Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 08 2024
20 08 2024
Historique:
received:
06
02
2024
accepted:
02
07
2024
medline:
21
8
2024
pubmed:
21
8
2024
entrez:
20
8
2024
Statut:
epublish
Résumé
Myasthenia 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; PO
Identifiants
pubmed: 39164310
doi: 10.1038/s41598-024-66501-3
pii: 10.1038/s41598-024-66501-3
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19316Subventions
Organisme : Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
ID : TUBITAK-1003 - 218S986
Organisme : Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
ID : TUBITAK-1003 - 218S987
Organisme : Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
ID : TUBITAK-1003 - 218S988
Informations de copyright
© 2024. The Author(s).
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