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Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

Alves, Marcelo V S ; Maciel, Lanaia I L ; Ramalho, Ruver R F ; Lima, Leomir A S ; Vaz, Boniek G ; Morais, Camilo L M ; Passos, João O S ; Pegado, Rodrigo ; Lima, Kássio M G

Scientific reports, 2021-11, Vol.11 (1), p.22625-22625, Article 22625 [Peer Reviewed Journal]

England: Nature Publishing Group

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Title:
Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia
Author: Alves, Marcelo V S ; Maciel, Lanaia I L ; Ramalho, Ruver R F ; Lima, Leomir A S ; Vaz, Boniek G ; Morais, Camilo L M ; Passos, João O S ; Pegado, Rodrigo ; Lima, Kássio M G
Subjects: Algorithms ; Case-Control Studies ; Chemistry Techniques, Analytical ; Chronic pain ; Classification ; Computer Simulation ; Diagnosis ; Discriminant Analysis ; Fibromyalgia ; Fibromyalgia - blood ; Fibromyalgia - diagnosis ; Humans ; Ionization ; Machine Learning ; Mass Screening - methods ; Mass spectrometry ; Mass Spectrometry - methods ; Mass spectroscopy ; Metabolomics ; Metabolomics - methods ; Multivariate Analysis ; Principal Component Analysis ; Principal components analysis ; Scientific imaging ; Sensitivity and Specificity
Is Part Of: Scientific reports, 2021-11, Vol.11 (1), p.22625-22625, Article 22625
Notes: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Description: Fibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.
Publisher: England: Nature Publishing Group
Language: English

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Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

Alves, Marcelo V S ; Maciel, Lanaia I L ; Ramalho, Ruver R F ; Lima, Leomir A S ; Vaz, Boniek G ; Morais, Camilo L M ; Passos, João O S ; Pegado, Rodrigo ; Lima, Kássio M G

England: Nature Publishing Group

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