Machine learning powered detection of biological toxins in association with confined lateral flow immunoassay (c-LFA),Analyst

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Machine learning powered detection of biological toxins in association with confined lateral flow immunoassay (c-LFA)
Analyst ( IF 3.6 ) Pub Date : 2024-08-05 , DOI: 10.1039/d4an00593g
Seoyeon Choi _{1,

2} , Seongmin Ha ₁ , Chanmi Kim ₂ , Cheng Nie ₁ , Ju-Hong Jang ₃ , Jieun Jang _{3,

4} , Do Hyung Kwon _{3,

4} , Nam-Kyung Lee _{3,

4} , Jangwook Lee _{3,

4} , Ju Hwan Jeong ₅ , Wonjun Yang ₃ , Hyo-Il Jung _{1,

2}

Affiliation

Biological weapons, primarily dispersed as aerosols, can spread not only to the targeted area but also to adjacent regions following the movement of air driven by wind. Thus, there is a growing demand for toxin analysis because biological weapons are among the most influential and destructive. Specifically, such a technique should be hand-held, rapid, and easy to use because current methods require more time and well-trained personnel. Our study demonstrates the use of a novel lateral flow immunoassay, which has a confined structure like a double barbell in the detection area (so called c-LFA) for toxin detection such as staphylococcal enterotoxin B (SEB), ricinus communis (Ricin), and botulinum neurotoxin type A (BoNT-A). Additionally, we have explored the integration of machine learning (ML), specifically, a toxin chip boosting (TOCBoost) hybrid algorithm for improved sensitivity and specificity. Consequently, the ML powered c-LFA concurrently categorized three biological toxin types with an average accuracy as high as 95.5%. To our knowledge, the sensor proposed in this study is the first attempt to utilize ML for the assessment of toxins. The advent of the c-LFA orchestrated a paradigm shift by furnishing a versatile and robust platform for the rapid, on-site detection of various toxins, including SEB, Ricin, and BoNT-A. Our platform enables accessible and on-site toxin monitoring for non-experts and can potentially be applied to biosecurity.

中文翻译：

机器学习驱动的生物毒素检测与受限侧流免疫分析 (c-LFA) 相关

生物武器主要以气溶胶的形式传播，不仅可以传播到目标区域，还可以随着风驱动的空气运动传播到邻近地区。因此，对毒素分析的需求不断增长，因为生物武器是最具影响力和破坏性的武器之一。具体来说，这种技术应该是手持式的、快速且易于使用的，因为当前的方法需要更多的时间和训练有素的人员。我们的研究展示了使用新型侧流免疫测定法，该测定法在检测区域（所谓的 c-LFA）中具有像双杠铃一样的受限结构，用于毒素检测，例如葡萄球菌肠毒素 B (SEB)、蓖麻毒素 (Ricin)、和 A 型肉毒杆菌神经毒素 (BoNT-A)。此外，我们还探索了机器学习 (ML) 的集成，特别是毒素芯片增强 (TOCBoost) 混合算法，以提高灵敏度和特异性。因此，ML 支持的 c-LFA 同时对三种生物毒素类型进行分类，平均准确度高达 95.5%。据我们所知，本研究中提出的传感器是利用机器学习评估毒素的首次尝试。 c-LFA 的出现带来了范式转变，为快速现场检测各种毒素（包括 SEB、蓖麻毒素和 BoNT-A）提供了多功能且强大的平台。我们的平台可为非专家提供可访问的现场毒素监测，并有可能应用于生物安全。

更新日期：2024-08-05

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