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      Colorimetric cerium oxide nanoparticles for monitoring glucose levels in tears

      한글로보기

      https://www.riss.kr/link?id=T15624344

      • 저자
      • 발행사항

        서울 : 한양대학교 대학원, 2020

      • 학위논문사항

        학위논문(박사) -- 한양대학교 대학원 , 생명공학과 , 2020. 8

      • 발행연도

        2020

      • 작성언어

        영어

      • 주제어
      • 발행국(도시)

        서울

      • 형태사항

        ix, 91 p. : 삽도 ; 26 cm.

      • 일반주기명

        권두 Abstract, 권말 국문요지 수록
        지도교수: 이동윤
        참고문헌: p. 85-88

      • UCI식별코드

        I804:11062-000000113693

      • 소장기관
        • 국립중앙도서관 국립중앙도서관 우편복사 서비스
        • 한양대학교 안산캠퍼스 소장기관정보
        • 한양대학교 중앙도서관 소장기관정보
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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      Diabetes is a serious chronic disease and its incidence continues to rise. The growing incidence of diabetes is related to dietary changes and rising life expectancy. Diabetes can cause to complications such as stroke, nephropathy and neuropathy. To prevent and minimize the severe complications, patients need to check blood glucose levels. Self-monitoring of blood glucose (SMBG) allows patients to manage their blood glucose level and to determine when insulin should be injected. Diabetic patients should check blood glucose levels several times a day. The most comm way to do SMBG is the finger pricking. Therefore, it has the disadvantage of causing pain and the possibility of blood-borne infections. For these reasons, diabetic patients are reluctant to measure blood glucose levels with the finger prick test.
      To overcome shortcomings of the invasive finger prick methods, noninvasive methods have been studied using various techniques such as ultrasound, reverse iontophoresis, bioimpedance spectroscopy and laser microporation. These methods analyze glucose levels in saliva, urine, sweat, tear etc. However, there are other difficulties such as skin irritation, calibration challenges and inaccuracy. Noninvasive methods using sweat or saliva have large in error and difficult to accurately measure glucose levels. Urine also has some drawbacks, such as potential toxicity and the possibility of interference with other substances, making it difficult to measure glucose in urine.
      To overcome these limitations, I measured the glucose concentration in tears. This is based on previous studies on the relationship between glucose levels in tears and blood glucose. To measure tear glucose level, tears must be collected. However, the method of capturing tears in previous studies can cause pain and discomfort in the patient’s eyes, so experienced medical staff should collect tears with glass capillaries in the clinical environment. Therefore, herein, I presented two patient-friendly methods to collect tears and strategy for analyzing glucose in the collected tears as following:
      (1) Contact lens-type sensor for monitoring tear glucose level.
      (2) Strip-type sensor for monitoring tear glucose level.
      In these studies, I developed nanoparticles that change color in response to glucose. Strip-type biosensors usually use color reaction-based platforms, but, contact lens-type sensors measure the fluoresce from fluorescence resonance energy transfer (FRET) or the electrical signal through cyclic voltammetry. One disadvantage of these methods is the need for equipment for measurement. Especially fluorescence-based methods can cause eye damage from light. In addition, there is a risk of eye damage due to heat generated from circuits and antennas in case of ‘smart contact lens’ using a potentiometric or amperometric technology.
      In this research, therefore, I used cerium oxide nanoparticle (CNP) to develop a colorimetric glucose sensor because of their special colorimetric properties in a redox state. CNPs exist in both states Ce3+ and Ce4+. Ce3+ is colorless state, but when it reacts with hydrogen peroxide, it is oxidized to a yellow color Ce4+. To detect glucose, CNP and glucose oxidase (GOX) were linked together by PEG. The GOX of CNP-PEG-GOX oxidized glucose hydrogen peroxide (H2O2) and gluconolactone. The resulting H2O2 changed the color of the CNP to yellow.
      The algorithm-based color analysis was used to measure the colorimetric response of CNP-PEG-GOX. The self-diagnosis platforms were developed by constructing optimal algorithms to minimized noise based on the design of the tear collecting methods.
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      Diabetes is a serious chronic disease and its incidence continues to rise. The growing incidence of diabetes is related to dietary changes and rising life expectancy. Diabetes can cause to complications such as stroke, nephropathy and neuropathy. To p...

      Diabetes is a serious chronic disease and its incidence continues to rise. The growing incidence of diabetes is related to dietary changes and rising life expectancy. Diabetes can cause to complications such as stroke, nephropathy and neuropathy. To prevent and minimize the severe complications, patients need to check blood glucose levels. Self-monitoring of blood glucose (SMBG) allows patients to manage their blood glucose level and to determine when insulin should be injected. Diabetic patients should check blood glucose levels several times a day. The most comm way to do SMBG is the finger pricking. Therefore, it has the disadvantage of causing pain and the possibility of blood-borne infections. For these reasons, diabetic patients are reluctant to measure blood glucose levels with the finger prick test.
      To overcome shortcomings of the invasive finger prick methods, noninvasive methods have been studied using various techniques such as ultrasound, reverse iontophoresis, bioimpedance spectroscopy and laser microporation. These methods analyze glucose levels in saliva, urine, sweat, tear etc. However, there are other difficulties such as skin irritation, calibration challenges and inaccuracy. Noninvasive methods using sweat or saliva have large in error and difficult to accurately measure glucose levels. Urine also has some drawbacks, such as potential toxicity and the possibility of interference with other substances, making it difficult to measure glucose in urine.
      To overcome these limitations, I measured the glucose concentration in tears. This is based on previous studies on the relationship between glucose levels in tears and blood glucose. To measure tear glucose level, tears must be collected. However, the method of capturing tears in previous studies can cause pain and discomfort in the patient’s eyes, so experienced medical staff should collect tears with glass capillaries in the clinical environment. Therefore, herein, I presented two patient-friendly methods to collect tears and strategy for analyzing glucose in the collected tears as following:
      (1) Contact lens-type sensor for monitoring tear glucose level.
      (2) Strip-type sensor for monitoring tear glucose level.
      In these studies, I developed nanoparticles that change color in response to glucose. Strip-type biosensors usually use color reaction-based platforms, but, contact lens-type sensors measure the fluoresce from fluorescence resonance energy transfer (FRET) or the electrical signal through cyclic voltammetry. One disadvantage of these methods is the need for equipment for measurement. Especially fluorescence-based methods can cause eye damage from light. In addition, there is a risk of eye damage due to heat generated from circuits and antennas in case of ‘smart contact lens’ using a potentiometric or amperometric technology.
      In this research, therefore, I used cerium oxide nanoparticle (CNP) to develop a colorimetric glucose sensor because of their special colorimetric properties in a redox state. CNPs exist in both states Ce3+ and Ce4+. Ce3+ is colorless state, but when it reacts with hydrogen peroxide, it is oxidized to a yellow color Ce4+. To detect glucose, CNP and glucose oxidase (GOX) were linked together by PEG. The GOX of CNP-PEG-GOX oxidized glucose hydrogen peroxide (H2O2) and gluconolactone. The resulting H2O2 changed the color of the CNP to yellow.
      The algorithm-based color analysis was used to measure the colorimetric response of CNP-PEG-GOX. The self-diagnosis platforms were developed by constructing optimal algorithms to minimized noise based on the design of the tear collecting methods.

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