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Published on: February 7, 2017
Analytical Performance of Nanobody-Based Immunoassay and Immunosensing Platforms for Bacteria and Toxin Detection: A
Aya Jalil1, Nadia Touil2,3, Omar Nyabi4
1Health Sciences Research Center, Higher School of Biomedical Engineering, College of Health Sciences, International University of Rabat, Technopolis Parc, Rocade of Rabat-Salé, Sala-Al Jadida 11100, Morocco.
Abstract:
Background: bacterial pathogens and their toxins present analytical challenges for rapid and specific detection, contributing to over 600 million cases of illness annually and worsening antimicrobial resistance (AMR). Conventional detection methods are useful but limited. Single-domain antibodies (sdAbs) offer alternative recognition elements with unique biochemical and engineering benefits, enabling the development of nanobody-based immunoassays and biosensing platforms that provide fast, highly selective, and reliable detection of bacterial pathogens and toxins in both food and clinical environments. Objectives: this systematic review assesses the analytical and functional performance of nanobody-based immunoassays and sensing formats for detecting bacteria and toxins across food and clinical samples. Methods: following PRISMA guidelines, major scientific databases were used to gather research, resulting in 32 eligible studies published between 2011 and 2025. Results: data collected included assay platforms, target bacteria and toxins, limit of detection, sensitivity, specificity, matrix recovery, and practicality. Risk of bias was evaluated using an adapted QUADAS-2 framework. The review shows that nanobody-based immunoassays have achieved high performance, thermostability, compatibility with genetic engineering, and versatile assay design. When combined with advanced transduction and signal amplification strategies, these systems contribute to the development of highly sensitive and user-friendly bioanalytical platforms for detecting bacteria and toxins. Conclusions: however, most studies relied on spiked samples and lacked large-scale validation, emphasizing the need for standardized benchmarking and real-world testing.
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