Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation
© 2024 Wiley‐VCH GmbH.
Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 31 vom: 01. Aug., Seite e2404828 |
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1. Verfasser: | |
Weitere Verfasser: | , , , , , , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2024
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Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
Schlagworte: | Journal Article fluorescence navigation fluorescent probe machine learning signal‐to‐background ratio xanthene dyes Rhodamines Fluorescent Dyes Cathepsins EC 3.4.- mehr... |
Zusammenfassung: | © 2024 Wiley‐VCH GmbH. 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 dyes is first proposed, and a quantitative prediction model to guide the rational synthesis of novel fluorescent molecules with the desired pH responsivity is constructed. Two novel Si─rhodamine derivatives are successfully achieved and the cathepsin/pH sequentially activated probe Si─rhodamine─cathepsin-pH (SiR─CTS-pH) is constructed. The results reveal that SiR─CTS-pH exhibits higher signal-to-noise ratio of fluorescence imaging, compared to single pH or cathepsin-activated probe. Moreover, SiR─CTS-pH shows strong differentiation abilities for tumor cells and tissues and accurately discriminates the complex hepatocellular carcinoma tissues from normal ones, indicating its significant application potential in clinical practice. Therefore, the continuous development of xanthene dyes and the rational design of superior fluorescent molecules through machine-learning-assisted model broaden the path and provide more advanced methods to researchers |
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Beschreibung: | Date Completed 01.08.2024 Date Revised 01.08.2024 published: Print-Electronic Citation Status MEDLINE |
ISSN: | 1521-4095 |
DOI: | 10.1002/adma.202404828 |