Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning

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dc.contributor.authorSevercan, Feride
dc.contributor.authorÖzyurt, İpek
dc.contributor.authorDoğan, Ayça
dc.contributor.authorSevercan, Mete
dc.contributor.authorGurbanov, Rafig
dc.contributor.authorKüçükcankurt, Fulya
dc.contributor.authorElibol, Birsen
dc.contributor.authorTiftikçioğlu, İrem
dc.contributor.authorGürsoy, Esra
dc.contributor.authorYangın, Melike Nur
dc.contributor.authorZorlu, Yaşar
dc.date.accessioned2024-09-02T06:14:45Z
dc.date.available2024-09-02T06:14:45Z
dc.date.issued2024en_US
dc.departmentFakülteler, Tıp Fakültesi, Temel Tıp Bilimleri, Biyofizik Ana Bilim Dalıen_US
dc.description.abstractMyasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.en_US
dc.identifier.citationSevercan, F., Özyurt, İ., Doğan, A., Severcan, M., Gurbanov, R., Küçükcankurt, F., Elibol, B., Tiftikçioğlu, İ., Gürsoy, E., Yangın, M. N., Zorlu, Y. (2024). Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning. Scientific Reports, 14(1). 10.1038/s41598-024-66501-3en_US
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85201851975
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4810
dc.identifier.volume14en_US
dc.identifier.wosWOS:001295308500051
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSevercan, Feride
dc.institutionauthorÖzyurt, İpek
dc.institutionauthorDoğan, Ayça
dc.institutionauthorKüçükcankurt, Fulya
dc.institutionauthorYangın, Melike Nur
dc.language.isoen
dc.relation.ispartofScientific Reports
dc.relation.isversionof10.1038/s41598-024-66501-3en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMyasthenia Gravis (MG)en_US
dc.subjectNeurological Diseasesen_US
dc.subjectPrincipal Component Analysis (PCA)en_US
dc.titleDecoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning
dc.typeArticle

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