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@ARTICLE{Coles:282548,
      author       = {Coles, Nathan P and Elsheikh, Suzan and Gouda, Alaa and
                      Quesnel, Agathe and Butler, Lucy and Achadu, Ojodomo J and
                      Islam, Meez and Kalesh, Karunakaran and Occhipinti, Annalisa
                      and Angione, Claudio and Marles-Wright, Jon and Koss, David
                      J and Thomas, Alan J and Outeiro, Tiago F and Filippou,
                      Panagiota S and Khundakar, Ahmad A},
      title        = {{A} modified α-synuclein seed amplification assay in
                      {L}ewy body dementia using {R}aman spectroscopy and machine
                      learning analysis.},
      journal      = {Journal of neuroscience methods},
      volume       = {425},
      issn         = {0165-0270},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2025-01311},
      pages        = {110617},
      year         = {2026},
      abstract     = {Lewy 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.},
      keywords     = {Humans / Lewy Body Disease: cerebrospinal fluid / Lewy Body
                      Disease: diagnosis / alpha-Synuclein: cerebrospinal fluid /
                      Spectrum Analysis, Raman: methods / Machine Learning / Male
                      / Female / Aged / Aged, 80 and over / Proof of Concept Study
                      / Parkinson Disease: cerebrospinal fluid / Parkinson
                      Disease: diagnosis / Principal Component Analysis /
                      Biomarkers: cerebrospinal fluid / Diagnostics (Other) / Lewy
                      body dementia (Other) / Machine learning analysis (Other) /
                      Raman spectroscopy (Other) / α-synuclein aggregation
                      (Other) / alpha-Synuclein (NLM Chemicals) / Biomarkers (NLM
                      Chemicals)},
      cin          = {AG Fischer},
      ddc          = {610},
      cid          = {I:(DE-2719)1410002},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41238048},
      doi          = {10.1016/j.jneumeth.2025.110617},
      url          = {https://pub.dzne.de/record/282548},
}