Autor(es):
Lazari, Lucas C. ; Zerbinati, Rodrigo M. ; Rosa-Fernandes, Livia ; Santiago, Veronica Feijoli ; Rosa, Klaise F. ; Angeli, Claudia B. ; Schwab, Gabriela ; Palmieri, Michelle ; Sarmento, Dmitry J. S. ; Marinho, Claudio R. F. ; Almeida, Janete Dias [UNESP] ; To, Kelvin ; Giannecchini, Simone ; Wrenger, Carsten ; Sabino, Ester C. ; Martinho, Herculano ; Lindoso, José A. L. ; Durigon, Edison L. ; Braz-Silva, Paulo H. ; Palmisano, Giuseppe
Data: 2022
Identificador Persistente: http://hdl.handle.net/11449/234241
Origem: Oasisbr
Assunto(s): biomarkers; prognosis; proteomics; Saliva; SARS-CoV-2
Descrição
Made available in DSpace on 2022-05-01T15:13:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01
Background: The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate are essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. Methods: Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. Results: We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. When we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. Conclusion: Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a larger dataset is necessary to consolidate the technique.
GlycoProteomics Laboratory Department of Parasitology ICB University of São Paulo
Laboratory of Virology Institute of Tropical Medicine of São Paulo School of Medicine University of São Paulo
Laboratory of Experimental Immunoparasitology Department of Parasitology ICB University of São Paulo
Department of Stomatology School of Dentistry University of São Paulo
Department of Biosciences and Oral Diagnosis Institute of Science and Technology São Paulo State University
State Key Laboratory for Emerging Infectious Diseases Department of Microbiology Carol Yu Centre for Infection Li KaShing Faculty of Medicine of the University of Hong Kong
Department of Experimental and Clinical Medicine University of Florence
Unit for Drug Discovery Department of Parasitology ICB University of São Paulo
Institute of Tropical Medicine of São Paulo School of Medicine University of São Paulo
Centro de Ciencias Naturais e Humanas Universidade Federal do ABC
Institute of Infectious Diseases Emílio Ribas
Laboratory of Protozoology Institute of Tropical Medicine of São Paulo School of Medicine University of São Paulo
Department of Infectious Diseases School of Medicine University of São Paulo
Laboratory of Clinical and Molecular Virology Department of Microbiology ICB University of São Paulo
Department of Biosciences and Oral Diagnosis Institute of Science and Technology São Paulo State University
