Application and mechanism of manganese-coated caramelization nanospheres for active targeting in hepatobiliary tumors

Advancements in manganese complex-based MRI agents: Innovations, design strategies, and future directions

This review focuses on the advancements in manganese (Mn) complex-based magnetic resonance imaging (MRI) agents for imaging different diseases. Here we emphasize the unique redox properties of Mn to deliver innovative MRI contrast agents, including small molecules, nanoparticles (NPs), metal-organic frameworks (MOFs), and polymer hybrids. Aspects of their rational design have been discussed, including size dependence, morphology tuning, surface property enhancement, etc., while also discussing the existing challenges and potential solutions. The present work will inspire and motivate scientists to emphasize MRI-guided applications and bring clinical success in the coming years.

A Multifunctional, Highly Biocompatible, and Double-Triggering Caramelized Nanotheranostic System Loaded with Fe3O4 and DOX for Combined Chemo-Photothermal Therapy and Real-Time Magnetic Resonance Imaging Monitoring of Triple Negative Breast Cancer

Purpose

Owing to lack of specific molecular targets, the current clinical therapeutic strategy for triple negative breast cancer (TNBC) is still limited. In recent years, some nanosystems for malignancy treatment have received considerable attention. In this study, we prepared caramelized nanospheres (CNSs) loaded with doxorubicin (DOX) and Fe₃O₄ to achieve the synergistic effect of combined therapy and real-time magnetic resonance imaging (MRI) monitoring, so as to improve the diagnosis and therapeutic effect of TNBC.

Methods

CNSs with biocompatibility and unique optical properties were prepared by hydrothermal method, DOX and Fe₃O₄ were loaded on it to obtain Fe₃O₄/DOX@CNSs nanosystem. Characteristics including morphology, hydrodynamic size, zeta potentials and magnetic properties of Fe₃O₄/DOX@CNSs were evaluated. The DOX release was evaluated by different pH/near-infrared (NIR) light energy. Biosafety, pharmacokinetics, MRI and therapeutic treatment of Fe₃O₄@CNSs, DOX and Fe₃O₄/DOX@CNSs were examined in vitro or in vivo.

Results

Fe₃O₄/DOX@CNSs has an average particle size of 160 nm and a zeta potential of 27.5mV, it demonstrated that Fe₃O₄/DOX@CNSs is a stable and homogeneous dispersed system. The hemolysis experiment of Fe₃O₄/DOX@CNSs proved that it can be used in vivo. Fe₃O₄/DOX@CNSs displayed high photothermal conversion efficiency, extensive pH/heat-induced DOX release. 70.3% DOX release is observed under the 808 nm laser in the pH = 5 PBS solution, obviously higher than pH = 5 (50.9%) and pH = 7.4 (less than 10%). Pharmacokinetic experiments indicated the t1/2β, and AUC_0-t of Fe₃O₄/DOX@CNSs were 1.96 and 1.31 -fold higher than those of DOX solution, respectively. Additionally, Fe₃O₄/DOX@CNSs with NIR had the greatest tumor suppression in vitro and in vivo. Moreover, this nanosystem demonstrated distinct contrast enhancement on T2 MRI to achieve real-time imaging monitoring during treatment.

Conclusion

Fe₃O₄/DOX@CNSs is a highly biocompatible, double-triggering and improved DOX bioavailability nanosystem that combines chemo-PTT and real-time MRI monitoring to achieve integration of diagnosis and treatment of TNBC.

Diagnosis System of Microscopic Hyperspectral Image of Hepatobiliary Tumors Based on Convolutional Neural Network

Hepatobiliary tumor is one of the common tumors and cancers in medicine, which seriously affects people's lives, so how to accurately diagnose it is a very serious problem. This article mainly studies a diagnostic method of microscopic images of liver and gallbladder tumors. Under this research direction, this article proposes to use convolutional neural network to learn and use hyperspectral images to diagnose it. It is found that the addition of the convolutional neural network can greatly improve the actual map classification and the accuracy of the map, and effectively improve the success rate of the treatment. At the same time, the article designs related experiments to compare its feature extraction performance and classification situation. The experimental results in this article show that the improved diagnostic method based on convolutional neural network has an accuracy rate of 85%–90%, which is as high as 6%–8% compared with the traditional accuracy rate, and thus it effectively improves the clinical problem of hepatobiliary tumor treatment.