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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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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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Gupta Y, Savytskyi OV, Coban M, Venugopal A, Pleqi V, Weber CA, Chitale R, Durvasula R, Hopkins C, Kempaiah P, Caulfield TR. Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics. Mol Aspects Med 2023; 91:101151. [PMID: 36371228 PMCID: PMC9613808 DOI: 10.1016/j.mam.2022.101151] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Abstract
With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.
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Affiliation(s)
- Yash Gupta
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Oleksandr V Savytskyi
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; In Vivo Biosystems, Eugene, OR, USA
| | - Matt Coban
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Vasili Pleqi
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Caleb A Weber
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Rohit Chitale
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA; The Council on Strategic Risks, 1025 Connecticut Ave NW, Washington, DC, USA
| | - Ravi Durvasula
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | | | - Prakasha Kempaiah
- Department of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA
| | - Thomas R Caulfield
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of QHS Computational Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA; Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA.
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Asiedu SO, Gupta Y, Nicolaescu V, Gula H, Caulfield TR, Durvasula R, Kempaiah P, Kwofie SK, Wilson MD. Mycolactone: A Broad Spectrum Multitarget Antiviral Active in the Picomolar Range for COVID-19 Prevention and Cure. Int J Mol Sci 2023; 24:ijms24087151. [PMID: 37108313 PMCID: PMC10139166 DOI: 10.3390/ijms24087151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/25/2023] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
We have previously shown computationally that Mycolactone (MLN), a toxin produced by Mycobacterium ulcerans, strongly binds to Munc18b and other proteins, presumably blocking degranulation and exocytosis of blood platelets and mast cells. We investigated the effect of MLN on endocytosis using similar approaches, and it bound strongly to the N-terminal of the clathrin protein and a novel SARS-CoV-2 fusion protein. Experimentally, we found 100% inhibition up to 60 nM and 84% average inhibition at 30 nM in SARS-CoV-2 live viral assays. MLN was also 10× more potent than remdesivir and molnupiravir. MLN's toxicity against human alveolar cell line A549, immortalized human fetal renal cell line HEK293, and human hepatoma cell line Huh7.1 were 17.12%, 40.30%, and 36.25%, respectively. The cytotoxicity IC50 breakpoint ratio versus anti-SARS-CoV-2 activity was more than 65-fold. The IC50 values against the alpha, delta, and Omicron variants were all below 0.020 µM, and 134.6 nM of MLN had 100% inhibition in an entry and spread assays. MLN is eclectic in its actions through its binding to Sec61, AT2R, and the novel fusion protein, making it a good drug candidate for treating and preventing COVID-19 and other similarly transmitted enveloped viruses and pathogens.
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Affiliation(s)
- Seth Osei Asiedu
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra P.O. Box GA 337, Ghana
| | - Yash Gupta
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | - Vlad Nicolaescu
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, USA
| | - Haley Gula
- Department of Microbiology, Ricketts Laboratory, University of Chicago, Chicago, IL 60637, USA
| | - Thomas R Caulfield
- Department of Neuroscience, Division of QHS Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ravi Durvasula
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | - Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | - Samuel K Kwofie
- Department of Biomedical Engineering, School of Engineering, University of Ghana, Legon, Accra P.O. Box 77, Ghana
| | - Michael D Wilson
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra P.O. Box GA 337, Ghana
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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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Watkins RR. Using Precision Medicine for the Diagnosis and Treatment of Viral Pneumonia. Adv Ther 2022; 39:3061-3071. [PMID: 35596912 PMCID: PMC9123616 DOI: 10.1007/s12325-022-02180-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/04/2022] [Indexed: 01/06/2023]
Abstract
The COVID-19 pandemic has drawn considerable attention to viral pneumonia from clinicians, public health authorities, and the general public. With dozens of viruses able to cause pneumonia in humans, differentiating viral from bacterial pneumonia can be very challenging in clinical practice using traditional diagnostic methods. Precision medicine is a medical model in which decisions, practices, interventions, and therapies are adapted to the individual patient on the basis of their predicted response or risk of disease. Precision medicine approaches hold promise as a way to improve outcomes for patients with viral pneumonia. This review describes the latest advances in the use of precision medicine for diagnosing and treating viral pneumonia in adults and discusses areas where further research is warranted.
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Affiliation(s)
- Richard R Watkins
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA.
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Bhakta S, Sanghavi DK, Johnson PW, Kunze KL, Neville MR, Wadei HM, Bosch W, Carter RE, Shah SZ, Pollock BD, Oman SP, Speicher L, Siegel J, Libertin CR, Matson MW, Franco PM, Cowart JB. Clinical and Laboratory Profiles of SARS-CoV-2 Delta Variant Compared to Pre-Delta Variants. Int J Infect Dis 2022; 120:88-95. [PMID: 35487339 PMCID: PMC9040426 DOI: 10.1016/j.ijid.2022.04.050] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients. METHODS This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services was used to identify the waves' primary variant. Wave 1 (08/08/20-04/01/21) was characterized by heterogeneous variants, while Wave 2 (06/26/21-10/18/21) was predominantly Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves. RESULTS From the cohort(n=1318), Wave 2 patients(n=665) were more likely to be younger, have fewer comorbidities, require more ICU care, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and INR compared to Wave 1. The gradient boosting model showed an area under the ROC curve of 0.854(sensitivity 86.4%;specificity 61.5%;positive predictive value 73.8%; negative predictive value 78.3%). CONCLUSIONS Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.
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Affiliation(s)
- Shivang Bhakta
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, Florida, USA.
| | - Devang K Sanghavi
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Patrick W Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
| | - Katie L Kunze
- Department of Quantitative Health Sciences, Mayo Clinic, Scottsdale, Arizona, USA
| | - Matthew R Neville
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Jacksonville, Florida, USA
| | - Hani M Wadei
- Department of Transplantation, Mayo Clinic, Jacksonville, Florida, USA
| | - Wendelyn Bosch
- Division of Infectious Diseases, Mayo Clinic, Jacksonville, Florida, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA
| | - Sadia Z Shah
- Department of Transplantation, Mayo Clinic, Jacksonville, Florida, USA
| | - Benjamin D Pollock
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Jacksonville, Florida, USA
| | - Sven P Oman
- Division of Hospital Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Leigh Speicher
- Division of General Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Jason Siegel
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, Florida, USA; Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Claudia R Libertin
- Division of Infectious Diseases, Mayo Clinic, Jacksonville, Florida, USA
| | - Mark W Matson
- Center for Digital Health - Data & Analytics, Mayo Clinic, Rochester, Minnesota, USA
| | - Pablo Moreno Franco
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, Florida, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Jacksonville, Florida, USA; Department of Transplantation, Mayo Clinic, Jacksonville, Florida, USA
| | - Jennifer B Cowart
- Division of Hospital Internal Medicine, Mayo Clinic, Jacksonville, Florida, USA.
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