Machine‐Learning‐Assisted Rational Design of Si─Rhodamine as Cathepsin‐pH‐Activated Probe for Accurate Fluorescence Navigation

• Published in: Advanced materials (Weinheim) Vol. 36; no. 31; pp. e2404828 - n/a
• Main Authors: Xiang, Fei‐Fan, Zhang, Hong, Wu, Yan‐Ling, Chen, Yu‐Jin, Liu, Yan‐Zhao, Chen, Shan‐Yong, Guo, Yan‐Zhi, Yu, Xiao‐Qi, Li, Kun
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.08.2024
• Subjects: Carcinoma, Hepatocellular - diagnostic imaging; Cathepsins - metabolism; Chemical synthesis; Dyes; fluorescence navigation; Fluorescent Dyes - chemistry; Fluorescent indicators; fluorescent probe; Humans; Hydrogen-Ion Concentration; Liver Neoplasms - diagnostic imaging; Machine Learning; Optical Imaging - methods; Prediction models; Rhodamine; Rhodamines - chemistry; signal‐to‐background ratio; Silicon - chemistry; xanthene dyes; fluorescent probe; xanthene dyes; fluorescence navigation; signal‐to‐background ratio; machine learning

High‐performance fluorescent probes stand as indispensable tools in fluorescence‐guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, machine‐learning‐assisted strategy to investigate the current available xanthene dy...