Au-decorated Ti3C2Tx/porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy
Talanta, cilt.278, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 278
- Basım Tarihi: 2024
- Doi Numarası: 10.1016/j.talanta.2024.126507
- Dergi Adı: Talanta
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, L'Année philologique, Aerospace Database, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Food Science & Technology Abstracts, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
- Anahtar Kelimeler: Artificial neural network, ECM1 biomarker, Gold nanoparticles, Immunosensors, Machine learning, Porous carbons, Ti3C2Tx
- İstanbul Üniversitesi Adresli: Hayır
Özet
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 Ti3C2Tx-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 Ti3C2Tx. 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 Ti3C2Tx-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.