[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127756-en":3,"doc-seo-127756-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127756,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Framework to Identify the Correlates of Disease Severity in Acute Arbovirus Infection","Viral diseases show diverse clinical outcomes driven by differences in viral strain virulence and host susceptibility. Clarifying how severe infections diverge from milder forms supports development of therapies and early prognostic markers, particularly for arboviruses where many cases present as mild febrile illness. This study uses bluetongue in ruminants as an experimental system to reproduce distinct clinical outcomes across three virus strains. By integrating clinical, hematological, virological, and histopathological data and applying machine learning to 332 parameters, five core processes were identified as drivers of severity, including viral load and replication, type-I IFN modulation, pro-inflammatory responses, vascular damage, and immunosuppression.","bioRxiv preprint doi: [https://doi.org/10.1101/2024.02.23.581333](https://doi.org/10.1101/2024.02.23.581333); this version posted February 24, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.  \n1 A Machine Learning Framework to Identify the Correlates of Disease Severity in Acute  \n2 Arbovirus Infection  \n3  \n4 Vanessa Herder1 , Marco Caporale2 , Oscar A MacLean 1 , Davide Pintus3 , Xinyi Huang 1 , Kyriaki  \n5 Nomikou 1\\# , Natasha Palmalux 1 , Jenna Nichols 1 , Rosario Scivoli3 , Chris Boutell1 , Aislynn Taggart 1 , 6 Jay Allan 1 , Haris Malik 1 , Georgios Ilia 1 , Quan Gu 1 , Gaetano Federico Ronchi2 , Wilhelm Furnon1 , 7 Stephan Zientara4 , Emmanuel Bréard4 , Daniela Antonucci2 , Sara Capista2 , Daniele Giansante2 , 8 Antonio Cocco2 , Maria Teresa Mercante2 , Mauro Di Ventura2 , Ana Da Silva Filipe 1 , Giantonella  \n9 Puggioni3 , Noemi Sevilla5 , Meredith E. Stewart 1 , Ciriaco Ligios3 , Massimo Palmarini 1*  \n10  \n11 1 MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom.  \n12 2 Istituto Zooprofilattico Sperimentale dell’ Abruzzo e Molise “G. Caporale”, Teramo, Italy.  \n13 3 Istituto Zooprofilattico Sperimentale della Sardegna, Sassari, Italy.  \n14 4 Laboratory for Animal Health, INRAE, Ecole Nationale Vétérinaire d'Alfort, ANSES, Maisons-Alfort, 15 France.  \n16 5Centro de Investigación en Sanidad Animal. Instituto Nacional de Investigación y Tecnología  \n17 Agraria y Alimentaria. Consejo Superior de Investigaciones Científicas (CISA-INIA-CSIC) .  \n18 Valdeolmos, Madrid, Spain.  \n19  \n20 \\#deceased  \n21  \n22 *Corresponding author:  \n23 [massimo.palmarini@glasgow.ac.uk](massimo.palmarini@glasgow.ac.uk)  \nbioRxiv preprint doi: [https://doi.org/10.1101/2024.02.23.581333](https://doi.org/10.1101/2024.02.23.581333); this version posted February 24, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.  \n24 Abstract  \n25 Most viral diseases display a variable clinical outcome due to differences in virus strain virulence  \n26 and/or individual host susceptibility to infection. Understanding the biological mechanisms  \n27 differentiating a viral infection displaying severe clinical manifestations from its milder forms can  \n28 provide the intellectual framework toward therapies and early prognostic markers. This is especially  \n29 true in arbovirus infections, where most clinical cases are present as mild febrile illness. Here, we  \n30 used a naturally occurring vector-borne viral disease of ruminants, bluetongue, as an experimental  \n31 system to uncover the fundamental mechanisms of virus-host interactions resulting in distinct clinical  \n32 outcomes. As with most viral diseases, clinical symptoms in bluetongue can vary dramatically. We  \n33 reproduced experimentally distinct clinical forms of bluetongue infection in sheep using three  \n34 bluetongue virus (BTV) strains (BTV-1IT2006 , BTV-1IT2013 and BTV-8FRA2017) . Infected animals  \n35 displayed clinical signs varying from clinically unapparent, to mild and severe disease. We collected  \n36 and integrated clinical, haematological, virological, and histopathological data resulting in the  \n37 analyses of 332 individual parameters from each infected and uninfected control animal. We  \n38 subsequently used machine learning to identify the key viral and host processes associated with  \n39 disease pathogenesis. We identified five different fundamental processes affecting the severity of  \n40 bluetongue: (i) virus load and replication in target organs, (ii) modulation of the host type-I IFN  \n41 response, (iii) pro-inflammatory responses, (iv) vascular damage, and (v) immunosuppr","cbCailDycmBtt8QL","https://ap.wps.com/l/cbCailDycmBtt8QL","pdf",3941554,1,48,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the main goal of this study on acute arbovirus infection?\",\"answer\":\"To identify biological mechanisms and key viral and host processes that correlate with disease severity, enabling prioritization of pathogenetic routes for future prognostic markers and therapies.\"},{\"question\":\"How was disease severity modeled in the experiment?\",\"answer\":\"Distinct clinical forms of bluetongue infection were reproduced in sheep using three bluetongue virus strains, producing outcomes ranging from unapparent to mild and severe disease.\"},{\"question\":\"Which types of data and methods were used to find severity correlates?\",\"answer\":\"Clinical, hematological, virological, and histopathological data were collected and integrated, followed by an agnostic machine learning approach to associate 332 parameters with disease pathogenesis.\"},{\"question\":\"What five processes were identified as affecting bluetongue disease severity?\",\"answer\":\"Virus load and replication in target organs, modulation of the host type-I IFN response, pro-inflammatory responses, vascular damage, and immunosuppression.\"}]","A Machine Learning Framework to Identify the Correlates of Disease Severity in Acute Arbovirus Infection | 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is the main goal of this study on acute arbovirus infection?","Question",{"text":76,"@type":77},"To identify biological mechanisms and key viral and host processes that correlate with disease severity, enabling prioritization of pathogenetic routes for future prognostic markers and therapies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was disease severity modeled in the experiment?",{"text":81,"@type":77},"Distinct clinical forms of bluetongue infection were reproduced in sheep using three bluetongue virus strains, producing outcomes ranging from unapparent to mild and severe disease.",{"name":83,"@type":74,"acceptedAnswer":84},"Which types of data and methods were used to find severity correlates?",{"text":85,"@type":77},"Clinical, hematological, virological, and histopathological data were collected and integrated, followed by an agnostic machine learning approach to associate 332 parameters with disease pathogenesis.",{"name":87,"@type":74,"acceptedAnswer":88},"What 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