Suppr超能文献

基于纸的侧向流动分析的高灵敏度检测方法的最新进展。

Recent advances in high-sensitivity detection methods for paper-based lateral-flow assay.

机构信息

Department of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemoon-gu, Seoul, 03722, Republic of Korea.

School of Mechanical Engineering, Korea University of Technology and Education, Cheonan, Chungnam, 31253, Republic of Korea.

出版信息

Biosens Bioelectron. 2020 Mar 15;152:112015. doi: 10.1016/j.bios.2020.112015. Epub 2020 Jan 13.

Abstract

Paper-based lateral-flow assays (LFAs) have achieved considerable commercial success and continue to have a significant impact on medical diagnostics and environmental monitoring. Conventional LFAs are typically performed by examining the color changes in the test bands by naked eye. However, for critical biochemical markers that are present in extremely small amounts in the clinical specimens, this readout method is not quantitative, and does not provide sufficient sensitivity or suitable detection limit for a reliable assay. Diverse technologies for high-sensitivity LFA detection have been developed and commercialization efforts are underway. In this review, we aim to provide a critical and objective overview of the recent progress in high-sensitivity LFA detection technologies, which involve the exploitation of the physical and chemical responses of transducing particles. The features and biomedical applications of the technologies, along with future prospects and challenges, are also discussed.

摘要

基于纸的横向流动分析(LFA)已经取得了相当大的商业成功,并继续对医学诊断和环境监测产生重大影响。传统的 LFAs 通常通过肉眼检查测试带中的颜色变化来进行。然而,对于临床标本中存在的数量极其微小的关键生化标志物,这种读取方法不是定量的,并且对于可靠的测定没有足够的灵敏度或合适的检测限。已经开发出用于高灵敏度 LFA 检测的多种技术,并且正在进行商业化努力。在这篇综述中,我们旨在对涉及转换粒子的物理和化学响应的高灵敏度 LFA 检测技术的最新进展提供批判性和客观的概述。还讨论了这些技术的特点和生物医学应用,以及未来的前景和挑战。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验