Seçici Bakteriyel DNA Tespiti Amacıyla Geliştirilmiş İnosin Tabanlı Sinyal-Artışlı Elektrokimyasal Biyosensör
Makaleler > Tam Makale · 2026
Yazarlar: Kaya, Hüseyin Oğuzhan, Tekintaş, Yamaç, Hakan KARADENİZ, Topkaya Çetin, Seda Nur
Fabad Eczacılık Bilimler Dergisi, Vol. 51 No. 2, Uluslararası, 2026
Özet
Accurate and rapid identification of Shigella sonnei and Stenotrophomonas maltophilia remains challenging due to their close genomic similarity to other Gram-negative bacteria and their limited coverage in routine molecular diagnostic panels. In this study, we report a label-free electrochemical DNA biosensing strategy for the sequence-selective detection detection of these two clinically relevant but under-recognized pathogens. The biosensor is based on a pencil graphite electrode modified with carboxyl-functionalized single-walled carbon nanotubes (SWCNT-COOH), providing a conductive and chemically active interface for probe immobilization. To minimize intrinsic guanine-related background signals, inosine-substituted DNA probes were employed, enabling a hybridization-induced “signal-on” response that arises exclusively from the target DNA. Stable and reproducible probe attachment was achieved through optimized –COOH/–NH₂ interactions between the SWCNT interface and amine-terminated probes. Under optimized conditions, the biosensor exhibited linear responses in the 20–80 µg/mL range, with limits of detection of 7.3 µg/mL for S. sonnei and 10.2 µg/mL for S. maltophilia. The platform demonstrated effective discrimination against mismatched and non-complementary sequences and retained analytical performance in artificial saliva matrices, supporting its applicability in non-invasive sample environments. This work presents a simple and low-cost electrochemical approach for DNA-based detection of S. sonnei and S. maltophilia. The proposed sensing strategy highlights the potential of inosine-assisted, signal-on electrochemical readout as a modular platform for nucleic-acid detection, with relevance for decentralized and resource-limited diagnostic settings.