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Your search keyword '"Raquel Lemos Pimentel"' showing total 4 results
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4 results on '"Raquel Lemos Pimentel"'

1. Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

Author

Marcelo Saito Nogueira, Leonardo Barbosa Leal, Wena Macarini, Raquel Lemos Pimentel, Matheus Muller, Paula Frizera Vassallo, Luciene Cristina Gastalho Campos, Leonardo dos Santos, Wilson Barros Luiz, José Geraldo Mill, Valerio Garrone Barauna, and Luis Felipe das Chagas e Silva de Carvalho

Subjects
Medicine, Science
Abstract

Abstract Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

Published
2021
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3. Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

Author

Luis Felipe das Chagas e Silva de Carvalho, Paula Frizera Vassallo, Leonardo dos Santos, Wena Macarini, Matheus Muller, Leonardo Barbosa Leal, Wilson Barros Luiz, José Geraldo Mill, Marcelo Saito Nogueira, Valério Garrone Barauna, Raquel Lemos Pimentel, and Luciene Cristina Gastalho Campos

Subjects
Oropharyngeal swab, Multidisciplinary, Coronavirus disease 2019 (COVID-19), business.industry, Science, Translational research, Machine learning, computer.software_genre, Article, Viral infection, Attenuated total reflection, Partial least squares regression, Medicine, Viral transport, Artificial intelligence, Biophotonics, Fourier transform infrared spectroscopy, Spectroscopy, business, computer, Applied optics
Abstract

Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

Published
2021

4. Author Correction: Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

Author

Luciene Cristina Gastalho Campos, Raquel Lemos Pimentel, Leonardo dos Santos, Valério Garrone Barauna, Wena Dantas Marcarini, Luis Felipe das Chagas e Silva de Carvalho, Paula Frizera Vassallo, Marcelo Saito Nogueira, Matheus Muller, Leonardo Barbosa Leal, Wilson Barros Luiz, and José Geraldo Mill

Subjects
Adult, Male, 2019-20 coronavirus outbreak, Materials science, Time Factors, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Science, Analytical chemistry, Machine Learning, COVID-19 Testing, Spectroscopy, Fourier Transform Infrared, Humans, Fourier transform infrared spectroscopy, Least-Squares Analysis, Spectroscopy, Author Correction, Aged, Multidisciplinary, SARS-CoV-2, COVID-19, Middle Aged, Early Diagnosis, Medicine, Female, Brazil
Abstract

Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

Published
2021

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