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  1. Article ; Online: Cooperation under Pressure: Lessons from the COVID-19 Swab Crisis.

    Arnaout, Ramy A

    Journal of clinical microbiology

    2021  Volume 59, Issue 10, Page(s) e0123921

    Abstract: The early months of the COVID-19 pandemic were marked by a desperate need for nasopharyngeal swabs to test for SARS-CoV-2, with demand far outstripping supply. April marked the anniversary of an unprecedented nationwide multibusiness/multihospital ... ...

    Abstract The early months of the COVID-19 pandemic were marked by a desperate need for nasopharyngeal swabs to test for SARS-CoV-2, with demand far outstripping supply. April marked the anniversary of an unprecedented nationwide multibusiness/multihospital partnership that successfully met this need, a fitting occasion to review lessons learned. Here, I briefly recount the key events, constraints, and thought processes surrounding the effort in order to better inform responses to future crises. Overall, the experience was a strong validation of Joy's Law and illustrated the utility of recognizing temptations to avoid, in order to reap the rewards of cooperation. I conclude by summarizing lessons learned.
    MeSH term(s) COVID-19 ; Humans ; Nasopharynx ; Pandemics ; SARS-CoV-2 ; Specimen Handling
    Language English
    Publishing date 2021-08-18
    Publishing country United States
    Document type Editorial
    ZDB-ID 390499-4
    ISSN 1098-660X ; 0095-1137
    ISSN (online) 1098-660X
    ISSN 0095-1137
    DOI 10.1128/JCM.01239-21
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  2. Article ; Online: Visualizing omicron: COVID-19 deaths vs. cases over time.

    Arnaout, Ramy / Arnaout, Rima

    PloS one

    2022  Volume 17, Issue 4, Page(s) e0265233

    Abstract: For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of ... ...

    Abstract For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.
    MeSH term(s) COVID-19/epidemiology ; Humans ; Pandemics ; SARS-CoV-2 ; South Africa/epidemiology
    Language English
    Publishing date 2022-04-19
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0265233
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  3. Article ; Online: ENRICHing medical imaging training sets enables more efficient machine learning.

    Chinn, Erin / Arora, Rohit / Arnaout, Ramy / Arnaout, Rima

    Journal of the American Medical Informatics Association : JAMIA

    2023  Volume 30, Issue 6, Page(s) 1079–1090

    Abstract: Objective: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In ... ...

    Abstract Objective: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model's performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets.
    Materials and methods: To address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set.
    Results: First, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets.
    Conclusions: ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.
    MeSH term(s) Humans ; Diagnostic Imaging ; Machine Learning ; Radiography ; Palliative Care ; Image Processing, Computer-Assisted/methods
    Language English
    Publishing date 2023-04-10
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocad055
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  4. Article ; Online: Machine Learning in Clinical Pathology: Seeing the Forest for the Trees.

    Arnaout, Ramy

    Clinical chemistry

    2018  Volume 64, Issue 11, Page(s) 1553–1554

    MeSH term(s) Diagnostic Tests, Routine ; Machine Learning
    Language English
    Publishing date 2018-09-20
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1373/clinchem.2018.295121
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  5. Article ; Online: Repertoire-scale measures of antigen binding.

    Arora, Rohit / Arnaout, Ramy

    Proceedings of the National Academy of Sciences of the United States of America

    2022  Volume 119, Issue 34, Page(s) e2203505119

    Abstract: Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic ... ...

    Abstract Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.
    MeSH term(s) Adaptive Immunity ; Epitopes/metabolism ; Humans ; Immunity/immunology ; Peptides/metabolism ; Receptors, Antigen, T-Cell
    Chemical Substances Epitopes ; Peptides ; Receptors, Antigen, T-Cell
    Language English
    Publishing date 2022-08-15
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2203505119
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  6. Article ; Online: Universal PCR for bacteria, mycobacteria, and fungi: a 10-year retrospective review of clinical indications and patient outcomes.

    Kubiak, Jeffrey / Morgan, Alexandra / Kirmaier, Andrea / Arnaout, Ramy / Riedel, Stefan

    Journal of clinical microbiology

    2023  Volume 61, Issue 12, Page(s) e0095223

    Abstract: Importance: Our work provides a retrospective analysis of universal PCR orders for bacteria, mycobacteria, and fungi across our institution across a 10-year period. We assessed the positivity rates for this diagnostic tool by test type and specimen type ...

    Abstract Importance: Our work provides a retrospective analysis of universal PCR orders for bacteria, mycobacteria, and fungi across our institution across a 10-year period. We assessed the positivity rates for this diagnostic tool by test type and specimen type and, critically, studied whether and how the results influenced the outcomes from treatment change, to readmission, to death.
    MeSH term(s) Humans ; Fungi/genetics ; Mycobacterium/genetics ; Polymerase Chain Reaction/methods ; Retrospective Studies
    Language English
    Publishing date 2023-11-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 390499-4
    ISSN 1098-660X ; 0095-1137
    ISSN (online) 1098-660X
    ISSN 0095-1137
    DOI 10.1128/jcm.00952-23
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  7. Article ; Online: Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.

    Quer, Giorgio / Arnaout, Ramy / Henne, Michael / Arnaout, Rima

    Journal of the American College of Cardiology

    2021  Volume 77, Issue 3, Page(s) 300–313

    Abstract: The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts ... ...

    Abstract The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.
    MeSH term(s) Cardiology ; Humans ; Machine Learning
    Language English
    Publishing date 2021-01-21
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ZDB-ID 605507-2
    ISSN 1558-3597 ; 0735-1097
    ISSN (online) 1558-3597
    ISSN 0735-1097
    DOI 10.1016/j.jacc.2020.11.030
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  8. Article ; Online: SARS-CoV-2 live virus culture and sample freeze-thaw stability.

    Kanki, Phyllis J / Hamel, Donald J / Riedel, Stefan / Dutta, Sanjucta / Cheng, Annie / Chang, Charlotte A / Arnaout, Ramy / Kirby, James E

    Diagnostic microbiology and infectious disease

    2024  Volume 109, Issue 3, Page(s) 116282

    Abstract: The effect of freeze-thaw on SARS-CoV-2 viral viability is not well established. We isolated virus from 31 split clinical samples cultured fresh or after a 7- or 17/18-day freeze. We found that freeze-thaw did not significantly affect viral culture ... ...

    Abstract The effect of freeze-thaw on SARS-CoV-2 viral viability is not well established. We isolated virus from 31 split clinical samples cultured fresh or after a 7- or 17/18-day freeze. We found that freeze-thaw did not significantly affect viral culture isolation. Therefore, frozen samples may be used to assess SARS-CoV-2 infectiousness.
    Language English
    Publishing date 2024-03-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604920-5
    ISSN 1879-0070 ; 0732-8893
    ISSN (online) 1879-0070
    ISSN 0732-8893
    DOI 10.1016/j.diagmicrobio.2024.116282
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  9. Article ; Online: Economic modelling could aid brain map.

    Arnaout, Ramy

    Nature

    2013  Volume 497, Issue 7450, Page(s) 439

    MeSH term(s) Adverse Drug Reaction Reporting Systems ; Brain Mapping/economics ; Models, Economic ; Pharmacogenetics/economics ; United States
    Language English
    Publishing date 2013-05-23
    Publishing country England
    Document type Letter
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/497439d
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  10. Article ; Online: Elementary, my dear Doctor Watson.

    Arnaout, Ramy

    Clinical chemistry

    2012  Volume 58, Issue 6, Page(s) 986–988

    MeSH term(s) Artificial Intelligence ; Chemistry, Clinical/instrumentation ; Chemistry, Clinical/methods ; Computers ; Decision Support Systems, Clinical/instrumentation ; Medical Records Systems, Computerized
    Language English
    Publishing date 2012-04-20
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1373/clinchem.2011.180992
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