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使用涂碳的 3D 打印微针阵列微创检测丁丙诺啡。

Minimally invasive detection of buprenorphine using a carbon-coated 3D-printed microneedle array.

机构信息

Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina, Raleigh, NC, 27695, USA.

Division of Research & Development, Lovely Professional University, Phagwara, 144411, Punjab, India.

出版信息

Mikrochim Acta. 2024 Oct 15;191(11):672. doi: 10.1007/s00604-024-06754-x.

Abstract

A machine learning-assisted 3D-printed conducting microneedle-based electrochemical sensing platform was developed for wireless, efficient, economical, and selective determination of buprenorphine. The developed microneedle array-based sensing platform used 3D printing and air spray coating technologies for rapid and scalable manufacturing of a conducting microneedle surface. Upon optimization and understanding of the electrode stability, redox behavior, and electrochemical characteristics of as-prepared conducting microneedle array, the developed electrochemical platform was investigated for monitoring different levels of buprenorphine in the artificial intestinal fluid and found to be highly sensitive and selective towards buprenorphine for a wide detection range from 2 to 140 μM, with a low limit of detection of 0.129 μM. Furthermore, to make the sensing platform user accessible, the experimentally recorded sensing data was used to train a machine learning model and develop a web application for the numerical demonstration of buprenorphine levels at the point of site. Finally, the proof-of-concept study demonstrated that by advancing our prevailing 3D printing and additive manufacturing techniques, a low-cost, user-accessible, and compelling wearable electrochemical sensor could be manufactured for minimally invasive determination of buprenorphine in interstitial fluid.

摘要

一种基于机器学习辅助的 3D 打印导电微针的电化学传感平台被开发出来,用于无线、高效、经济和选择性地测定丁丙诺啡。所开发的基于微针阵列的传感平台使用 3D 打印和空气喷涂涂层技术,用于快速和可扩展地制造导电微针表面。在对制备的导电微针阵列的电极稳定性、氧化还原行为和电化学特性进行优化和理解后,研究了所开发的电化学平台对人工肠液中不同水平的丁丙诺啡的监测,发现该平台对丁丙诺啡具有高度的敏感性和选择性,检测范围从 2 到 140 μM 很宽,检测限低至 0.129 μM。此外,为了使传感平台易于用户使用,实验记录的传感数据被用于训练机器学习模型,并开发了一个网络应用程序,用于在现场演示丁丙诺啡水平的数值。最后,概念验证研究表明,通过推进我们现有的 3D 打印和增材制造技术,可以制造出低成本、用户可访问的、引人注目的可穿戴电化学传感器,用于微创测定间质液中的丁丙诺啡。

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