Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly with an incidence that progressively increases worldwide [1]. One of the main neuropathological hallmarks of AD is the presence of amyloid-b protein (Ab) aggregates which forms extracellular amyloid plaques [2]. At present, clinical diagnosis of AD relies on NINCDS-ADRDA (National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association) criteria that permit to classify the disease as possible or probable, but not definite (which still requires neuropathological examinations) [3]. This is partially due to the fact that the clinical, laboratory and instrumental biomarkers investigated are not specific for AD and can be altered in other neurodegenerative conditions [4]. In this work, we present an innovative approach in which a seed amplification assay (SAA) capable to detect traces of pathological Ab species in the cerebrospinal fluid (CSF) of patients with AD [5] is combined with Surface Enhanced Raman Spectroscopy for the ultrasensitive analysis of CSF collected from extensively-characterized patients with AD or other neurological conditions. Our findings show that SERS analysis of SAA end products through an optimized low-cost silver nanowires/PTFE SERS-active substrate [6,7], supported by machine learning approach [8] and correlated with the other clinical, instrumental and laboratory findings, could reveal chemo-structural information useful to distinguish AD from other neurological diseases in living patients.

Surface-enhanced Raman scattering with nanophotonic and biomedical amplifying systems for a more accurate diagnosis of Alzheimer's disease

Cristiano D'Andrea;Martina Banchelli;Edoardo Farnesi;Panagis Polykretis;Chiara Marzi;Marella de Angelis;Andrea Barucci;Paolo Matteini
2022

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly with an incidence that progressively increases worldwide [1]. One of the main neuropathological hallmarks of AD is the presence of amyloid-b protein (Ab) aggregates which forms extracellular amyloid plaques [2]. At present, clinical diagnosis of AD relies on NINCDS-ADRDA (National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association) criteria that permit to classify the disease as possible or probable, but not definite (which still requires neuropathological examinations) [3]. This is partially due to the fact that the clinical, laboratory and instrumental biomarkers investigated are not specific for AD and can be altered in other neurodegenerative conditions [4]. In this work, we present an innovative approach in which a seed amplification assay (SAA) capable to detect traces of pathological Ab species in the cerebrospinal fluid (CSF) of patients with AD [5] is combined with Surface Enhanced Raman Spectroscopy for the ultrasensitive analysis of CSF collected from extensively-characterized patients with AD or other neurological conditions. Our findings show that SERS analysis of SAA end products through an optimized low-cost silver nanowires/PTFE SERS-active substrate [6,7], supported by machine learning approach [8] and correlated with the other clinical, instrumental and laboratory findings, could reveal chemo-structural information useful to distinguish AD from other neurological diseases in living patients.
2022
Istituto di Fisica Applicata - IFAC
Inglese
Plasmonica 2022
Sì, ma tipo non specificato
7-8/07/2022
Politecnico di Torino, Torino, Italy
SERS
Surface Enhanced Raman Scattering
Alzheimer disease
machine learning
8
info:eu-repo/semantics/conferenceObject
none
274
04 Contributo in convegno::04.02 Abstract in Atti di convegno
Cristiano D'Andrea; Martina Banchelli; Edoardo Farnesi; Panagis Polykretis;Chiara Marzi; Edoardo Bistaffa; Federico Cazzaniga; Pietro Tiraboschi;Marel...espandi
   EUROpean network for transnational collaborative RTD projects in the field of NANOMEDicine
   EURONANOMED II
   FP7
   321570
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/419451
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