Chromophoric cerium oxide nanoparticle-loaded sucking disk-type strip sensor for optical measurement of glucose in tear fluid

• Published in: Biomaterials research 2023, 27(00), , pp.3056-3068
• Main Authors: Park, Sijin, Nam, Dong Yeon, Jeon, Hee-Jae, Han, Jae Hoon, Jang, Dawon, Hwang, Juil, Park, Yeong-Seo, Han, Young-Geun, Choy, Young Bin, Lee, Dong Yun
• Format: Article
• Language: English
• Subjects: Algorithms; Biocompatibility; Biosensors; Cerium; Cerium oxides; Colorimetry; Conjunctiva; Dextrose; Diabetes; Diabetes mellitus; Diabetic neuropathy; Glucose; Glucose oxidase; Image processing; Invasiveness; Irritation; Measurement; Measurement techniques; Nanoparticles; Nitrates; Optics; Oxidases; Patient compliance; Sensors; Silicones; Spectrum analysis; Tears; 의공학
• ISSN: 1226-4601; 2055-7124; 2055-7124
• DOI: 10.1186/s40824-023-00469-5

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