Development of a Label-Free Immunosensor for Clusterin Detection as an Alzheimer’s Biomarker

Islam, Kamrul; Damiati, Samar; Sethi, Jagriti; Suhail, Ahmed; Pan, G.

doi:10.3390/s18010308

Cited by 31 publications

(19 citation statements)

References 52 publications

(53 reference statements)

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“…This is due to an increase in the electron transfer resistance as it acquires available electroactive sites on rGO. The pyrene moiety binds to the rGO surface via non-covalent bonding (π-π interaction), whereas ester group forms an amide bond (covalent bonding) with the antibody [25]. The I pa increases to 214.987 μA after the immobilization of antibody.…”

Section: Characterization Of the Biosensormentioning

confidence: 99%

“…Therefore, noncovalent modification methods are a preferred choice. 1-Pyrenebutyric acid N-hydroxysuccinimide ester (Pyr-NHS) is a bi-functional molecule which binds non-covalently to graphene and carbon surfaces [25,26]. It promotes strong immobilization of antibodies without any adverse effect on the underlying structure.…”

Section: Introductionmentioning

confidence: 99%

“…It promotes strong immobilization of antibodies without any adverse effect on the underlying structure. This has shown to improve sensitivity for detection of clusterin, another well-known biomarker of AD [25]. Thus, Pyr-NHS is an excellent linker for the immobilization of probes on the graphene biosensors.…”

Section: Introductionmentioning

confidence: 99%

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A label-free biosensor based on graphene and reduced graphene oxide dual-layer for electrochemical determination of beta-amyloid biomarkers

et al. 2020

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A label-free biosensor is developed for the determination of plasma-based Aβ 1-42 biomarker in Alzheimer's disease (AD). The platform is based on highly conductive dual-layer of graphene and electrochemically reduced graphene oxide (rGO). The modification of dual-layer with 1-pyrenebutyric acid N-hydroxysuccinimide ester (Pyr-NHS) is achieved to facilitate immobilization of H31L21 antibody. The effect of these modifications were studied with morphological, spectral and electrochemical techniques. The response of the biosensor was evaluated using differential pulse voltammetry (DPV). The data was acquired at a working potential of~180 mV and a scan rate of 50 mV s −1. A low limit of detection (LOD) of 2.398 pM is achieved over a wide linear range from 11 pM to 55 nM. The biosensor exhibits excellent specificity over Aβ 1-40 and ApoE ε4 interfering species. Thus, it provides a viable tool for electrochemical determination of Aβ 1-42. Spiked human and mice plasmas were used for the successful validation of the sensing platform in bio-fluidic samples. The results obtained from mice plasma analysis concurred with the immunohistochemistry (IHC) and magnetic resonance imaging (MRI) data obtained from brain analysis.

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Section: Characterization Of the Biosensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A label-free biosensor based on graphene and reduced graphene oxide dual-layer for electrochemical determination of beta-amyloid biomarkers

et al. 2020

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Akramifard

Balafar

Razavi

et al. 2020

Sensors

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In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer's disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model's accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer's disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

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“…1-pyrenebutyric acid-N-hydroxy-succinimide ester (PANHS) is a linker that can be used as a scaffolding molecule to modify electrode surface by providing a suitable base for immobilizing antibodies. PANHS has been immobilized on carbon or graphene sheets (17,18), and carbon nanotubes through π-stacking and its succinimidyl ester group can react strongly to nucleophilic substitution by the amine groups of antibody or protein (19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Comparison of Macro- and Micro-Electrochemical Biosensors for Human Chorionic Gonadotropin (hCG) Biomarker Detection

et al. 2019

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Selectivity and sensitivity are important figures of merit in the design and optimization of electrochemical biosensors. The efficiency of the fabricated immunosensing surface can easily be influenced by several factors, such as detection limit, non-specific binding, and type of sensing platform. Here, we demonstrate the effect of macro-and micro-sized planner working electrodes (4 mm and 400 µm diameter, respectively) on the electrochemical behavior and the performance of the developed biosensor to detect human chorionic gonadotropin (hCG), please say why hCG here briefly. The fabricated screen-printed sensor was constructed by modifying the carbon macro-and micro-electrodes with a linker, 1-pyrenebutyric acid-Nhydroxysuccinimide ester (PANHS) and immobilization of anti-hCG antibodies to detect specifically the hCG protein. The characterization of the developed electrodes was performed by cyclic voltammetry (CV) and square wave voltammetry (SWV). Each immunesensing system has its unique electrochemical behavior which might be attributed to arrangement of particles on the surface. However, the smaller surface area of the micro-electrode is found to show higher sensitivity (1 pg/mL) compared to the macro-electrode sensor with a lower detection limit of 100 pg/mL. The proposed assay represents a promising approach that is highly effective for specific detection of an analyte and can be exploited to target biomarkers for a variety of point-of-care diagnostic applications.

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Development of a Label-Free Immunosensor for Clusterin Detection as an Alzheimer’s Biomarker

Cited by 31 publications

References 52 publications

A label-free biosensor based on graphene and reduced graphene oxide dual-layer for electrochemical determination of beta-amyloid biomarkers

A label-free biosensor based on graphene and reduced graphene oxide dual-layer for electrochemical determination of beta-amyloid biomarkers

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

Sensitivity Comparison of Macro- and Micro-Electrochemical Biosensors for Human Chorionic Gonadotropin (hCG) Biomarker Detection

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