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000282548 037__ $$aDZNE-2025-01311
000282548 041__ $$aEnglish
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000282548 1001_ $$aColes, Nathan P$$b0
000282548 245__ $$aA modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.
000282548 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2026
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000282548 520__ $$aLewy body dementias (LBD), comprising dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), are defined by misfolded α-synuclein aggregation. Seed amplification assays (SAAs), such as RT-QuIC, enable sensitive detection of α-synuclein aggregates but typically provide binary readouts and require fluorescence labeling. Raman spectroscopy offers a label-free approach to detect subtle biochemical changes, and its diagnostic potential can be enhanced with machine learning.This proof-of-concept study aimed to evaluate whether Raman spectroscopy combined with machine learning can improve SAA-based discrimination of LBD from controls in cerebrospinal fluid (CSF).We analyzed a small number of post-mortem CSF samples from pathologically confirmed DLB (n = 2), PDD (n = 2), and controls (n = 2) using a 7-day SAA. Raman spectra were collected on Days 1, 4, and 7 and analyzed using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP).Following SAA, both PCA and UMAP distinguished combined LBD samples from controls within 24 h (Day 1), reflecting biochemical changes consistent with α-synuclein fibrillation. Spectral shifts indicated decreased α-helical content with increased β-sheet structures. No consistent separation between DLB and PDD was observed.This preliminary study demonstrates that combining Raman spectroscopy with machine learning can enable rapid, label-free detection of disease-specific changes. Despite the very limited sample size, these findings highlight the potential of this novel workflow and strongly warrant its validation in larger cohorts.
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000282548 650_7 $$2Other$$aDiagnostics
000282548 650_7 $$2Other$$aLewy body dementia
000282548 650_7 $$2Other$$aMachine learning analysis
000282548 650_7 $$2Other$$aRaman spectroscopy
000282548 650_7 $$2Other$$aα-synuclein aggregation
000282548 650_7 $$2NLM Chemicals$$aalpha-Synuclein
000282548 650_7 $$2NLM Chemicals$$aBiomarkers
000282548 650_2 $$2MeSH$$aHumans
000282548 650_2 $$2MeSH$$aLewy Body Disease: cerebrospinal fluid
000282548 650_2 $$2MeSH$$aLewy Body Disease: diagnosis
000282548 650_2 $$2MeSH$$aalpha-Synuclein: cerebrospinal fluid
000282548 650_2 $$2MeSH$$aSpectrum Analysis, Raman: methods
000282548 650_2 $$2MeSH$$aMachine Learning
000282548 650_2 $$2MeSH$$aMale
000282548 650_2 $$2MeSH$$aFemale
000282548 650_2 $$2MeSH$$aAged
000282548 650_2 $$2MeSH$$aAged, 80 and over
000282548 650_2 $$2MeSH$$aProof of Concept Study
000282548 650_2 $$2MeSH$$aParkinson Disease: cerebrospinal fluid
000282548 650_2 $$2MeSH$$aParkinson Disease: diagnosis
000282548 650_2 $$2MeSH$$aPrincipal Component Analysis
000282548 650_2 $$2MeSH$$aBiomarkers: cerebrospinal fluid
000282548 7001_ $$aElsheikh, Suzan$$b1
000282548 7001_ $$aGouda, Alaa$$b2
000282548 7001_ $$aQuesnel, Agathe$$b3
000282548 7001_ $$aButler, Lucy$$b4
000282548 7001_ $$aAchadu, Ojodomo J$$b5
000282548 7001_ $$aIslam, Meez$$b6
000282548 7001_ $$aKalesh, Karunakaran$$b7
000282548 7001_ $$aOcchipinti, Annalisa$$b8
000282548 7001_ $$aAngione, Claudio$$b9
000282548 7001_ $$aMarles-Wright, Jon$$b10
000282548 7001_ $$aKoss, David J$$b11
000282548 7001_ $$aThomas, Alan J$$b12
000282548 7001_ $$0P:(DE-2719)2814138$$aOuteiro, Tiago F$$b13$$udzne
000282548 7001_ $$aFilippou, Panagiota S$$b14
000282548 7001_ $$aKhundakar, Ahmad A$$b15
000282548 773__ $$0PERI:(DE-600)1500499-5$$a10.1016/j.jneumeth.2025.110617$$gVol. 425, p. 110617 -$$p110617$$tJournal of neuroscience methods$$v425$$x0165-0270$$y2026
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