Multivariate Statistical Analysis of Surface Enhanced Raman Spectra of Human Serum for Alzheimer’s Disease Diagnosis
Alzheimer’s disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer’s disease (AD) patients was analyzed by surface-en...
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| Published in | Applied sciences Vol. 9; no. 16; p. 3256 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.08.2019
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| Online Access | Get full text |
| ISSN | 2076-3417 2076-3417 |
| DOI | 10.3390/app9163256 |
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| Abstract | Alzheimer’s disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer’s disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD. |
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| AbstractList | Featured ApplicationIn this research we propose a novel method for detecting Alzheimer’s disease. This method involves the use of surface enhanced Raman spectroscopy in combination with multivariate statistical analysis. Based on the results of the proof-of-concept study, we have successfully demonstrated the potential of the method to identify Alzheimer’s disease through analysis of blood serum. With further work, this method could be developed into a novel clinical assay for the effective and accurate diagnosis of Alzheimer’s disease.AbstractAlzheimer’s disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer’s disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD. Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer's disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD. |
| Author | Zimmerman, Earl A. Ralbovsky, Nicole M. Quinn, Joseph Lednev, Igor K. Ryzhikova, Elena Celmins, Dzintra Malone, Paula Molho, Eric Halámková, Lenka |
| Author_xml | – sequence: 1 givenname: Elena surname: Ryzhikova fullname: Ryzhikova, Elena – sequence: 2 givenname: Nicole M. surname: Ralbovsky fullname: Ralbovsky, Nicole M. – sequence: 3 givenname: Lenka surname: Halámková fullname: Halámková, Lenka – sequence: 4 givenname: Dzintra surname: Celmins fullname: Celmins, Dzintra – sequence: 5 givenname: Paula surname: Malone fullname: Malone, Paula – sequence: 6 givenname: Eric surname: Molho fullname: Molho, Eric – sequence: 7 givenname: Joseph surname: Quinn fullname: Quinn, Joseph – sequence: 8 givenname: Earl A. surname: Zimmerman fullname: Zimmerman, Earl A. – sequence: 9 givenname: Igor K. orcidid: 0000-0002-6504-531X surname: Lednev fullname: Lednev, Igor K. |
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| Snippet | Alzheimer’s disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and... Featured ApplicationIn this research we propose a novel method for detecting Alzheimer’s disease. This method involves the use of surface enhanced Raman... Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and... |
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| SubjectTerms | Alzheimer's disease artificial neural networks Biomarkers blood serum differential diagnosis genetic algorithm Parkinson's disease silver colloidal nanoparticles Spectrum analysis surface enhanced Raman spectroscopy |
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| Title | Multivariate Statistical Analysis of Surface Enhanced Raman Spectra of Human Serum for Alzheimer’s Disease Diagnosis |
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