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使用阵列传感系统进行交叉反应性筛选的多维映射方法。

Multidimensional mapping method using an arrayed sensing system for cross-reactivity screening.

作者信息

Chocron Sheryl E, Weisberger Bryce M, Ben-Yoav Hadar, Winkler Thomas E, Kim Eunkyoung, Kelly Deanna L, Payne Gregory F, Ghodssi Reza

机构信息

MEMS Sensors and Actuators Laboratory (MSAL), University of Maryland, College Park, Maryland, United States of America; Fischell Department of Bioengineering, University of Maryland, College Park, Maryland, United States of America.

MEMS Sensors and Actuators Laboratory (MSAL), University of Maryland, College Park, Maryland, United States of America; Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America.

出版信息

PLoS One. 2015 Mar 19;10(3):e0116310. doi: 10.1371/journal.pone.0116310. eCollection 2015.

Abstract

When measuring chemical information in biological fluids, challenges of cross-reactivity arise, especially in sensing applications where no biological recognition elements exist. An understanding of the cross-reactions involved in these complex matrices is necessary to guide the design of appropriate sensing systems. This work presents a methodology for investigating cross-reactions in complex fluids. First, a systematic screening of matrix components is demonstrated in buffer-based solutions. Second, to account for the effect of the simultaneous presence of these species in complex samples, the responses of buffer-based simulated mixtures of these species were characterized using an arrayed sensing system. We demonstrate that the sensor array, consisting of electrochemical sensors with varying input parameters, generated differential responses that provide synergistic information of sample. By mapping the sensing array response onto multidimensional heat maps, characteristic signatures were compared across sensors in the array and across different matrices. Lastly, the arrayed sensing system was applied to complex biological samples to discern and match characteristic signatures between the simulated mixtures and the complex sample responses. As an example, this methodology was applied to screen interfering species relevant to the application of schizophrenia management. Specifically, blood serum measurement of antipsychotic clozapine and antioxidant species can provide useful information regarding therapeutic efficacy and psychiatric symptoms. This work proposes an investigational tool that can guide multi-analyte sensor design, chemometric modeling and biomarker discovery.

摘要

在测量生物流体中的化学信息时,会出现交叉反应的挑战,尤其是在不存在生物识别元件的传感应用中。了解这些复杂基质中涉及的交叉反应对于指导合适传感系统的设计至关重要。这项工作提出了一种研究复杂流体中交叉反应的方法。首先,在基于缓冲液的溶液中展示了对基质成分的系统筛选。其次,为了考虑这些物质在复杂样品中同时存在的影响,使用阵列传感系统对这些物质基于缓冲液的模拟混合物的响应进行了表征。我们证明,由具有不同输入参数的电化学传感器组成的传感器阵列产生了差异响应,这些响应提供了样品的协同信息。通过将传感阵列响应映射到多维热图上,比较了阵列中不同传感器以及不同基质之间的特征信号。最后,将阵列传感系统应用于复杂生物样品,以辨别并匹配模拟混合物与复杂样品响应之间的特征信号。例如,该方法被应用于筛选与精神分裂症管理应用相关的干扰物质。具体而言,抗精神病药物氯氮平和抗氧化物质的血清测量可以提供有关治疗效果和精神症状的有用信息。这项工作提出了一种研究工具,可指导多分析物传感器设计、化学计量学建模和生物标志物发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b452/4366158/efe6d31ce57f/pone.0116310.g001.jpg

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