Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea.
Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea.
Adv Healthc Mater. 2023 Oct;12(26):e2300845. doi: 10.1002/adhm.202300845. Epub 2023 Aug 4.
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.
糖尿病及其并发症影响年轻人群，死亡率高；但是，早期诊断有助于选择适当的治疗方案，从而降低死亡率。虽然通过呼气进行糖尿病诊断具有早期诊断的巨大潜力，但此类诊断的研究仅限于疾病检测，需要深入检查来诊断和分类疾病及其并发症，如心脏病。本研究展示了基于人工神经网络处理的生物电子鼻用于准确诊断糖尿病以及分类 1 型和 2 型糖尿病及其并发症（如心脏病）的应用。具体而言，使用基于 M13 噬菌体的电子鼻（e-nose）来探索处于不同细胞和机体组织水平（细胞、肝类器官和小鼠）的糖尿病患者的特征。在培养过程中收集呼气样本，并将其暴露于基于噬菌体的 e-nose。与细胞相比，在模拟糖尿病环境下培养的肝类器官显示出与糖尿病小鼠特征非常相似的特性。通过神经模式分离，基于 M13 噬菌体的 e-nose 对小鼠的四种情况（1 型糖尿病、2 型糖尿病、糖尿病性心肌病和心肌病）的分类成功率超过 86%。
