1. Au-decorated Ti 3 C 2 T x /porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy.
- Author
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Tumrani SH, Soomro RA, Thabet HK, Karakuş S, El-Bahy ZM, Küçükdeniz T, and Khoso S
- Subjects
- Humans, Porosity, Female, Immunoassay methods, Electrochemical Techniques methods, Extracellular Matrix Proteins chemistry, Extracellular Matrix Proteins analysis, Limit of Detection, Gold chemistry, Breast Neoplasms diagnosis, Biomarkers, Tumor analysis, Carbon chemistry, Titanium chemistry, Metal Nanoparticles chemistry, Machine Learning, Biosensing Techniques methods
- Abstract
Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti
3 C2 Tx -MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3 C2 Tx . The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3 C2 Tx -Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1-7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Selcan Karakus reports administrative support and writing assistance were provided by Istanbul University-Cerrahpasa. No relationship or activity. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)- Published
- 2024
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