College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
Qinghai Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai 810008, China.
ACS Nano. 2024 Oct 8;18(40):27167-27205. doi: 10.1021/acsnano.4c06564. Epub 2024 Sep 23.
Point-of-care (POC) nanosensors with high screening efficiency show promise for user-friendly manipulation in the ever-increasing on-site analysis demand for illness diagnosis, environmental monitoring, and food safety. Currently, inspired by the merits of integrating advanced nanomaterials, molecular biology, machine learning, and artificial intelligence, lateral flow immunoassay (LFIA)-based POC nanosensors have been devoted to satisfying the commercial demands in terms of sensitivity, specificity, and practicality. Herein, we examine the use of multidimensional enhanced LFIA in various fields over the past two decades, focusing on introducing advanced nanomaterials to improve the acquisition capability of small order of magnitude targets through engineering transformations and emphasizing interdomain fusion to collaboratively address the inherent challenges in current commercial applications, such as multiplexing, development of detectors for quantitative analysis, more practical on-site monitoring, and sensitivity enhancement. Specifically, this comprehensive review encompasses the latest advances in comprehending LFIA with an alternative signal transduction pattern, aiming to achieve rapid, ultrasensitive, and "sample-to-answer" available options with progressive applications for POC nanosensors. In summary, through the cross-collaboration development of disciplines, LFIA has the potential to break the barriers toward commercialization and achieve laboratory-level POC nanosensors, thus leading to the emergence of the next generation of LFIA.
即时检测 (POC) 纳米传感器具有高效的筛选效率，有望在疾病诊断、环境监测和食品安全等领域日益增加的现场分析需求中实现用户友好型操作。目前，基于侧向流动免疫分析 (LFIA) 的 POC 纳米传感器受到了将先进纳米材料、分子生物学、机器学习和人工智能等优势集成的启发，致力于满足在灵敏度、特异性和实用性方面的商业需求。在此，我们回顾了过去二十年中多维增强 LFIA 在各个领域的应用，重点介绍了通过工程改造引入先进纳米材料来提高小数量级目标的获取能力，并强调了域间融合，以协同解决当前商业应用中固有的挑战，如多重检测、定量分析检测仪器的开发、更实用的现场监测和灵敏度增强。具体而言，本综述涵盖了对替代信号转导模式的 LFIA 的最新理解进展，旨在实现快速、超灵敏和“样本到答案”的可用选项，并逐步应用于 POC 纳米传感器。总之，通过学科的交叉合作发展，LFIA 有可能打破商业化的障碍，实现实验室级别的 POC 纳米传感器，从而引领下一代 LFIA 的出现。
