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Neves MM, Guerra RF, de Lima IL, Arrais TS, Guevara-Vega M, Ferreira FB, Rosa RB, Vieira MS, Fonseca BB, Sabino da Silva R, da Silva MV. Perspectives of FTIR as Promising Tool for Pathogen Diagnosis, Sanitary and Welfare Monitoring in Animal Experimentation Models: A Review Based on Pertinent Literature. Microorganisms 2024; 12:833. [PMID: 38674777 PMCID: PMC11052489 DOI: 10.3390/microorganisms12040833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Currently, there is a wide application in the literature of the use of the Fourier Transform Infrared Spectroscopy (FTIR) technique. This basic tool has also proven to be efficient for detecting molecules associated with hosts and pathogens in infections, as well as other molecules present in humans and animals' biological samples. However, there is a crisis in science data reproducibility. This crisis can also be observed in data from experimental animal models (EAMs). When it comes to rodents, a major challenge is to carry out sanitary monitoring, which is currently expensive and requires a large volume of biological samples, generating ethical, legal, and psychological conflicts for professionals and researchers. We carried out a survey of data from the relevant literature on the use of this technique in different diagnostic protocols and combined the data with the aim of presenting the technique as a promising tool for use in EAM. Since FTIR can detect molecules associated with different diseases and has advantages such as the low volume of samples required, low cost, sustainability, and provides diagnostic tests with high specificity and sensitivity, we believe that the technique is highly promising for the sanitary and stress and the detection of molecules of interest of infectious or non-infectious origin.
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Affiliation(s)
- Matheus Morais Neves
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Renan Faria Guerra
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
| | - Isabela Lemos de Lima
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Thomas Santos Arrais
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Marco Guevara-Vega
- Innovation Center in Salivary Diagnostic and Nanotheranostics, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlândia 38408-100, MG, Brazil; (M.G.-V.); (R.S.d.S.)
| | - Flávia Batista Ferreira
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
| | - Rafael Borges Rosa
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
| | - Mylla Spirandelli Vieira
- Faculty of Medicine, Maria Ranulfa Institute, Av. Vasconselos Costa 321, Uberlândia 38400-448, MG, Brazil;
| | | | - Robinson Sabino da Silva
- Innovation Center in Salivary Diagnostic and Nanotheranostics, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlândia 38408-100, MG, Brazil; (M.G.-V.); (R.S.d.S.)
| | - Murilo Vieira da Silva
- Biotechnology in Experimental Models Laboratory—LABME, Federal University of Uberlândia, Uberlândia 38405-330, MG, Brazil; (M.M.N.); (R.F.G.); (I.L.d.L.); (T.S.A.); (F.B.F.)
- Rodents Animal Facilities Complex, Federal University of Uberlandia, Uberlândia 38400-902, MG, Brazil;
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Hu D, Li Z, Wang R, Gao X, Mou M, Xiang N. Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123742. [PMID: 38113559 DOI: 10.1016/j.saa.2023.123742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023]
Abstract
The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.
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Affiliation(s)
- Dean Hu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China.
| | - Ruixin Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Xuning Gao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
| | - Mingkai Mou
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
| | - Nan Xiang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066004, China
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3
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Brun BF, Nascimento MHC, Dias PAC, Marcarini WD, Singh MN, Filgueiras PR, Vassallo PF, Romão W, Mill JG, Martin FL, Barauna VG. Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value. Talanta 2024; 269:125482. [PMID: 38042146 DOI: 10.1016/j.talanta.2023.125482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/04/2023]
Abstract
Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 μg/mL and >0.5 μg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.
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Affiliation(s)
- Bruna F Brun
- Department of Physiological Science, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Marcia H C Nascimento
- Exact Sciences Center, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Pedro A C Dias
- Department of Physiological Science, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Wena D Marcarini
- Department of Physiological Science, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil; Centro Universitário Vale do CRICARÉ, São Matheus, Espírito Santo, Brazil
| | - Maneesh N Singh
- Biocel UK Ltd, Hull, HU10 6TS, UK; Chesterfield Royal Hospital, Chesterfield Road, Calow, Chesterfield, S44 5BL, UK
| | - Paulo R Filgueiras
- Exact Sciences Center, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Paula F Vassallo
- Clinical Hospital, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Wanderson Romão
- Exact Sciences Center, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil; Federal Institute of Education Science and Technology of Espírito Santo, Vila Velha, Espírito Santo, Brazil
| | - José G Mill
- Department of Physiological Science, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Francis L Martin
- Biocel UK Ltd, Hull, HU10 6TS, UK; Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool, FY3 8NR, UK
| | - Valerio G Barauna
- Department of Physiological Science, Federal University of Espírito Santo, Vitória, Espírito Santo, Brazil.
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Hackshaw KV, Yao S, Bao H, de Lamo Castellvi S, Aziz R, Nuguri SM, Yu L, Osuna-Diaz MM, Brode WM, Sebastian KR, Giusti MM, Rodriguez-Saona L. Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics. Biomedicines 2023; 11:2704. [PMID: 37893078 PMCID: PMC10604557 DOI: 10.3390/biomedicines11102704] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/01/2023] [Accepted: 10/03/2023] [Indexed: 10/29/2023] Open
Abstract
Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.
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Affiliation(s)
- Kevin V. Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Siyu Yao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
| | - Haona Bao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
- Campus Sescelades, Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (W.M.B.); (K.R.S.)
| | - Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA;
| | - Michelle M. Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (W.M.B.); (K.R.S.)
| | - W. Michael Brode
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (W.M.B.); (K.R.S.)
| | - Katherine R. Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (W.M.B.); (K.R.S.)
| | - M. Monica Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (S.Y.); (H.B.); (S.d.L.C.); (S.M.N.); (M.M.G.); (L.R.-S.)
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5
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Delrue C, De Bruyne S, Speeckaert MM. Unlocking the Diagnostic Potential of Saliva: A Comprehensive Review of Infrared Spectroscopy and Its Applications in Salivary Analysis. J Pers Med 2023; 13:907. [PMID: 37373896 DOI: 10.3390/jpm13060907] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/20/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
Infrared (IR) spectroscopy is a noninvasive and rapid analytical technique that provides information on the chemical composition, structure, and conformation of biomolecules in saliva. This technique has been widely used to analyze salivary biomolecules, owing to its label-free advantages. Saliva contains a complex mixture of biomolecules including water, electrolytes, lipids, carbohydrates, proteins, and nucleic acids which are potential biomarkers for several diseases. IR spectroscopy has shown great promise for the diagnosis and monitoring of diseases such as dental caries, periodontitis, infectious diseases, cancer, diabetes mellitus, and chronic kidney disease, as well as for drug monitoring. Recent advancements in IR spectroscopy, such as Fourier-transform infrared (FTIR) spectroscopy and attenuated total reflectance (ATR) spectroscopy, have further enhanced its utility in salivary analysis. FTIR spectroscopy enables the collection of a complete IR spectrum of the sample, whereas ATR spectroscopy enables the analysis of samples in their native form, without the need for sample preparation. With the development of standardized protocols for sample collection and analysis and further advancements in IR spectroscopy, the potential for salivary diagnostics using IR spectroscopy is vast.
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Affiliation(s)
- Charlotte Delrue
- Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium
| | - Sander De Bruyne
- Department of Clinical Biology, Ghent University Hospital, 9000 Ghent, Belgium
| | - Marijn M Speeckaert
- Department of Nephrology, Ghent University Hospital, 9000 Ghent, Belgium
- Research Foundation-Flanders (FWO), 1000 Brussels, Belgium
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6
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Oliveira SW, Cardoso-Sousa L, Georjutti RP, Shimizu JF, Silva S, Caixeta DC, Guevara-Vega M, Cunha TM, Carneiro MG, Goulart LR, Jardim ACG, Sabino-Silva R. Salivary Detection of Zika Virus Infection Using ATR-FTIR Spectroscopy Coupled with Machine Learning Algorithms and Univariate Analysis: A Proof-of-Concept Animal Study. Diagnostics (Basel) 2023; 13:diagnostics13081443. [PMID: 37189545 DOI: 10.3390/diagnostics13081443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/08/2023] [Accepted: 03/11/2023] [Indexed: 05/17/2023] Open
Abstract
Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student's t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm-1 as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.
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Affiliation(s)
- Stephanie Wutke Oliveira
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
| | - Leia Cardoso-Sousa
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
| | - Renata Pereira Georjutti
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
- College of Dentistry, University Center of Triangle (UNITRI), Uberlandia 38411-869, Brazil
| | - Jacqueline Farinha Shimizu
- Laboratory of Antiviral Research, Institute of Biomedical Science, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
- Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, Brazil
| | - Suely Silva
- Laboratory of Antiviral Research, Institute of Biomedical Science, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
- Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, Brazil
| | - Douglas Carvalho Caixeta
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
| | - Marco Guevara-Vega
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
| | - Thúlio Marquez Cunha
- School of Medicine, Federal University of Uberlandia (UFU), Uberlandia 38408-100, Brazil
| | | | - Luiz Ricardo Goulart
- Institute of Biotechnology, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
| | - Ana Carolina Gomes Jardim
- Laboratory of Antiviral Research, Institute of Biomedical Science, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
- Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, Brazil
| | - Robinson Sabino-Silva
- Innovation Center in Salivary Diagnostic and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
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7
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Campanella B, Legnaioli S, Onor M, Benedetti E, Bramanti E. The Role of the Preanalytical Step for Human Saliva Analysis via Vibrational Spectroscopy. Metabolites 2023; 13:metabo13030393. [PMID: 36984834 PMCID: PMC10055013 DOI: 10.3390/metabo13030393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/10/2023] Open
Abstract
Saliva is an easily sampled matrix containing a variety of biochemical information, which can be correlated with the individual health status. The fast, straightforward analysis of saliva by vibrational (ATR-FTIR and Raman) spectroscopy is a good premise for large-scale preclinical studies to aid translation into clinics. In this work, the effects of saliva collection (spitting/swab) and processing (two different deproteinization procedures) were explored by principal component analysis (PCA) of ATR-FTIR and Raman data and by investigating the effects on the main saliva metabolites by reversed-phase chromatography (RPC-HPLC-DAD). Our results show that, depending on the bioanalytical information needed, special care must be taken when saliva is collected with swabs because the polymeric material significantly interacts with some saliva components. Moreover, the analysis of saliva before and after deproteinization by FTIR and Raman spectroscopy allows to obtain complementary biological information.
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Affiliation(s)
- Beatrice Campanella
- Institute of Chemistry of Organometallic Compounds (ICCOM), Consiglio Nazionale delle Ricerche(CNR), 56124 Pisa, Italy
| | - Stefano Legnaioli
- Institute of Chemistry of Organometallic Compounds (ICCOM), Consiglio Nazionale delle Ricerche(CNR), 56124 Pisa, Italy
| | - Massimo Onor
- Institute of Chemistry of Organometallic Compounds (ICCOM), Consiglio Nazionale delle Ricerche(CNR), 56124 Pisa, Italy
| | - Edoardo Benedetti
- Hematology Unit of Azienda Ospedaliero Universitaria Pisana (AOUP), 56100 Pisa, Italy
| | - Emilia Bramanti
- Institute of Chemistry of Organometallic Compounds (ICCOM), Consiglio Nazionale delle Ricerche(CNR), 56124 Pisa, Italy
- Correspondence: ; Tel.: +39-050-315-2293
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8
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Karas BY, Sitnikova VE, Nosenko TN, Dedkov VG, Arsentieva NA, Gavrilenko NV, Moiseev IS, Totolian AA, Kajava AV, Uspenskaya MV. ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals. JOURNAL OF BIOPHOTONICS 2023:e202200166. [PMID: 36869427 DOI: 10.1002/jbio.202200166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 01/08/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.
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Affiliation(s)
- Boris Y Karas
- Institute BioEngineering, ITMO University, St. Petersburg, Russia
| | - Vera E Sitnikova
- Institute BioEngineering, ITMO University, St. Petersburg, Russia
| | | | - Vladimir G Dedkov
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
- Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Natalia A Arsentieva
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
| | - Natalia V Gavrilenko
- Raisa Gorbacheva memorial Research Institute for Pediatric Oncology, Hematology and Transplantation, Pavlov First Saint Petersburg State Medical University, St. Petersburg, Russia
| | - Ivan S Moiseev
- Raisa Gorbacheva memorial Research Institute for Pediatric Oncology, Hematology and Transplantation, Pavlov First Saint Petersburg State Medical University, St. Petersburg, Russia
| | - Areg A Totolian
- Saint-Petersburg Pasteur Institute, Federal Service on Consumers' Rights Protection and Human Well-Being Surveillance, St. Petersburg, Russia
| | - Andrey V Kajava
- Centre de Recherche en Biologie cellulaire de Montpellier, Université Montpellier, Montpellier, France
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9
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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10
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Pushpa S, Sukumaran RK, Savithri S. Robustness of FTIR-Based Ultrarapid COVID-19 Diagnosis Using PLS-DA. ACS OMEGA 2022; 7:47357-47371. [PMID: 36570187 PMCID: PMC9773962 DOI: 10.1021/acsomega.2c06786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
The World Health Organization (WHO) declared the Omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for the Coronavirus disease 2019 (COVID-19) pandemic, as a variant of concern on 26 November 2021. By this time, 42% of the world's population had received at least one dose of the vaccine against COVID-19. As on 1 October 2022, only 68% of the world population got the first dose of the vaccine. Although the vaccination is incredibly protective against severe complications of the disease and death, the highly contagious Omicron variant, compared to the Delta variant (B.1.617.2), has led the whole world into more chaotic situations. Furthermore, the virus has a high mutation rate, and hence, the possibility of a new variant of concern in the future cannot be ruled out. To face such a challenging situation, paramount importance should be given to rapid diagnosis and isolation of the infected patient. Current diagnosis methods, including reverse transcription-polymerase chain reaction and rapid antigen tests, face significant burdens during a COVID-19 wave. However, studies reported ultrarapid, reagent-free, cost-efficient, and non-destructive diagnosis methods based on chemometrics for COVID-19 and COVID-19 severity diagnosis. These studies used a smaller sample cohort to construct the diagnosis model and failed to discuss the robustness of the model. The current study systematically evaluated the robustness of the diagnosis models trained using smaller (real and augmented spectra) and larger (augmented spectra) datasets. The Monte Carlo cross-validation and permutation test results suggest that diagnosis using models trained by larger datasets was accurate and statistically significant (Q 2 > 99% and AUROC = 100%).
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Affiliation(s)
- Sreejith
Remanan Pushpa
- Material
Science and Technology Division, CSIR-National
Institute for Interdisciplinary Science and Technology, Industrial Estate P.O., Thiruvananthapuram695019, Kerala, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad201002, India
| | - Rajeev Kumar Sukumaran
- Microbial
Processes and Technology Division, CSIR-National
Institute for Interdisciplinary Science and Technology, Industrial Estate P.O., Thiruvananthapuram695019, Kerala, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad201002, India
| | - Sivaraman Savithri
- Material
Science and Technology Division, CSIR-National
Institute for Interdisciplinary Science and Technology, Industrial Estate P.O., Thiruvananthapuram695019, Kerala, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad201002, India
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11
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Calvo-Gomez O, Calvo H, Cedillo-Barrón L, Vivanco-Cid H, Alvarado-Orozco JM, Fernandez-Benavides DA, Arriaga-Pizano L, Ferat-Osorio E, Anda-Garay JC, López-Macias C, López MG. Potential of ATR-FTIR-Chemometrics in Covid-19: Disease Recognition. ACS OMEGA 2022; 7:30756-30767. [PMID: 36092630 PMCID: PMC9453986 DOI: 10.1021/acsomega.2c01374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
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%.
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Affiliation(s)
- Octavio Calvo-Gomez
- Centro
de Investigación y de Estudios Avanzados del IPN, Km. 9.6 Libramiento Norte Carretera
Irapuato León, 36824 Irapuato, Guanajuato, Mexico
| | - Hiram Calvo
- Center
for Computing Research, Instituto Politécnico
Nacional, 07738 Mexico City, Mexico
| | - Leticia Cedillo-Barrón
- Centro
de Investigación y de Estudios Avanzados del IPN. Avenida IPN #2508, Col. San Pedro
Zacatenco, CP 07360 Mexico, Distrito Federal, Mexico
| | - Héctor Vivanco-Cid
- Laboratorio
Multidisciplinario en Ciencias Biomédicas, Instituto de Investigaciones
Médico-Biológicas, Universidad
Veracruzana, 91000Veracruz, Mexico
| | - Juan Manuel Alvarado-Orozco
- Centro
de Ingeniería y Desarrollo Industrial, Avenida Playa Pie de la Cuesta No.
702, Desarrollo San Pablo, 76125 Santiago de Querétaro, Mexico
| | - David Andrés Fernandez-Benavides
- Centro
de Ingeniería y Desarrollo Industrial, Avenida Playa Pie de la Cuesta No.
702, Desarrollo San Pablo, 76125 Santiago de Querétaro, Mexico
| | - Lourdes Arriaga-Pizano
- Unidad
de
Investigación Médica en Inmunoquímica, UMAE,
Hospital de Especialidades del Centro Médico Nacional Siglo
XXI. Instituto Mexicano del Seguro Social
(IMSS), 06600 Mexico City, Mexico
| | - Eduardo Ferat-Osorio
- Unidad
de
Investigación Médica en Inmunoquímica, UMAE,
Hospital de Especialidades del Centro Médico Nacional Siglo
XXI. Instituto Mexicano del Seguro Social
(IMSS), 06600 Mexico City, Mexico
| | - Juan Carlos Anda-Garay
- Unidad
de
Investigación Médica en Inmunoquímica, UMAE,
Hospital de Especialidades del Centro Médico Nacional Siglo
XXI. Instituto Mexicano del Seguro Social
(IMSS), 06600 Mexico City, Mexico
| | - Constantino López-Macias
- Unidad
de
Investigación Médica en Inmunoquímica, UMAE,
Hospital de Especialidades del Centro Médico Nacional Siglo
XXI. Instituto Mexicano del Seguro Social
(IMSS), 06600 Mexico City, Mexico
| | - Mercedes G. López
- Centro
de Investigación y de Estudios Avanzados del IPN, Km. 9.6 Libramiento Norte Carretera
Irapuato León, 36824 Irapuato, Guanajuato, Mexico
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12
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de Almeida C, Motta LC, Folli GS, Marcarini WD, Costa CA, Vilela ACS, Barauna VG, Martin FL, Singh MN, Campos LCG, Costa NL, Vassallo PF, Chaves AR, Endringer DC, Mill JG, Filgueiras PR, Romão W. MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19. J Proteome Res 2022; 21:1868-1875. [PMID: 35880262 PMCID: PMC9344790 DOI: 10.1021/acs.jproteome.2c00148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Indexed: 11/28/2022]
Abstract
Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.
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Affiliation(s)
- Camila
M. de Almeida
- Chemistry
Department, Federal University of Espírito
Santo, Vitória, ES 29040-090, Brazil
| | - Larissa C. Motta
- Chemistry
Department, Federal University of Espírito
Santo, Vitória, ES 29040-090, Brazil
| | - Gabriely S. Folli
- Chemistry
Department, Federal University of Espírito
Santo, Vitória, ES 29040-090, Brazil
| | - Wena D. Marcarini
- Department
of Physiological Sciences, Federal University
of Espírito Santo, Vitória, ES 29040-090, Brazil
| | - Camila A. Costa
- School
of Dentistry, Department of Stomatology (Oral Pathology), Federal University of Goiás, Goiânia, GO 74000-000, Brazil
| | - Ana C. S. Vilela
- School
of Dentistry, Department of Stomatology (Oral Pathology), Federal University of Goiás, Goiânia, GO 74000-000, Brazil
| | - Valério G. Barauna
- Department
of Physiological Sciences, Federal University
of Espírito Santo, Vitória, ES 29040-090, Brazil
| | | | - Maneesh N. Singh
- Biocel
UK Ltd., 15 Riplingham
Road, West Ella, Hull HU10
6TS, U.K.
| | - Luciene C. G. Campos
- Department
of Biological Science, Santa Cruz State
University, Ilhéus, BA 45662-900, Brazil
| | - Nádia L. Costa
- School
of Dentistry, Department of Stomatology (Oral Pathology), Federal University of Goiás, Goiânia, GO 74000-000, Brazil
| | - Paula F. Vassallo
- Clinical
Hospital, Federal University of Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Andrea R. Chaves
- Chromatography
and Mass Spectrometry Laboratory, Institute of Chemistry, Federal University of Goiás, Goiânia, GO 74690-900, Brazil
| | - Denise C. Endringer
- Pharmaceutical
Science Graduate Program, Universidade Vila
Velha, Vila Velha, ES 29106-010, Brazil
| | - José G. Mill
- Department
of Physiological Sciences, Federal University
of Espírito Santo, Vitória, ES 29040-090, Brazil
| | - Paulo R. Filgueiras
- Chemistry
Department, Federal University of Espírito
Santo, Vitória, ES 29040-090, Brazil
| | - Wanderson Romão
- Chemistry
Department, Federal University of Espírito
Santo, Vitória, ES 29040-090, Brazil
- Science
Department, Federal Institute of Education,
Science, and Technology of Espírito Santo, Vila Velha, ES 29106-010, Brazil
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13
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Spick M, Lewis HM, Frampas CF, Longman K, Costa C, Stewart A, Dunn-Walters D, Greener D, Evetts G, Wilde MJ, Sinclair E, Barran PE, Skene DJ, Bailey MJ. An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics. Sci Rep 2022; 12:11867. [PMID: 35831456 PMCID: PMC9278322 DOI: 10.1038/s41598-022-16123-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/05/2022] [Indexed: 12/15/2022] Open
Abstract
The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.
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Affiliation(s)
- Matt Spick
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Holly-May Lewis
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Cecile F Frampas
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Katie Longman
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Catia Costa
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
- Surrey Ion Beam Centre, University of Surrey, Guildford, GU2 7XH, UK
| | - Alexander Stewart
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Deborah Dunn-Walters
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Danni Greener
- Frimley Park Hospital, Frimley Health NHS Trust, Frimley, GU16 7UJ, UK
| | - George Evetts
- Frimley Park Hospital, Frimley Health NHS Trust, Frimley, GU16 7UJ, UK
| | - Michael J Wilde
- School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, PL4 8AA, UK
| | - Eleanor Sinclair
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Perdita E Barran
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
| | - Debra J Skene
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Melanie J Bailey
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
- Surrey Ion Beam Centre, University of Surrey, Guildford, GU2 7XH, UK.
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