Investigation of optimizing indocyanine green solution for in vivo lymphatic research using near-infrared fluorescence indocyanine green lymphangiography

Scientific Reports(2023)

引用 1|浏览1
暂无评分
摘要
Despite the tireless efforts of many researchers in lymphatic research, indocyanine green (ICG) solution conditions suitable for lymphatic circulation tests have not been perfectly established yet. We aimed to investigate the optimal in vivo conditions of ICG solution to avoid photobleaching and quenching effects, which may affect the accuracy of lymphatic circulation evaluation. After ICG fluorescence intensity (or ICG intensity) was assessed under different in vitro conditions, the image quality of brachial lymph nodes (LNs) and collecting lymphatic vessels (LVs) in eight rats was investigated. The in vitro results showed that ICG intensity depends on concentration and time in various solvents; however, the brightest intensity was observed at a concentration of 8–30 μg/mL in all solvents. ICG concentration in the albumin (bovine serum albumin; BSA) solution and rat’s plasma showed more than two times higher fluorescence intensity than in distilled water (DW) in the same range. However, saline reduced the intensity by almost half compared to DW. In the in vivo experiment, we obtained relatively high-quality images of the LNs and LVs using ICG in the BSA solution. Even at low concentrations, the result in the BSA solution was comparable to those obtained from high-concentration solutions commonly used in conventional circulation tests. This study provides valuable information about the conditions for optimal ICG intensity in near infrared fluorescence indocyanine green (NIRF-ICG) lymphangiography, which may be useful not only for the diagnosis of lymphatic circulation diseases such as lymphedema but also for preclinical research for the lymphatic system.
更多
查看译文
关键词
Circulation,Fluorescence imaging,Imaging and sensing,Medical imaging,Preclinical research,Translational research,Science,Humanities and Social Sciences,multidisciplinary
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要