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Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.
Delafiori, Jeany; Navarro, Luiz Cláudio; Siciliano, Rinaldo Focaccia; de Melo, Gisely Cardoso; Busanello, Estela Natacha Brandt; Nicolau, José Carlos; Sales, Geovana Manzan; de Oliveira, Arthur Noin; Val, Fernando Fonseca Almeida; de Oliveira, Diogo Noin; Eguti, Adriana; Dos Santos, Luiz Augusto; Dalçóquio, Talia Falcão; Bertolin, Adriadne Justi; Abreu-Netto, Rebeca Linhares; Salsoso, Rocio; Baía-da-Silva, Djane; Marcondes-Braga, Fabiana G; Sampaio, Vanderson Souza; Judice, Carla Cristina; Costa, Fabio Trindade Maranhão; Durán, Nelson; Perroud, Mauricio Wesley; Sabino, Ester Cerdeira; Lacerda, Marcus Vinicius Guimarães; Reis, Leonardo Oliveira; Fávaro, Wagner José; Monteiro, Wuelton Marcelo; Rocha, Anderson Rezende; Catharino, Rodrigo Ramos.
  • Delafiori J; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 350-13083-970, Brazil.
  • Navarro LC; RECOD Laboratory, Computing Institute, University of Campinas, Cidade Universitária Zeferino Vaz,, Campinas, São Paulo 573-13083-852, Brazil.
  • Siciliano RF; Clinical Division of Infectious and Parasitic Diseases, University of São Paulo Medical School, São Paulo, São Paulo 01246-903, Brazil.
  • de Melo GC; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Busanello ENB; Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Nicolau JC; Amazonas State University, Manaus, Amazonas 25-69040-000, Brazil.
  • Sales GM; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 350-13083-970, Brazil.
  • de Oliveira AN; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Val FFA; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 350-13083-970, Brazil.
  • de Oliveira DN; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 350-13083-970, Brazil.
  • Eguti A; Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Dos Santos LA; Amazonas State University, Manaus, Amazonas 25-69040-000, Brazil.
  • Dalçóquio TF; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 350-13083-970, Brazil.
  • Bertolin AJ; Sumaré State Hospital, Sumaré, São Paulo 2400-13175-490, Brazil.
  • Abreu-Netto RL; Paulínia Municipal Hospital, Paulínia, São Paulo 100-13140-000, Brazil.
  • Salsoso R; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Baía-da-Silva D; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Marcondes-Braga FG; Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Sampaio VS; Amazonas State University, Manaus, Amazonas 25-69040-000, Brazil.
  • Judice CC; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Costa FTM; Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Durán N; Amazonas State University, Manaus, Amazonas 25-69040-000, Brazil.
  • Perroud MW; Instituto do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, Cerqueira César, São Paulo 44-05403-90, Brazil.
  • Sabino EC; Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Lacerda MVG; Health Surveillance Foundation of Amazonas State, Manaus, Amazonas, 4010-69093-018 Brazil.
  • Reis LO; Laboratory of Tropical Diseases, Institute of Biology, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 13083-970 Brazil.
  • Fávaro WJ; Laboratory of Tropical Diseases, Institute of Biology, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 13083-970 Brazil.
  • Monteiro WM; Laboratory of Urogenital Carcinogenesis and Immunotherapy, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas, São Paulo 13083-865, Brazil.
  • Rocha AR; Sumaré State Hospital, Sumaré, São Paulo 2400-13175-490, Brazil.
  • Catharino RR; Institute of Tropical Medicine, University of São Paulo, São Paulo, São Paulo 470-05403-000,Brazil.
Anal Chem ; 93(4): 2471-2479, 2021 02 02.
Article in English | MEDLINE | ID: covidwho-1065764
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Metabolomics / Machine Learning / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male / Middle aged Country/Region as subject: South America / Brazil Language: English Journal: Anal Chem Year: 2021 Document Type: Article Affiliation country: Acs.analchem.0c04497

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Metabolomics / Machine Learning / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Adult / Aged / Female / Humans / Male / Middle aged Country/Region as subject: South America / Brazil Language: English Journal: Anal Chem Year: 2021 Document Type: Article Affiliation country: Acs.analchem.0c04497