| Yazarlar (4) |
Hilal Bedir
Kafkas Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Mehmet EZER
Kafkas Üniversitesi, Türkiye |
|
Mükremin Özkan Arslan
Kafkas Üniversitesi, Türkiye |
Doç. Dr. Zihni Onur UYGUN
Kafkas Üniversitesi, Türkiye |
| Özet |
| In this study, a calpastatin-modified indium–tin oxide (ITO) electrochemical biosensor coupled with machine-learning–guided scanning electrochemical microscopy (SECM) operated in feedback mode. Electrochemical impedance spectroscopy (EIS) provided quantitative calibration via charge-transfer resistance (Rct), while SECM produced spatial maps to localize individual eggs. A lightweight classifier analyzed survey tiles online and triggered event-zoom scans around putative targets. Analytical performance was evaluated in urine, including selectivity against common urinary interferents. Egg binding leads to increase in Rct with linear calibration from 2 to 400 eggs per 0.2 cm2 (log-linear R2 ≈ 0.993). LOD 1 egg and LOQ 2 eggs per 0.2 cm2, with repeatability CV typically 2–5 % across five replicates and close overlap of independent calibration runs (high reproducibility). Integrating a selective calpastatin–ITO interface with ML-guided feedback-mode SECM enables rapid, label-free detection and localization of Schistosoma haematobium eggs at diagnostically relevant low counts, with strong selectivity and practical. |
| Anahtar Kelimeler |
| Electrochemical biosensor | Impedance spectroscopy | Machine learning | Schistosoma haematobium | Schistosomiasis | SECM |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Microchemical Journal |
| Dergi ISSN | 0026-265X Wos Dergi Scopus Dergi |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2026 |
| Cilt No | 220 |
| Sayı | 1 |
| DOI Numarası | 10.1016/j.microc.2025.116647 |
| Atıf Sayıları |
