生物医学工程
材料科学
体内
持续监测
连续血糖监测
生物相容性材料
跟踪(教育)
生物传感器
远程病人监护
糖尿病
纳米技术
医学
1型糖尿病
工程类
生物技术
内分泌学
放射科
生物
运营管理
教育学
心理学
作者
Jagannath Malik,Seongmun Kim,Jong Mo Seo,Young Min Cho,Franklin Bien
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:70 (3): 1000-1011
被引量:4
标识
DOI:10.1109/tbme.2022.3207240
摘要
Continuous glucose monitoring system (CGMS) is growing popular and preferred by diabetes over conventional methods of self-blood glucose monitoring (SBGM) systems. However, currently available commercial CGMS in the market is useful for few days to few months. This paper presents a durable, highly sensitive and minimally invasive implant type electromagnetic sensor for continuous glucose monitoring that is capable of tracking minute changes in blood glucose level (BGL).The proposed sensor utilizes strong oscillating nearfield to detect minute changes in dielectric permittivity of interstitial fluid (ISF) and blood due to changes in BGL. A biocompatible packaging material is used to cover the sensor. It helps in minimizing foreign body reactions (FBR) and improves stability of the sensor.The performance of the proposed sensor was evaluated on live rodent models (C57BL/6J mouse and Sprague Dawley rat) through intravenous glucose and insulin tolerance tests. Biocompatible polyolefin was used as the sensor packaging material, and the effect of packaging thickness on the sensitivity of sensor was examined in in-vivo test. Proposed sensor could track real-time BGL change measured with a commercial blood glucose meter. High linear correlation (R2 > 0.9) with measured BGL was observed during in vivo experiments.The experimental results demonstrate that the proposed sensor is suitable for long term CGMS applications with a high accuracy.Present work offers a new perspective towards development of long term CGM system using electromagnetic based implant sensor. The in vivo evaluation of the sensor shows excellent tracking of BGL changes.
科研通智能强力驱动
Strongly Powered by AbleSci AI