Fast and accurate identification of pathogenic bacteria using excitation–emission spectroscopy and machine learning†

IF 3.5 Q2 CHEMISTRY, ANALYTICAL
Jacob Henry, Jennifer L. Endres, Marat R. Sadykov, Kenneth W. Bayles and Denis Svechkarev
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引用次数: 0

Abstract

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation–emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4′-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye–bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation–emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.

Abstract Image

利用激发-发射光谱和机器学习快速准确地识别病原菌
快速可靠地鉴定病原菌对人类健康和安全至关重要。目前在临床实践中使用的方法往往费时费力,需要昂贵的设备和训练有素的人员,因此在资源匮乏的环境中应用有限。分子鉴定方法弥补了其中的一些不足。同时,这些方法通常使用抗体、抗体片段或其他生物大分子作为识别单元,这使得此类检测对特定目标具有特异性。与此相反,基于阵列的方法使用的是不针对单一病原体的报告物组合。这些方法提供的数据更丰富,反应更普遍,可用于鉴定各种感兴趣的细菌。在本报告中,我们展示了一种环境敏感性荧光染料的激发-发射光谱在病原菌鉴定中的应用。2-(4'-二甲基氨基)-3-羟基黄酮(DMAF)与细菌细胞包膜相互作用,产生每种细菌特有的光谱响应。对染料与细菌相互作用的动态进行了深入研究,并确定了检测和识别的极限。采用神经网络分类算法对光谱数据进行模式识别分析和分类。传感器成功区分了八种具有代表性的病原菌,在物种水平上的分类准确率达到 98.2%,在革兰氏状态水平上的分类准确率达到 99.8%。所提出的基于环境敏感荧光染料激发-发射光谱的方法是一种功能强大、用途广泛的诊断工具,在鉴定细菌病原体方面具有很高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.30
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0.00%
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