Machine-Learning-Assisted Rational Design of SiâRhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation.
Adv Mater
; 36(31): e2404828, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38781580
ABSTRACT
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 Siârhodamine derivatives are successfully achieved and the cathepsin/pH sequentially activated probe Siârhodamineâcathepsin-pH (SiRâCTS-pH) is constructed. The results reveal that SiRâCTS-pH exhibits higher signal-to-noise ratio of fluorescence imaging, compared to single pH or cathepsin-activated probe. Moreover, SiRâCTS-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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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Rodaminas
/
Catepsinas
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Corantes Fluorescentes
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article