Infrared Based Saliva Screening Test for COVID‐19

Wood, Bayden R.; Kochan, Kamila; Bedolla, Diana E.; Salazar‐Quiroz, Natalia; Grimley, Samantha L.; Pérez-Guaita, David; Baker, Matthew J.; Vongsvivut, Jitraporn; Tobin, Mark J.; Bambery, Keith R.; Christensen, Dale; Pasricha, Shivani; Eden, Anthony K.; McLean, A. R.; Roy, Supti; Roberts, Jason A.; Druce, Julian; Williamson, Deborah A.; McAuley, Julie; Catton, Mike; Purcell, Damian F. J.; Godfrey, Dale I.; Heraud, Philip

doi:10.1002/ange.202104453

Cited by 17 publications

(9 citation statements)

References 28 publications

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“…The analysis of the COVID-19 saliva data set using near IR-Raman spectroscopy showed significant improvement with the use of the UPSTM created by the IDeA algorithm. Decorrelating the observed data produced a set of highly significant features that are associated with the presence of COVID-19 metabolites, that are very similar to the one found by the second derivative of the spectrum which revealed the wavenumber locations of wavenumbers linked to the SARS-CoV-2 virus (33). The UPSTM decorrelation process identified a similar number and set of wavenumbers for the saliva metabolites, making it possible to detect the virus in saliva samples, as shown in Fig.…”

Section: Covid-19-ir Ramanmentioning

confidence: 59%

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Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Tamez-Peña

2023

Preprint

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Background Numerous biomarker discovery studies and exploratory clinical studies extract a large set of measurable variables, which often have varying degrees of correlation among them. This data collinearity can impact statistical model interpretation and hinder the discovery of potential associations between measured variables and the observed outcome. Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Machine-Learning (ML) can be used to discover latent variables associated with disease progression or outcome by computing transformation matrices, but the interpretation of unsupervised/supervised latent variables in high-dimensional datasets can be challenging. Results This study describe and reports the performance of the iterative decorrelation analysis algorithm (IDeA). The algorithm iteratively analyzes the correlation matrix of the data, updating the transformation coefficients until it reaches the desired correlation goal. The output of IDeA is a basis-transformation matrix that preserves the data dimensionality and unit of measure of the original observed variables. The main advantages of the IDeA basis transformations are sparsity and interpretability. The transformation does not alter uncorrelated features, thus statistical modeling and biomarker discovery in the new transformed basis can be a combination of novel latent variables and a sizable subset of unaltered variables. The algorithm was tested on five multidimensional/hyperdimensional and multimodal sets, demonstrating the effect of decorrelation parameters, interpretability, and latent biomarker discovery. Conclusions The iterative decorrelation approach provides a simple to use tool for researchers to explore the association between correlated features in hyperdimensional/multimodal settings and to decorrelate significant associations via latent variables that preserve the unit of measurement. An efficient computer implementation of IDeA is available in the FRESA.CAD R package (https://cran.r-project.org/web/packages/FRESA.CAD/index.html).

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Section: Covid-19-ir Ramanmentioning

confidence: 59%

“…The table shows the characteristics of the dataset used to validate the biomarker discovery properties of the IDeA method. The COVID-19-IR-Raman experiment measured the presence of the SARS-CoV-2 virus in saliva samples (33). The data set is hyperdimensional with more features of data samples.…”

Section: Datasetsmentioning

confidence: 99%

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Tamez-Peña

2023

Preprint

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“…Raman signal enhancement realized via the use of gold nanoparticles attached to a reduced porous graphene oxide modified substrate has resulted in obtaining a detection limit of 75 fmol L −1 . A comparative evaluation of SARS-CoV-2 virions and purified RNA via Raman spectroscopy was reported in a recent proof-of-concept study, where a specific RNA marker band has been identified at 807 cm −1 (Wood et al 2021 ). The potential of receptor binding domain (RBD) to be used as an effective target for SERS detection of the SARS-CoV-2 was also demonstrated recently (Awada et al 2021 ).…”

Section: Sars-cov-2 Detectionmentioning

confidence: 99%

Raman spectroscopy for viral diagnostics

et al. 2023

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Raman spectroscopy offers the potential for fingerprinting biological molecules at ultra-low concentration and therefore has potential for the detection of viruses. Here we review various Raman techniques employed for the investigation of viruses. Different Raman techniques are discussed including conventional Raman spectroscopy, surface-enhanced Raman spectroscopy, Raman tweezer, tip-enhanced Raman Spectroscopy, and coherent anti-Stokes Raman scattering. Surface-enhanced Raman scattering can play an essential role in viral detection by multiplexing nanotechnology, microfluidics, and machine learning for ensuring spectral reproducibility and efficient workflow in sample processing and detection. The application of these techniques to diagnose the SARS-CoV-2 virus is also reviewed. Graphical abstract

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“…For example, Esperança et al [32] used NIRS of insect bodies to identify malaria (Plasmodium berghei) infected mosquitoes (Anopheles stephensi), which is essential for monitoring the transmission potential of this parasite to humans and the efficacy of control interventions. Recent research has found that the novel COVID-19 virus can be detected from serum and pharynx exudate samples in humans using NIRS [79,80]. This method also may be able to provide a rapid screening tool for coronaviruses in bats and other animals that could threaten human populations.…”

Section: Animal Health and Diseasementioning

confidence: 99%

The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation

Morgan

Marsh

Tolleson³

et al. 2021

Remote Sensing

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The ability to measure and monitor wildlife populations is important for species management and conservation. The use of near-infrared spectroscopy (NIRS) to rapidly detect physiological traits from wildlife scat and other body materials could play an important role in the conservation of species. Previous research has demonstrated the potential for NIRS to detect diseases such as the novel COVID-19 from saliva, parasites from feces, and numerous other traits from animal skin, hair, and scat, such as cortisol metabolites, diet quality, sex, and reproductive status, that may be useful for population monitoring. Models developed from NIRS data use light reflected from a sample to relate the variation in the sample’s spectra to variation in a trait, which can then be used to predict that trait in unknown samples based on their spectra. The modelling process involves calibration, validation, and evaluation. Data sampling, pre-treatments, and the selection of training and testing datasets can impact model performance. We review the use of NIRS for measuring physiological traits in animals that may be useful for wildlife management and conservation and suggest future research to advance the application of NIRS for this purpose.

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Infrared Based Saliva Screening Test for COVID‐19

Cited by 17 publications

References 28 publications

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Raman spectroscopy for viral diagnostics

The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation

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