Yuan Hang, Yong Ruiqi, Yuan Wenwen, Zhang Quan, Lim Eng Gee, Wang Yongjie, Niu Fuzhou, Song Pengfei
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 7ZX, UK.
Microsyst Nanoeng. 2025 May 22;11(1):101. doi: 10.1038/s41378-025-00923-5.
Lateral flow assays (LFAs) are widely used in point-of-care testing (POCT) due to their simplicity and rapid operation. However, their reliance on passive capillary flow limits sensitivity, making it challenging to detect low-abundance biomarkers accurately. Approaches such as computer signal processing, chemical modification, and physical regulation have been explored to improve LFA sensitivity, but they remain limited by passive capillary-driven flow and uncontrollable flow rate. An alternative approach is to actively regulate fluid dynamics to optimize analyte binding and signal generation. The key challenge is to enhance LFA sensitivity while preserving compatibility with existing lateral flow strips (LFSs). Here, this study introduces a centrifugation-assisted LFA (CLFA) platform with smartphone-based result processing. This platform applies centrifugal force opposite to capillary flow, actively regulating fluid movement to optimize incubation time at the reaction zone and enhance detection performance. This approach increases signal intensity while maintaining a rapid detection process (5 min) and ensuring integration with traditional LFSs. As a proof-of-concept, the CLFA platform successfully detected human chorionic gonadotropin (hCG) and hemoglobin (Hb) in artificial urine without requiring custom-designed centrifugal discs or modified chromatography membranes. Its adaptability to diverse biomarkers and smartphone-based quantification make it a promising POCT tool, particularly in resource-limited settings.
侧向流动分析（LFA）因其操作简单、快速，在即时检测（POCT）中被广泛应用。然而，其对被动毛细管流动的依赖限制了灵敏度，使得准确检测低丰度生物标志物具有挑战性。人们已经探索了诸如计算机信号处理、化学修饰和物理调节等方法来提高LFA的灵敏度，但它们仍然受到被动毛细管驱动流动和不可控流速的限制。另一种方法是主动调节流体动力学，以优化分析物结合和信号产生。关键挑战在于提高LFA灵敏度的同时，保持与现有侧向流动条（LFS）的兼容性。在此，本研究引入了一种基于智能手机结果处理的离心辅助LFA（CLFA）平台。该平台施加与毛细管流动相反的离心力，主动调节流体运动，以优化反应区的孵育时间并提高检测性能。这种方法在保持快速检测过程（5分钟）的同时增加了信号强度，并确保与传统LFS集成。作为概念验证，CLFA平台在无需定制离心盘或改性色谱膜的情况下，成功检测了人工尿液中的人绒毛膜促性腺激素（hCG）和血红蛋白（Hb）。其对多种生物标志物的适应性以及基于智能手机的定量分析，使其成为一种有前景的POCT工具，尤其是在资源有限的环境中。
