Abstract
The accurate preoperative diagnosis and tracking of lung adenocarcinoma is hindered by non-targeting and diffusion of dyes used for marking tumors. Hence, there is an urgent need to develop a practical nanoprobe for tracing lung adenocarcinoma precisely even treating them noninvasively. Herein, Gold nanoclusters (AuNCs) conjugate with thyroid transcription factor-1 (TTF-1) antibody, then multifunctional nanoprobe Au-TTF-1 is designed and synthesized, which underscores the paramount importance of advancing the machine learning diagnosis and bioimaging-guided treatment of lung adenocarcinoma. Bright fluorescence (FL) and strong CT signal of Au-TTF-1 set the stage for tracking. Furthermore, the high specificity of TTF-1 antibody facilitates selective targeting of lung adenocarcinoma cells as compared to common lung epithelial cells, so machine learning software Lung adenocarcinoma auxiliary detection system was designed, which combined with Au-TTF-1 to assist the intelligent recognition of lung adenocarcinoma jointly. Besides, Au-TTF-1 not only contributes to intuitive and targeted visualization, but also guides the following noninvasive photothermal treatment. The boundaries of tumor are light up by Au-TTF-1 for navigation, it penetrates into tumor and implements noninvasive photothermal treatment, resulting in ablating tumors in vivo locally. Above all, Au-TTF-1 serves as a key platform for target bio-imaging navigation, machine learning diagnosis and synergistic PTT as a single nanoprobe, which demonstrates attractive performance on lung adenocarcinoma.
| Original language | English |
|---|---|
| Article number | 22 |
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Journal of Nanobiotechnology |
| Volume | 22 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Dec 2024 |
Bibliographical note
©The Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Bioimaging-guided therapy
- Dual-mode bioimaging
- Lung adenocarcinoma
- Machine learning
- Nanoprobe
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