TY - JOUR
T1 - Potential of ATR-FTIR-Chemometrics in Covid-19
T2 - Disease Recognition
AU - Calvo-Gomez, Octavio
AU - Calvo, Hiram
AU - Cedillo-Barrón, Leticia
AU - Vivanco-Cid, Héctor
AU - Alvarado-Orozco, Juan Manuel
AU - Fernandez-Benavides, David Andrés
AU - Arriaga-Pizano, Lourdes
AU - Ferat-Osorio, Eduardo
AU - Anda-Garay, Juan Carlos
AU - López-Macias, Constantino
AU - López, Mercedes G.
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/9/6
Y1 - 2022/9/6
N2 - The COVID-19 pandemic has caused major disturbances to human health and economy on a global scale. Although vaccination campaigns and important advances in treatments have been developed, an early diagnosis is still crucial. While PCR is the golden standard for diagnosing SARS-CoV-2 infection, rapid and low-cost techniques such as ATR-FTIR followed by multivariate analyses, where dimensions are reduced for obtaining valuable information from highly complex data sets, have been investigated. Most dimensionality reduction techniques attempt to discriminate and create new combinations of attributes prior to the classification stage; thus, the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. In this work, we developed a method for evaluating SARS-CoV-2 infection and COVID-19 disease severity on infrared spectra of sera, based on a rather simple feature selection technique (correlation-based feature subset selection). Dengue infection was also evaluated for assessing whether selectivity toward a different virus was possible with the same algorithm, although independent models were built for both viruses. High sensitivity (94.55%) and high specificity (98.44%) were obtained for assessing SARS-CoV-2 infection with our model; for severe COVID-19 disease classification, sensitivity is 70.97% and specificity is 94.95%; for mild disease classification, sensitivity is 33.33% and specificity is 94.64%; and for dengue infection assessment, sensitivity is 84.27% and specificity is 94.64%.
AB - The COVID-19 pandemic has caused major disturbances to human health and economy on a global scale. Although vaccination campaigns and important advances in treatments have been developed, an early diagnosis is still crucial. While PCR is the golden standard for diagnosing SARS-CoV-2 infection, rapid and low-cost techniques such as ATR-FTIR followed by multivariate analyses, where dimensions are reduced for obtaining valuable information from highly complex data sets, have been investigated. Most dimensionality reduction techniques attempt to discriminate and create new combinations of attributes prior to the classification stage; thus, the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. In this work, we developed a method for evaluating SARS-CoV-2 infection and COVID-19 disease severity on infrared spectra of sera, based on a rather simple feature selection technique (correlation-based feature subset selection). Dengue infection was also evaluated for assessing whether selectivity toward a different virus was possible with the same algorithm, although independent models were built for both viruses. High sensitivity (94.55%) and high specificity (98.44%) were obtained for assessing SARS-CoV-2 infection with our model; for severe COVID-19 disease classification, sensitivity is 70.97% and specificity is 94.95%; for mild disease classification, sensitivity is 33.33% and specificity is 94.64%; and for dengue infection assessment, sensitivity is 84.27% and specificity is 94.64%.
UR - http://www.scopus.com/inward/record.url?scp=85137702524&partnerID=8YFLogxK
U2 - 10.1021/acsomega.2c01374
DO - 10.1021/acsomega.2c01374
M3 - Artículo
C2 - 36092630
AN - SCOPUS:85137702524
SN - 2470-1343
VL - 7
SP - 30756
EP - 30767
JO - ACS Omega
JF - ACS Omega
IS - 35
ER -