L Rivas
- Center for Global Health-Division of Infectious Diseases, School of Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Almira L Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, Yucatán, Mexico
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Verma JS, Libertin CR, Gupta Y, Khanna G, Kumar R, Arora BS, Krishna L, Fasina FO, Hittner JB, Antoniades A, van Regenmortel MHV, Durvasula R, Kempaiah P, Rivas AL. Multi-Cellular Immunological Interactions Associated With COVID-19 Infections. Front Immunol 2022; 13:794006. [PMID: 35281033 PMCID: PMC8913044 DOI: 10.3389/fimmu.2022.794006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.
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Affiliation(s)
- Jitender S. Verma
- Central Institute of Orthopaedics, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
- *Correspondence: Jitender S. Verma, ; Prakasha Kempaiah, ; Ariel L. Rivas,
| | | | - Yash Gupta
- Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States
| | - Geetika Khanna
- Central Institute of Orthopaedics, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Rohit Kumar
- Respiratory Medicine, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Balvinder S. Arora
- Department of Microbiology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Loveneesh Krishna
- Central Institute of Orthopaedics, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Folorunso O. Fasina
- Food and Agriculture Organization of the United Nations, Dar es Salaam, Tanzania
- Department of Veterinary Tropical Diseases, University of Pretoria, Pretoria, South Africa
| | - James B. Hittner
- Psychology, College of Charleston, Charleston, SC, United States
| | | | - Marc H. V. van Regenmortel
- Medical University of Vienna, Vienna, Austria
- Higher School of Biotechnology, University of Strasbourg, Strasbourg, France
| | - Ravi Durvasula
- Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States
| | - Prakasha Kempaiah
- Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States
- *Correspondence: Jitender S. Verma, ; Prakasha Kempaiah, ; Ariel L. Rivas,
| | - Ariel L. Rivas
- Center for Global Health-Division of Infectious Diseases, School of Medicine, University of New Mexico, Albuquerque, NM, United States
- *Correspondence: Jitender S. Verma, ; Prakasha Kempaiah, ; Ariel L. Rivas,
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Rivas AL, Hoogesteijn AL, Antoniades A, Tomazou M, Buranda T, Perkins DJ, Fair JM, Durvasula R, Fasina FO, Tegos GP, van Regenmortel MHV. Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data. Front Immunol 2019; 10:1258. [PMID: 31249569 PMCID: PMC6582751 DOI: 10.3389/fimmu.2019.01258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 05/17/2019] [Indexed: 02/05/2023] Open
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 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.
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Affiliation(s)
- Ariel L. Rivas
- School of Medicine, Center for Global Health-Division of Infectious Diseases, University of New Mexico, Albuquerque, NM, United States
- *Correspondence: Ariel L. Rivas
| | - Almira L. Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, Mexico
| | | | | | - Tione Buranda
- Department of Pathology, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - Douglas J. Perkins
- School of Medicine, Center for Global Health-Division of Infectious Diseases, University of New Mexico, Albuquerque, NM, United States
| | - Jeanne M. Fair
- Biosecurity and Public Health, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Ravi Durvasula
- Loyola University Medical Center, Chicago, IL, United States
| | - Folorunso O. Fasina
- Department of Veterinary Tropical Diseases, University of Pretoria, Pretoria, South Africa
- Food and Agriculture Organization of the United Nations, Dar es Salaam, Tanzania
| | | | - Marc H. V. van Regenmortel
- Centre National de la Recherche Scientifique (CNRS), School of Biotechnology, University of Strasbourg, Strasbourg, France
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7
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Rivas AL, Leitner G, Jankowski MD, Hoogesteijn AL, Iandiorio MJ, Chatzipanagiotou S, Ioannidis A, Blum SE, Piccinini R, Antoniades A, Fazio JC, Apidianakis Y, Fair JM, Van Regenmortel MHV. Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases. Front Immunol 2017; 8:612. [PMID: 28620378 PMCID: PMC5449438 DOI: 10.3389/fimmu.2017.00612] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 05/09/2017] [Indexed: 12/22/2022] Open
Abstract
Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.
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Affiliation(s)
- Ariel L. Rivas
- Center for Global Health, Division of Infectious Diseases, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - Gabriel Leitner
- National Mastitis Center, Kimron Veterinary Institute, Bet Dagan, Israel
| | - Mark D. Jankowski
- Environmental Assessment, U.S. Environmental Protection Agency, Seattle, WA, United States
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, United States
| | - Almira L. Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, México
| | - Michelle J. Iandiorio
- Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - Stylianos Chatzipanagiotou
- Department of Biopathology and Clinical Microbiology, Aeginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anastasios Ioannidis
- Department of Nursing, Faculty of Human Movement and Quality of Life Sciences, University of Peloponnese, Sparta, Greece
| | - Shlomo E. Blum
- National Mastitis Center, Kimron Veterinary Institute, Bet Dagan, Israel
| | - Renata Piccinini
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Athos Antoniades
- Department of Computer Science, University of Cyprus, Nicosia, Cyprus
| | - Jane C. Fazio
- Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | | | - Jeanne M. Fair
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM, United States
| | - Marc H. V. Van Regenmortel
- School of Biotechnology, Centre National de la Recherche Scientifique (CNRS), University of Strasbourg, Strasbourg, France
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