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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
