Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens
Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color change...
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| Published in | Nano letters Vol. 21; no. 20; pp. 8933 - 8940 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
American Chemical Society
27.10.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1530-6984 1530-6992 1530-6992 |
| DOI | 10.1021/acs.nanolett.1c01880 |
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| Abstract | Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients. |
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| AbstractList | Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients. Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients.Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients. |
| Author | Kim, Sooyeon Jeong, In-Kyung Kang, Jaheon Kim, Young Ro Chung, Euiheon Park, Sijin Jeon, Hee-Jae Lee, Dong Yun |
| AuthorAffiliation | Department of Biomedical Science and Engineering Athinoula A. Martinos Center for Biomedical Imaging GIST Weldon School of Biomedical Engineering Department of Physics and Photon Science, GIST Department of Bioengineering, College of Engineering, and BK FOUR Biopharmaceutical Innovation Leader for Education and Research Group, and Institute of Nano Science and Technology (INST) Department of Endocrinology and Metabolism, Kyung Hee University Hospital at Gangdong Harvard Medical School Department of Radiology AI Graduate School Elixir Pharmatech Inc |
| AuthorAffiliation_xml | – name: Department of Bioengineering, College of Engineering, and BK FOUR Biopharmaceutical Innovation Leader for Education and Research Group, and Institute of Nano Science and Technology (INST) – name: Department of Radiology – name: Athinoula A. Martinos Center for Biomedical Imaging – name: Harvard Medical School – name: Department of Endocrinology and Metabolism, Kyung Hee University Hospital at Gangdong – name: Department of Physics and Photon Science, GIST – name: Elixir Pharmatech Inc – name: Department of Biomedical Science and Engineering – name: AI Graduate School – name: Weldon School of Biomedical Engineering – name: GIST |
| Author_xml | – sequence: 1 givenname: Hee-Jae surname: Jeon fullname: Jeon, Hee-Jae organization: Weldon School of Biomedical Engineering – sequence: 2 givenname: Sooyeon surname: Kim fullname: Kim, Sooyeon organization: Department of Biomedical Science and Engineering – sequence: 3 givenname: Sijin surname: Park fullname: Park, Sijin organization: Elixir Pharmatech Inc – sequence: 4 givenname: In-Kyung surname: Jeong fullname: Jeong, In-Kyung organization: Department of Endocrinology and Metabolism, Kyung Hee University Hospital at Gangdong – sequence: 5 givenname: Jaheon surname: Kang fullname: Kang, Jaheon organization: Department of Endocrinology and Metabolism, Kyung Hee University Hospital at Gangdong – sequence: 6 givenname: Young Ro surname: Kim fullname: Kim, Young Ro organization: Athinoula A. Martinos Center for Biomedical Imaging – sequence: 7 givenname: Dong Yun orcidid: 0000-0001-7691-0447 surname: Lee fullname: Lee, Dong Yun email: dongyunlee@hanyang.ac.kr organization: Elixir Pharmatech Inc – sequence: 8 givenname: Euiheon orcidid: 0000-0002-3326-6927 surname: Chung fullname: Chung, Euiheon email: ogong50@gist.ac.kr organization: Department of Physics and Photon Science, GIST |
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| Keywords | image processing algorithms tear glucose nanoparticle embedded contact lens (NECLs) optical monitoring system (OMS) |
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