한빛사논문
- 조회677
Woong Bi Jang 1,2, Dongwon Yi 3, Thanh Mien Nguyen 4, Yujin Lee 5, Eun Ji Lee 1,2, Jaewoo Choi 1,2, You Hwan Kim 5, Eun-Jung Choi 5, Jin-Woo Oh 4,5,6, Sang-Mo Kwon 1,2
1Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
2Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea.
3Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, 50612, Republic of Korea.
4Bio-IT Fusion Technology Research Institute, Pusan National University, Busan, 46241, Republic of Korea.
5Department of Nano Fusion Technology, Pusan National University, Busan, 46214, Republic of Korea.
6Korea Nanobiotechnology Center, Pusan National University, Busan, 46241, Republic of Korea.
CORRESPONDING AUTHORS : Jin-Woo Oh, Sang-Mo Kwon
Abstract
Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.
논문정보
- Research article
- 2023년 08월 (BRIC 등록일 2023-08-07)
- 국내 연구진
- 의약학 > 응용의학
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