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基于纳米材料的非侵入性糖尿病传感技术的发展趋势。

Trends in Nanomaterial-Based Non-Invasive Diabetes Sensing Technologies.

机构信息

Alpha Szenszor Inc., Carlisle, MA 01741, USA.

Department of Electrical Engineering, University of North Florida, Jacksonville, FL 32246, USA.

出版信息

Diagnostics (Basel). 2014 Apr 21;4(2):27-46. doi: 10.3390/diagnostics4020027.

Abstract

Blood glucose monitoring is considered the gold standard for diabetes diagnostics and self-monitoring. However, the underlying process is invasive and highly uncomfortable for patients. Furthermore, the process must be completed several times a day to successfully manage the disease, which greatly contributes to the massive need for non-invasive monitoring options. Human serums, such as saliva, sweat, breath, urine and tears, contain traces of glucose and are easily accessible. Therefore, they allow minimal to non-invasive glucose monitoring, making them attractive alternatives to blood measurements. Numerous developments regarding noninvasive glucose detection techniques have taken place over the years, but recently, they have gained recognition as viable alternatives, due to the advent of nanotechnology-based sensors. Such sensors are optimal for testing the amount of glucose in serums other than blood thanks to their enhanced sensitivity and selectivity ranges, in addition to their size and compatibility with electronic circuitry. These nanotechnology approaches are rapidly evolving, and new techniques are constantly emerging. Hence, this manuscript aims to review current and future nanomaterial-based technologies utilizing saliva, sweat, breath and tears as a diagnostic medium for diabetes monitoring.

摘要

血糖监测被认为是糖尿病诊断和自我监测的金标准。然而,其基础过程对患者具有侵入性,且极度不适。此外,为了成功控制疾病,该过程必须每天完成多次,这极大地促进了对非侵入性监测选择的巨大需求。人类血清(如唾液、汗液、呼吸、尿液和眼泪)中含有葡萄糖痕迹,且容易获取。因此,它们允许进行最小程度到非侵入性的葡萄糖监测,使其成为血液测量的有吸引力的替代方法。多年来,已经有许多关于非侵入性葡萄糖检测技术的发展,但最近,由于基于纳米技术的传感器的出现,它们已被认为是可行的替代方法。由于这些传感器具有增强的灵敏度和选择性范围,以及其尺寸和与电子电路的兼容性,因此非常适合测试血清(包括血液)中的葡萄糖含量。这些纳米技术方法正在迅速发展,新的技术也在不断涌现。因此,本文旨在综述当前和未来基于纳米材料的技术,这些技术利用唾液、汗液、呼吸和眼泪作为糖尿病监测的诊断介质。

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本文引用的文献

1
3
In vivo evaluation of a chip based near infrared sensor for continuous glucose monitoring.
Biosens Bioelectron. 2014 Mar 15;53:99-104. doi: 10.1016/j.bios.2013.09.043. Epub 2013 Sep 27.
4
Glucose detection in human sweat using an electronic nose.
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:1462-5. doi: 10.1109/EMBC.2013.6609787.
5
Is breath acetone a biomarker of diabetes? A historical review on breath acetone measurements.
J Breath Res. 2013 Sep;7(3):037109. doi: 10.1088/1752-7155/7/3/037109. Epub 2013 Aug 20.
7
Evaluation of enzyme-based tear glucose electrochemical sensors over a wide range of blood glucose concentrations.
Biosens Bioelectron. 2013 Nov 15;49:204-9. doi: 10.1016/j.bios.2013.05.014. Epub 2013 May 16.
8
Miniature biofuel cell as a potential power source for glucose-sensing contact lenses.
Anal Chem. 2013 Jul 2;85(13):6342-8. doi: 10.1021/ac4006793. Epub 2013 Jun 19.
9
Photoinduced charge transfer and acetone sensitivity of single-walled carbon nanotube-titanium dioxide hybrids.
J Am Chem Soc. 2013 Jun 19;135(24):9015-22. doi: 10.1021/ja402887v. Epub 2013 Jun 5.
10
Construction of near-infrared photonic crystal glucose-sensing materials for ratiometric sensing of glucose in tears.
Biosens Bioelectron. 2013 Oct 15;48:94-9. doi: 10.1016/j.bios.2013.03.082. Epub 2013 Apr 11.

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