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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., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Author AA was employed by the company Stremble Ventures, LTD. Author AR is a co-inventor of the temporary guides used to recognize data patterns (European Union patent number 2959295, US patent number 10,429,389 B2). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Verma, Libertin, Gupta, Khanna, Kumar, Arora, Krishna, Fasina, Hittner, Antoniades, van Regenmortel, Durvasula, Kempaiah and Rivas.)

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., (Copyright © 2021. Published by Elsevier Inc.)

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The known polyspecificity of antibodies, which is crucial for efficient immune response, is determined by the conformational flexibility and intrinsic disorder encoded in local peculiarities of the amino acid sequence of antibodies within or in the vicinity of their complementarity determining regions. Similarly, epitopes represent fuzzy binding sites, which are also characterized by local structural flexibility. Existing data suggest that the efficient interactions between antigens and antibodies rely on the conformational mobility and some disorder of their binding sites and therefore can be relatively well described by the "flexible lock - adjustable key" model, whereas both, extreme order (rigid lock-and-key) and extreme disorder (viral shape-shifters) are not compatible with the efficient antigen-antibody interactions and are not present in immune interactions.

This review argues that the three popular concepts of design, rationality and reductionism, which guided vaccine research for many years, actually contributed to the inability of vaccinologists to develop an effective HIV vaccine. The strong goal-directed intentionality inherent in the concept of design together with excessive confidence in the power of rational thinking convinced investigators that the accumulated structural knowledge on HIV epitopes, derived from crystallographic studies of complexes of neutralizing antibodies bound to HIV Env epitopes, would allow them to rationally design complementary immunogens capable of inducing anti-HIV protective antibodies. This strategy failed because it was not appreciated that the structures observed in epitope-paratope crystallographic complexes result from mutually induced fit between the two partners and do not represent structures present in the free disordered molecules before they had interacted. In addition, reductionist thinking led investigators to accept that biology could be reduced to chemistry, and this made them neglect the fundamental difference between chemical antigenicity and biological immunogenicity. As a result, they did not investigate which inherent constituents of immune systems controlled the induction of protective antibodies and focused instead only on the steric complementarity that exists between bound epitopes and paratopes.

In addition to the well-established sense-antisense complementarity abundantly present in the nucleic acid world and serving as a basic principle of the specific double-helical structure of DNA, production of mRNA, and genetic code-based biosynthesis of proteins, sense-antisense complementarity is also present in proteins, where sense and antisense peptides were shown to interact with each other with increased probability. In nucleic acids, sense-antisense complementarity is achieved via the Watson-Crick complementarity of the base pairs or nucleotide pairing. In proteins, the complementarity between sense and antisense peptides depends on a specific hydropathic pattern, where codons for hydrophilic and hydrophobic amino acids in a sense peptide are complemented by the codons for hydrophobic and hydrophilic amino acids in its antisense counterpart. We are showing here that in addition to this pattern of the complementary hydrophobicity, sense and antisense peptides are characterized by the complementary order-disorder patterns and show complementarity in sequence distribution of their disorder-based interaction sites. We also discuss how this order-disorder complementarity can be related to protein evolution., (© 2020 John Wiley & Sons Ltd.)

Properties useful for defining virus species are phenotypic properties of viruses that can be altered by a few mutations. Such properties include the natural host range, cell and tissue tropism, symptomatology, pathogenicity and mode of transmission. All these properties are not necessarily present in identical form in all the members of a species; therefore, a virus species is a polythetic class of viruses defined by a variable combination of several properties rather than by a single conserved property present in all the members of the species. This review will discuss current controversies about what virus species actually are as well as which names should be given to them. It will be emphasized that most species-defining properties are so-called relational properties that arise because viruses necessarily interact with biological partners such as vectors, hosts and immune systems. Although these relational properties are of utmost importance to laboratory and clinical virologists, they remain unknown if only the viral genome is available and the relational partners of the virus have not yet been identified. Since the International Committee on Taxonomy of Viruses (ICTV) in 2013 ratified a new definition of virus species, which no longer accepts that species are polythetic classes but instead are monophyletic groups, the implications of this new definition for viral taxonomy and nomenclature will be analyzed. In my private capacity, I also make the following recommendations regarding current debates on proposed new names for virus species as well as on the feasibility of assigning viral sequences found in metagenomic databases to individual species taxa in the current ICTV classification. 1) The ICTV should abandon the current rule that the names of virus species (for instance Measles virus) should differ from the virus name (measles virus) only by typography. 2) Non-Latinized binomial species names based on familiar virus and genus names should become the norm. This would obviate the need to create about 5000 hard-to-memorize Latinized species names. 3) Virus species are defined not by the intrinsic properties of virions and viral genomes but by the relational properties of viruses that arise from their interactions with host and vector partners. Since the hosts and vectors associated with nearly all viral sequences found in metagenomic databases are unknown, the phenotypic properties of the putative viruses also remain unknown, and these viral sequences cannot be allocated to established species in the ICTV classification.

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.

A common logical confusion is prevalent in the whole of biology, namely that biological species are viewed both as an abstract category in an hierarchical classification and as a concrete kind of organism. This is partly due to the fact that the vast majority of living organisms do not have common names that differ from the Latin name of the species to which the organism belongs. However, it is somewhat astonishing that the same confusion exists in virology since every virus has a common name, different from the species name to which the virus belongs, which could be used to refer to the infectious viral entity as a concrete material object. The original 1991 ICTV definition of virus species stated that a virus species is a polythetic class of viruses and thus that a species is a class, namely a conceptual construction of the mind and not a physical, real object located in space and time. In 2013, the ICTV redefined a virus species no longer as a class but as a material object consisting of a monophyletic group of viruses that were all physically part of the species. This new definition is reminiscent of an earlier school of thought known as bionominalism which considered species to be concrete individuals rather than classes. Both bionominalism and the new ICTV definition are based on the logical fallacy of reification which treats abstractions such as classes as if they were concrete physical entities. The implications of this new ontology of virus species for virus taxonomy and for the possibility of incorporating nucleotide metagenomic sequences in the current ICTV classification is discussed.

Hypotheses and theories are essential constituents of the scientific method. Many vaccinologists are unaware that the problems they try to solve are mostly inverse problems that consist in imagining what could bring about a desired outcome. An inverse problem starts with the result and tries to guess what are the multiple causes that could have produced it. Compared to the usual direct scientific problems that start with the causes and derive or calculate the results using deductive reasoning and known mechanisms, solving an inverse problem uses a less reliable inductive approach and requires the development of a theoretical model that may have different solutions or none at all. Unsuccessful attempts to solve inverse problems in HIV vaccinology by reductionist methods, systems biology and structure-based reverse vaccinology are described. The popular strategy known as rational vaccine design is unable to solve the multiple inverse problems faced by HIV vaccine developers. The term "rational" is derived from "rational drug design" which uses the 3D structure of a biological target for designing molecules that will selectively bind to it and inhibit its biological activity. In vaccine design, however, the word "rational" simply means that the investigator is concentrating on parts of the system for which molecular information is available. The economist and Nobel laureate Herbert Simon introduced the concept of "bounded rationality" to explain why the complexity of the world economic system makes it impossible, for instance, to predict an event like the financial crash of 2007-2008. Humans always operate under unavoidable constraints such as insufficient information, a limited capacity to process huge amounts of data and a limited amount of time available to reach a decision. Such limitations always prevent us from achieving the complete understanding and optimization of a complex system that would be needed to achieve a truly rational design process. This is why the complexity of the human immune system prevents us from rationally designing an HIV vaccine by solving inverse problems.

Virus classification deals with conceptual species classes that have viruses as their members. A virus species cannot be described but can only be defined by listing certain species-defining properties of its member. However, it is not possible to define a virus species by using a single species-defining property. The new 2013 official definition of virus species is not appropriate because it applies equally to virus genera. A nucleotide motif is a chemical part of a viral genome and is not a species-defining property that could be used for establishing new virus species. A virus classification based solely on nucleotide sequences is a classification of viral genomes and not of viruses. The variable distribution of species-defining properties of a polythetic species class is not itself a single common property of all the members of the class, since this would lead to the paradox that every polythetic class is also a monothetic one., (© 2018 Elsevier Inc. All rights reserved.)

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.

It is commonly assumed that neutralizing Mabs that bind to the HIV-1 Env glycoprotein are more specific reagents than anti-HIV-1 polyclonal antisera and that knowledge of the structure of these Mabs facilitates the rational design of effective HIV-1 vaccine immunogens. However, after more than ten years of unsuccessful experimentation using the structure-based reverse vaccinology approach, it is now evident that it is not possible to infer from the structure of neutralizing Mabs which HIV immunogens induced their formation nor which vaccine immunogens will elicit similar Abs in an immunized host. The use of Mabs for developing an HIV-1 vaccine was counterproductive because it overlooked the fact that the apparent specificity of a Mab very much depends on the selection procedure used to obtain it and also did not take into account that an antibody is never monospecific for a single epitope but is always polyspecific for many epitopes. When the rationale of the proponents of the unsuccessful rational design strategy is analyzed, it appears that investigators who claim they are designing a vaccine immunogen are only improving the binding reactivity of a single epitope-paratope pair and are not actually designing an immunogen able to generate protective antibodies. The task of a designer consists in imagining what type of immunogen is likely to elicit a protective immune response but in the absence of knowledge regarding which features of the immune system are responsible for producing a functional neutralizing activity in antibodies, it is not feasible to intentionally optimize a potential immunogen candidate in order to obtain the desired outcome. The only available option is actually to test possible solutions by trial-and-error experiments until the preset goal is perhaps attained. Rational design and empirical approaches in HIV vaccine research should thus not be opposed as alternative options since empirical testing is an integral part of a so-called design strategy., Competing Interests: Conflict of interest: The author declares no conflict of interest in this paper.

Our knowledge of antigenic specificity has greatly increased in recent years mainly through X-ray crystallographic studies of proteins and peptides complexed with monoclonal antibodies. However, our ability to predict the location of antigenic sites in proteins remains limited partly because prediction algorithms reduce the complexity of epitopes to one-dimensional, linear peptide models. Epitopes and paratopes are relational entities definable by their mutual complementarity and adaptation potential as well as by their activity. A complete account of antigenic specificity demands the integration of both structural and binding activity data that can be achieved only through a spatiotemporal four-dimensional analysis. Failure to include the fourth dimension, i.e., time, in the analysis of antigen-antibody complementarity amounts to considering proteins as rigid bodies and ignores the mutual adaptation that occurs when the two partners interact. Reducing four-dimensional protein systems to three-dimensional or two-dimensional representations inevitably distorts our perception of the dynamic nature of epitopes.