Svechkarev Denis, Sadykov Marat R, Houser Lucas J, Bayles Kenneth W, Mohs Aaron M
Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE, United States.
Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, United States.
Front Chem. 2020 Jan 15;7:916. doi: 10.3389/fchem.2019.00916. eCollection 2019.
Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.
快速可靠地识别传染病病原体是医疗系统面临的最重要挑战之一。区分混合感染的各个成分是一项特别困难的任务。在本研究中，我们进一步扩展了基于小分子环境敏感有机染料的比率传感器阵列技术的功能，该技术可成功应用于混合细菌样本的分析。使用模式识别方法和来自纯细菌物种的数据，我们证明这种方法可用于量化混合物的组成，以及在无需获取额外参考数据的情况下以约80%的准确率预测其成分。所描述的方法显著扩展了传感器阵列的功能，并为分析其他复杂样本的数据处理提供了重要见解。
