Achieving effective treatment of deep-seated tumors is a major challenge for traditional photodynamic therapy (PDT) due to difficulties in delivering light into the sub-surface. Thanks to its great tissue penetration, X-rays hold the potential to become an ideal excitation source for activating photosensitizers (PS) that accumulate in deep tumor tissue. Recently, a wide variety of nanoparticles have been developed for this purpose. The nanoparticles are not only designed as carriers for loading various kinds of PSs, but also can facilitate the activation process by transferring energy harvested from X-ray irradiation to the loaded PS. In this review, we focus on recent developments of nanoscintillators with high energy transfer efficiency, their rational designs, as well as potential applications in next generation PDT. Treatment of deep-seated tumors by using radioisotopes as an internal light source will also be discussed.
Contrast agents (CAs) play a crucial role in high-quality magnetic resonance imaging (MRI) applications. At present, as a result of the Gd-based CAs which are associated with renal fibrosis as well as the inherent dark imaging characteristics of superparamagnetic iron oxide nanoparticles, Mn-based CAs which have a good biocompatibility and bright images are considered ideal for MRI. In addition, manganese oxide nanoparticles (MONs, such as MnO, MnO2, Mn3O4, and MnOx) have attracted attention as T1-weighted magnetic resonance CAs due to the short circulation time of Mn(II) ion chelate and the size-controlled circulation time of colloidal nanoparticles. In this review, recent advances in the use of MONs as MRI contrast agents for tumor detection and diagnosis are reported, as are the advances in in vivo toxicity, distribution and tumor microenvironment-responsive enhanced tumor chemotherapy and radiotherapy as well as photothermal and photodynamic therapies.
Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.
Three-dimensional (3D) hierarchical nanostructures have been considered as one of the most promising surface-enhanced Raman spectroscopy (SERS) substrates because of the ordered arrangement of high-density hotspots along the third dimension direction. Herein, we reported a unique 3D nanostructure for SERS detection based on silver nanoparticles (AgNPs) decorated zinc oxide/silicon (ZnO/Si) heterostructured nanomace arrays. They were prepared by two steps: (1) Si nanoneedles were grafted onto ZnO nanorod arrays via a catalyst-assisted vapor-liquid-solid (VLS) growth mechanism. (2) AgNPs were rapidly immobilized on the surface of nanomaces by a facile galvanic displacement reaction. The fabricated substrates were employed to detect rhodamine 6G (R6G) with a detection limit down to 10(-16) M, and exhibited a high-enhanced performance (enhancement factor (EF) as high as 8.7 × 10(7)). To illustrate the potential value of the prepared substrates, the different concentrations of melamine aqueous solution (from 10(-4) to 10(-10) M) were detected, and a quantitative relationship between the SERS spectrum intensity and the melamine concentration had been established. In addition, the measure of melamine residual in pure milk was carried out successfully, and the results indicated that the prepared 3D nanomace substrates had great potential in food inspection, environment protection, and a few other technologically important fields.
Manganese oxide nanoparticles (Mn3O4 NPs) have attracted a great deal of attention in the field of biomedical imaging because of their ability to create an enhanced imaging signal in MRI as novel potent T1 contrast agents. In this study, we present tumor vasculature-targeted imaging in mice using Mn3O4 NPs through conjugation to the anti-CD105 antibody TRC105 and radionuclide copper-64 (64Cu, t1/2: 12.7 h). The Mn3O4 conjugated NPs, 64Cu-NOTA-Mn3O4@PEG-TRC105, exhibited sufficient stability in vitro and in vivo. Serial positron emission tomography (PET) and magnetic resonance imaging (MRI) studies evaluated the pharmacokinetics and demonstrated targeting of 64Cu-NOTA-Mn3O4@PEG-TRC105 to 4T1 murine breast tumors in vivo, compared to 64Cu-NOTA-Mn3O4@PEG. The specificity of 64Cu-NOTA-Mn3O4@PEG-TRC105 for the vascular marker CD105 was confirmed through in vivo, in vitro, and ex vivo experiments. Since Mn3O4 conjugated NPs exhibited desirable properties for T1 enhanced imaging and low toxicity, the tumor-specific Mn3O4 conjugated NPs reported in this study may serve as promising multifunctional nanoplatforms for precise cancer imaging and diagnosis.
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