MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1021/acssensors.6c02118

Медицинская библиотека · ACS sensors · 2026

Nanobody-Based Lateral Flow Assays: Applications, Innovations, Challenges, and Future Directions.

Peng Yuli, Gómez-Castillo María, Hoehndorf Robert, Kiessling Fabian, Renn Dominik, Rueping Magnus

СтатьяПеревод пока не подготовлен

Аннотация

Nanobody-based lateral flow assays (LFAs) are an emerging class of point-of-care diagnostics that combine enhanced thermal stability, target affinity comparable to or in some cases exceeding that of conventional antibodies, and low-cost recombinant production of nanobodies with the versatility of conventional antibody-based detection formats. This review examines recent progress in nanobody engineering, assay architecture, and signal amplification strategies that improve analytical sensitivity, specificity, and multiplexing potential. Despite these advances, several technical and translational challenges persist, including broad analyte compatibility, difficulties in optimal nanobody pair selection, scalability of expression systems, and the need for robust and reproducible conjugation chemistries. The growing integration of computational modeling and AI-driven nanobody design has accelerated the discovery and optimization pipelines, yet standardized frameworks for validation and benchmarking remain scarce. Addressing these limitations will be important for the further clinical translation and commercial deployment of nanobody-based LFAs across diverse applications in infectious disease diagnostics, food safety testing, and environmental monitoring.

Для цитирования: Peng Yuli, Gómez-Castillo María, Hoehndorf Robert, Kiessling Fabian, Renn Dominik, Rueping Magnus. Nanobody-Based Lateral Flow Assays: Applications, Innovations, Challenges, and Future Directions.. ACS sensors. 2026. DOI: 10.1021/acssensors.6c02118.
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.