2020
DOI: 10.1155/2020/9186583
|View full text |Cite
|
Sign up to set email alerts
|

Aptamer-Conjugated Multifunctional Polymeric Nanoparticles as Cancer-Targeted, MRI-Ultrasensitive Drug Delivery Systems for Treatment of Castration-Resistant Prostate Cancer

Abstract: Nanoscopic therapeutic systems that incorporate therapeutic agents, molecular targeting, and imaging capabilities have gained momentum and exhibited significant therapeutic potential. In this study, multifunctional polymeric nanoparticles with controlled drug delivery, cancer-targeted capability, and efficient magnetic resonance imaging (MRI) contrast characteristics were formulated and applied in the treatment of castration-resistant prostate cancer (CRPC). The “core-shell” targeted nanoparticles (NPs) were s… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
27
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 36 publications
(28 citation statements)
references
References 22 publications
1
27
0
Order By: Relevance
“…The results of this study showed that compared with the traditional CNN MRI image segmentation effect, the upgraded 3D CNN-based MRI image segmentation effect was better, with significant improvement in the accuracy, sensitivity, and specificity indicators, and the differences were all statistically marked ( p < 0.05). This was similar to the research results of Fang et al [ 19 ], indicating that the image segmentation algorithm based on CNN could improve the quality of MRI images very well. In the monitoring of the targeted drug therapy of doxorubicin nanopreparations for liver cancer patients, it was found that the MRI images of liver cancer patients processed by a 3D CNN-based MRI image segmentation algorithm could be more intuitively observed and guided to accurately arrive at the target of liver cancer to accurately kill liver cancer cells and optimize the targeted therapy process of liver cancer.…”
Section: Discussionsupporting
confidence: 91%
“…The results of this study showed that compared with the traditional CNN MRI image segmentation effect, the upgraded 3D CNN-based MRI image segmentation effect was better, with significant improvement in the accuracy, sensitivity, and specificity indicators, and the differences were all statistically marked ( p < 0.05). This was similar to the research results of Fang et al [ 19 ], indicating that the image segmentation algorithm based on CNN could improve the quality of MRI images very well. In the monitoring of the targeted drug therapy of doxorubicin nanopreparations for liver cancer patients, it was found that the MRI images of liver cancer patients processed by a 3D CNN-based MRI image segmentation algorithm could be more intuitively observed and guided to accurately arrive at the target of liver cancer to accurately kill liver cancer cells and optimize the targeted therapy process of liver cancer.…”
Section: Discussionsupporting
confidence: 91%
“…Xenograft model for in vivo analysis also showed inhibitory effect against tumor with normal level of white blood cell (WBC) count. So this nanoformulation showed enhanced efficacy of CRPC theranostic and low toxicity to the circulation both in vivo and in vitro that make it promising drug delivery system for the efficient treatment of CRPC [120].…”
Section: Othersmentioning
confidence: 91%
“…However, DTX became resistant to cancer therapeutics, as mentioned earlier [108]. Both of these limitations can be removed by a nanoscopic therapeutic system through the formulation of Wy5a-DTX-SPION [120]. In this nanoformulation, SPION were encapsulated by DTX and Wy5a aptamer directed the transport of nanoparticles to the targeted cancer cells.…”
Section: Othersmentioning
confidence: 99%
“…It is evident that multifunctional formulations like this one are a promising class of targeted drug and contrast delivery systems. 27 …”
Section: Targeted Contrast For Molecular Imagingmentioning
confidence: 99%