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  1. Article ; Online: Approach to the mechanism of action of hydroxychloroquine on SARS-CoV-2: a molecular docking study.

    Celı K, Ismail / Onay-Besı Kcı, Arzu / Ayhan-Kilcigı L, Gulgun

    Journal of biomolecular structure & dynamics

    2020  Volume 39, Issue 15, Page(s) 5792–5798

    Abstract: We aimed to analyze the interactions of both hydroxychloroquine and chloroquine with SARS-CoV-2 and identify their possible role for the prevention/treatment of COVID-19 by molecular docking studies. Protein crystal structures of SARS-CoV-2 and ACE2, the ...

    Abstract We aimed to analyze the interactions of both hydroxychloroquine and chloroquine with SARS-CoV-2 and identify their possible role for the prevention/treatment of COVID-19 by molecular docking studies. Protein crystal structures of SARS-CoV-2 and ACE2, the compounds hydroxychloroquine and chloroquine, and other ligand structures were minimized by OPLS3 force field. Glide Standard Precision and Extra Precision docking are performed and MM-GBSA values ​​are calculated. Molecular docking studies showed that hydroxychloroquine and chloroquine do not interact with SARS-CoV-2 proteins, but bind to the amino acids ASP350, ASP382, ALA348, PHE40 and PHE390 on the ACE2 allosteric site rather than the ACE2 active site. Our results showed that neither hydroxychloroquine and chloroquine bind to the active site of ACE2. However, both molecules prevent the binding of SARS-CoV-2 spike protein to ACE2 by interacting with the allosteric site. This result can help ACE2 inhibitor drug development studies to prevent viruses entering the cell by attaching spike protein to ACE2. Communicated by Ramaswamy H. Sarma.
    MeSH term(s) COVID-19/drug therapy ; Humans ; Hydroxychloroquine/pharmacology ; Molecular Docking Simulation ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus
    Chemical Substances Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 ; Hydroxychloroquine (4QWG6N8QKH)
    Keywords covid19
    Language English
    Publishing date 2020-07-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 49157-3
    ISSN 1538-0254 ; 0739-1102
    ISSN (online) 1538-0254
    ISSN 0739-1102
    DOI 10.1080/07391102.2020.1792993
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Approach to the mechanism of action of hydroxychloroquine on SARS-CoV-2: a molecular docking study

    Celi K, Ismail / Onay-Besi Kci, Arzu / Ayhan-Kilcigi L, Gulgun

    J Biomol Struct Dyn

    Abstract: We aimed to analyze the interactions of both hydroxychloroquine and chloroquine with SARS-CoV-2 and identify their possible role for the prevention/treatment of COVID-19 by molecular docking studies. Protein crystal structures of SARS-CoV-2 and ACE2, the ...

    Abstract We aimed to analyze the interactions of both hydroxychloroquine and chloroquine with SARS-CoV-2 and identify their possible role for the prevention/treatment of COVID-19 by molecular docking studies. Protein crystal structures of SARS-CoV-2 and ACE2, the compounds hydroxychloroquine and chloroquine, and other ligand structures were minimized by OPLS3 force field. Glide Standard Precision and Extra Precision docking are performed and MM-GBSA values ​​are calculated. Molecular docking studies showed that hydroxychloroquine and chloroquine do not interact with SARS-CoV-2 proteins, but bind to the amino acids ASP350, ASP382, ALA348, PHE40 and PHE390 on the ACE2 allosteric site rather than the ACE2 active site. Our results showed that neither hydroxychloroquine and chloroquine bind to the active site of ACE2. However, both molecules prevent the binding of SARS-CoV-2 spike protein to ACE2 by interacting with the allosteric site. This result can help ACE2 inhibitor drug development studies to prevent viruses entering the cell by attaching spike protein to ACE2. Communicated by Ramaswamy H. Sarma.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #650478
    Database COVID19

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  3. Article ; Online: A scientometric analysis of fairness in health AI literature.

    Alberto, Isabelle Rose I / Alberto, Nicole Rose I / Altinel, Yuksel / Blacker, Sarah / Binotti, William Warr / Celi, Leo Anthony / Chua, Tiffany / Fiske, Amelia / Griffin, Molly / Karaca, Gulce / Mokolo, Nkiruka / Naawu, David Kojo N / Patscheider, Jonathan / Petushkov, Anton / Quion, Justin Michael / Senteio, Charles / Taisbak, Simon / Tırnova, İsmail / Tokashiki, Harumi /
    Velasquez, Adrian / Yaghy, Antonio / Yap, Keagan

    PLOS global public health

    2024  Volume 4, Issue 1, Page(s) e0002513

    Abstract: Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members ... ...

    Abstract Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. Articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. Linear and logistic regressions were conducted using Python to measure the relationship between funding status, number of citations, and the gender demographics of the authorship team. We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. Analysis of the bibliographic data revealed that there is an overrepresentation of authors that are white, male, and are from high-income countries, especially in the roles of first and last author. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards.
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3375
    ISSN (online) 2767-3375
    DOI 10.1371/journal.pgph.0002513
    Database MEDical Literature Analysis and Retrieval System OnLINE

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