Electrochemical Detection of Plasma Immunoglobulin as a Biomarker for Alzheimer’s Disease

Abstract

The clinical diagnosis and treatment of Alzheimer’s disease (AD) represent a challenge to clinicians due to the variability of clinical symptomatology as well as the unavailability of reliable diagnostic tests. In this study, the development of a novel electrochemical assay and its potential to detect peripheral blood biomarkers to diagnose AD using plasma immunoglobulins is investigated. The immunosensor employs a gold electrode as the immobilizing substrate, albumin depleted plasma immunoglobulin as the biomarker, and polyclonal rabbit Anti-human immunoglobulin (against IgA, IgG, IgM) as the receptor for plasma conjugation. The assay showed good response, sensitivity and reproducibility in differentiating plasma immunoglobulin from AD and control subjects down to 10−9 dilutions of plasma immunoglobulin representing plasma content concentrations in the pg mL−1 range. The newly developed assay is highly sensitive, less time consuming, easy to handle, can be easily modified to detect other dementia-related biomarkers in blood samples, and can be easily integrated into portable devices.

A Disposable Saliva Electrochemical MIP-Based Biosensor for Detection of the Stress Biomarker α-Amylase in Point-of-Care Applications

“…Then, effective template protein immobilization over the pre-formed CA SAM was achieved mainly by hydrophilic interactions [38] between the thiolated surface and the enzyme but also through electrostatic interactions. At medium pH = 7.2, the amine groups at the CA SAM surface (pK a : 8.27 [39,40]) were expected to be slightly positively charged and attracted the negatively carboxylic acid groups of the enzyme (pK a : 6.0 to 6.9 [5]). The electropolymerization process was performed by incubating the chip surface with Py monomer solution followed by scanning the electrode potential between −0.2 V to 1.0 V, at 100 mV s −1 .…”

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study—Alzheimer’s Disease Diagnosis

“…These brain-observing techniques using machine learning can provide tools to overcome brain dysfunction problems. These combined techniques can use different modalities including MRI, PET, and other neurological data to diagnose AD/MCI patients from healthy people [18][19][20][21]. In [22] 50 MRI images from the OASIS dataset were used for characterization of MRIs of brains affected with Alzheimer's disease by fractal descriptors.…”

Emphasis Learning (EL), Feature Repetition in Width Instead of Length, to Obtain Better Classification Performance; Case Study: Alzheimer’s Disease Diagnosis

“…These brain observing techniques, using machine learning can provide nice tools to diagnosis and overcome brain's dysfunction problems. These combined techniques can use different modalities including MRI, PET, and other neurological data to diagnose AD/Mild Cognitive Impairment (MCI) patients from healthy people (NC) (Garyfallou et al, 2017;Islam et al, 2018;Maqsood et al, 2019;Toro & Gonzalo Martin, 2018). We can be sure that there exist unsight features among analyzed data in this area, that can help us for diagnosis and prognosis of AD/MCI.…”