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具备学习能力的传感器：从味觉指纹到早期疾病检测模式的演变

Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.

作者信息

Christodoulides Nicolaos, McRae Michael P, Simmons Glennon W, Modak Sayli S, McDevitt John T

机构信息

Department of Biomaterials, College of Dentistry, Bioengineering Institute, New York University, New York, NY 10010, USA.

出版信息

Micromachines (Basel). 2019 Apr 16;10(4):251. doi: 10.3390/mi10040251.

DOI:10.3390/mi10040251
PMID:30995728
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6523560/
Abstract

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.

摘要

麦克德维特团队一直在持续努力开发一种可编程传感平台，该平台具备先进的多重/多类别化学/生物检测能力。这个基于芯片的可扩展平台已经过优化，以服务于实际生物样本，并在分析性能方面得到了验证。该平台被设计成一种能学习的传感器，可以为手头的应用承载新内容。从复杂混合物中识别基于生物标志物的指纹与电子鼻和电子舌研究直接相关。最近，我们已经进入大数据采集阶段，并将其与机器学习和人工智能联系起来。在这里，多参数传感提供了令人兴奋的机会，它模仿味觉，克服了咸、甜、酸、苦和谷氨酸传感的局限性，并朝着健康与 wellness 的指纹识别迈进。本文总结了与电子味觉芯片系统相关的发展，该系统正演变成一个将生物学数字化并提供临床决策支持工具的平台。还强调了为这些活动的形成做出贡献的一系列动态文献和关键综述文章。这种完全集成的传感器有望使生物标志物检测小组更快地广泛应用于临床实践，为健康诊断带来有价值的新见解，有利于早期疾病检测。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/a72daf836d3b/micromachines-10-00251-g018.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/5c4cfd06af8d/micromachines-10-00251-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/dd1a549d9b56/micromachines-10-00251-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/aead988f1b31/micromachines-10-00251-g022.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/35f92f111073/micromachines-10-00251-g024.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e78/6523560/09c50dec1560/micromachines-10-00251-g026.jpg

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