用于光学测量泪液中葡萄糖的含铬氧化铈纳米粒子吸盘式条形传感器。

IF 8.1 Q1 ENGINEERING, BIOMEDICAL Biomaterials research Pub Date : 2023-12-18 DOI:10.1186/s40824-023-00469-5
Sijin Park, Dong Yeon Nam, Hee-Jae Jeon, Jae Hoon Han, Dawon Jang, Juil Hwang, Yeong-Seo Park, Young-Geun Han, Young Bin Choy, Dong Yun Lee
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引用次数: 0

摘要

背景:无创监测泪液葡萄糖水平可方便患者控制糖尿病。然而,监测泪液葡萄糖水平存在一些问题,如某些方法的侵入性、微型化、不准确性或可穿戴设备的高成本。为了克服这些问题,我们新设计了一种吸盘式(SD)条状生物传感器,它能快速吸入泪液,并含有氧化铈纳米粒子(CNP),能根据泪液中的葡萄糖含量产生独特的颜色变化,而无需复杂的电子元件:方法:设计了由三个不同部分(尖端、通道和反应室)组成的 SD 条形生物传感器,其中包含可收集和输送泪液的传感纸。对经 CNP/APTS(氨基丙基三乙氧基硅烷)/GOx(葡萄糖氧化酶)处理的传感纸进行了表征。然后,我们在糖尿病兔动物身上测试了 SD 条形生物传感器的可靠性。我们通过比色分析算法对 SD 条形生物传感器的颜色值进行了定量分析:结果:我们使用 SD 条形生物传感器接触糖尿病兔眼睛的下睑结膜(IPC),在不刺激眼睛的情况下收集泪液,并成功验证了传感器的性能和定量功效。为准确测量 SD 条形生物传感器的颜色变化,开发了一种可优化测量精度的图像处理算法。验证测试表明,泪液和血液中测量到的葡萄糖浓度具有良好的相关性:我们的研究结果表明,嵌入 CNP 的 SD 条形生物传感器和相关的图像处理可以简单地监测泪液葡萄糖,从而控制糖尿病。
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Chromophoric cerium oxide nanoparticle-loaded sucking disk-type strip sensor for optical measurement of glucose in tear fluid.

Background: Noninvasive monitoring of tear glucose levels can be convenient for patients to manage their diabetes mellitus. However, there are issues with monitoring tear glucose levels, such as the invasiveness of some methods, the miniaturization, inaccuracy, or the high cost of wearable devices. To overcome the issues, we newly designed a sucking disk-type (SD) strip biosensor that can quickly suck tear fluid and contains cerium oxide nanoparticle (CNP) that causes a unique color change according to the glucose level of the tear without complicated electronic components.

Methods: The SD strip biosensor composed of three distinct parts (tip, channel, and reaction chamber) was designed to contain the sensing paper, onto which tear fluid can be collected and delivered. The sensing paper treated with CNP/APTS (aminopropyltriethoxysilane) /GOx (glucose oxidase) was characterized. Then we carried out the reliability of the SD strip biosensor in the diabetic rabbit animals. We quantitatively analyzed the color values of the SD strip biosensor through the colorimetric analysis algorithm.

Results: We contacted the inferior palpebral conjunctiva (IPC) of a diabetic rabbit eye using an SD strip biosensor to collect tears without eye irritation and successfully verified the performance and quantitative efficacy of the sensor. An image processing algorithm that can optimize measurement accuracy is developed for accurate color change measurement of SD strip biosensors. The validation tests show a good correlation between glucose concentrations measured in the tear and blood.

Conclusion: Our findings demonstrate that the CNP-embedded SD strip biosensor and the associated image processing can simply monitor tear glucose to manage diabetes mellitus.

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