Anti-IgG Doped Melanin Nanoparticles Functionalized Quartz Tuning Fork Immunosensors for Immunoglobulin G Detection: In Vitro and In Silico Study.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
03 Jul 2024
Historique:
received: 17 04 2024
revised: 29 06 2024
accepted: 30 06 2024
medline: 13 7 2024
pubmed: 13 7 2024
entrez: 13 7 2024
Statut: epublish

Résumé

The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork's small size and difficulty in modifying the prongs' surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.

Identifiants

pubmed: 39001098
pii: s24134319
doi: 10.3390/s24134319
pii:
doi:

Substances chimiques

Immunoglobulin G 0
Melanins 0
Quartz 14808-60-7
anti-IgG 0
Antibodies, Anti-Idiotypic 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Dilhan Gürcan (D)

Department of Biomedical Engineering, İzmir Democracy University, İzmir 35140, Türkiye.

Engin Baysoy (E)

Department of Biomedical Engineering, Bahçeşehir University, İstanbul 34353, Türkiye.

Gizem Kaleli-Can (G)

Department of Biomedical Engineering, İzmir Democracy University, İzmir 35140, Türkiye.

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Classifications MeSH