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Rivas AL, Smith SD. Editorial: Interdisciplinary approaches in veterinary sciences after COVID-19. Front Vet Sci 2024; 11:1361813. [PMID: 38292464 PMCID: PMC10824909 DOI: 10.3389/fvets.2024.1361813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 01/08/2024] [Indexed: 02/01/2024] Open
Affiliation(s)
- Ariel L. Rivas
- Center for Global Health, Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, 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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Rivas AL, van Regenmortel MHV. COVID-19 related interdisciplinary methods: Preventing errors and detecting research opportunities. Methods 2021; 195:3-14. [PMID: 34029715 PMCID: PMC8545872 DOI: 10.1016/j.ymeth.2021.05.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/12/2022] Open
Abstract
More than 130,000 peer-reviewed studies have been published within one year after COVID-19 emerged in many countries. This large and rapidly growing field may overwhelm the synthesizing abilities of both researchers and policy-makers. To provide a sinopsis, prevent errors, and detect cognitive gaps that may require interdisciplinary research methods, the literature on COVID-19 is summarized, twice. The overall purpose of this study is to generate a dialogue meant to explain the genesis of and/or find remedies for omissions and contradictions. The first review starts in Biology and ends in Policy. Policy is chosen as a destination because it is the setting where cognitive integration must occur. The second review follows the opposite path: it begins with stated policies on COVID-19 and then their assumptions and disciplinary relationships are identified. The purpose of this interdisciplinary method on methods is to yield a relational and explanatory view of the field -one strategy likely to be incomplete but usable when large bodies of literature need to be rapidly summarized. These reviews identify nine inter-related problems, research needs, or omissions, namely: (1) nation-wide, geo-referenced, epidemiological data collection systems (open to and monitored by the public); (2) metrics meant to detect non-symptomatic cases -e.g., test positivity-; (3) cost-benefit oriented methods, which should demonstrate they detect silent viral spreaders even with limited testing; (4) new personalized tests that inform on biological functions and disease correlates, such as cell-mediated immunity, co-morbidities, and immuno-suppression; (5) factors that influence vaccine effectiveness; (6) economic predictions that consider the long-term consequences likely to follow epidemics that growth exponentially; (7) the errors induced by self-limiting and/or implausible paradigms, such as binary and reductionist approaches; (8) new governance models that emphasize problem-solving skills, social participation, and the use of scientific knowledge; and (9) new educational programs that utilize visual aids and audience-specific communication strategies. The analysis indicates that, to optimally address these problems, disciplinary and social integration is needed. By asking what is/are the potential cause(s) and consequence(s) of each issue, this methodology generates visualizations that reveal possible relationships as well as omissions and contradictions. While inherently limited in scope and likely to become obsolete, these shortcomings are avoided when this 'method on methods' is frequently practiced. Open-ended, inter-/trans-disciplinary perspectives and broad social participation may help researchers and citizens to construct, de-construct, and re-construct COVID-19 related research.
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Affiliation(s)
- Ariel L Rivas
- Center for Global Health, School of Medicine, University of New Mexico, Albuquerque, NM, United States.
| | - Marc H V van Regenmortel
- University of Vienna, Austria; and Higher School of Biotechnology, University of Strasbourg, and French National Research Center, France
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Rivas AL, van Regenmortel MHV. Toward interdisciplinary methods appropriate for optimal epidemic control. Methods 2021; 195:1-2. [PMID: 34543748 PMCID: PMC8449515 DOI: 10.1016/j.ymeth.2021.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Affiliation(s)
- Ariel L Rivas
- Center for Global Health, School of Medicine, University of New Mexico, Albuquerque, NM, United States.
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Rivas AL, Hoogesteijn AL. Biologically grounded scientific methods: The challenges ahead for combating epidemics. Methods 2021; 195:113-119. [PMID: 34492300 PMCID: PMC8423586 DOI: 10.1016/j.ymeth.2021.09.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/26/2021] [Accepted: 09/02/2021] [Indexed: 01/12/2023] Open
Abstract
The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.
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Affiliation(s)
| | - Almira L Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Merida, Mexico.
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Van Regenmortel MHV. Design in biology and rational design in vaccinology: A conceptual analysis. Methods 2021; 195:120-127. [PMID: 34352372 DOI: 10.1016/j.ymeth.2021.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/20/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022] Open
Abstract
This review discusses the philosophical foundations of what used to be called "the scientific method" and is nowadays often known as the scientific attitude. It used to be believed that scientific theories and methods aimed at the truth especially in the case of physics, chemistry and astronomy because these sciences were able to develop numerous scientific laws that made it possible to understand and predict many physical phenomena. The situation is different in the case of the biological sciences which deal with highly complex living organisms made up of huge numbers of constituents that undergo continuous dynamic processes; this leads to novel emergent properties in organisms that cannot be predicted because they are not present in the constituents before they have interacted with each other. This is one of the reasons why there are no universal scientific laws in biology. Furthermore, all scientific theories can only achieve a restricted level of predictive success because they remain valid only under the limited range of conditions that were used for establishing the theory' in the first place. Many theories that used to be accepted were subsequently shown to be false, demonstrating that scientific theories always remain tentative and can never be proven beyond and doubt. It is ironical that as scientists have finally accepted that approximate truths are perfectly adequate and that absolute truth is an illusion, a new irrational sociological phenomenon called Post-Truth conveyed by social media, the Internet and fake news has developed in the Western world that is convincing millions of people that truth simply does not exist. Misleading information is circulated with the intention to deceive and science denialism is promoted by denying the remarkable achievements of science and technology during the last centuries. Although the concept of intentional design is widely used to describe the methods that biologists use to make discoveries and inventions, it will be argued that the term is not appropriate for explaining the appearance of life on our planet nor for describing the scientific creativity of scientific investigators. The term rational for describing the development of new vaccines is also unjustified. Because the analysis of the COVID-19 pandemic requires contributions from biomedical and psycho-socioeconomic sciences, one scientific method alone would be insufficient for combatting the pandemic.
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Rivas AL, Febles JL, Smith SD, Hoogesteijn AL, Tegos GP, Fasina FO, Hittner JB. Early network properties of the COVID-19 pandemic - The Chinese scenario. Int J Infect Dis 2020; 96:519-523. [PMID: 32470603 PMCID: PMC7250076 DOI: 10.1016/j.ijid.2020.05.049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES To control epidemics, sites more affected by mortality should be identified. METHODS Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.
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Affiliation(s)
- Ariel L Rivas
- Center for Global Health, Department of Internal Medicine, Medical School, University of New Mexico, Albuquerque, USA
| | - José L Febles
- Department of Human Ecology, CINVESTAV-IPN, Mérida, Mexico
| | - Stephen D Smith
- Institute for Resource Information Science, College of Agriculture, Cornell University, Ithaca, USA
| | | | | | - Folorunso O Fasina
- Food and Agriculture Organization, Dar es Salam, Tanzania & Department of Veterinary Tropical Diseases, University of Pretoria, South Africa.
| | - James B Hittner
- Department of Psychology, College of Charleston, Charleston, USA
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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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