Novel anti-VEGFR2 antibody-conjugated nanobubbles for targeted ultrasound molecular imaging in a rabbit VX2 hepatic tumor model.

Nanobubbles (NBs), as ultrasound contrast agents, possess the potential for clinical applications in targeted ultrasound molecular imaging due to their small diameters and the specific molecular markers attached. Previous research studies mainly focused on the tumor-specific recruitment capability or drug carriers based on subcutaneous tumor models. In clinical trials, orthotopic tumor models are considered more clinically relevant and better predictive models for assessing drug efficacy compared to standard subcutaneous models. Here, we first prepared uniform-sized NBs with a soft chitosan-lipid membrane containing perfluoropropane gas and then anti-VEGFR2 antibodies were incorporated into NB membranes in order to achieve targeting ability toward tumor angiogenesis. The results of physicochemical characterization (the average size of 260.9 ± 3.3 nm and a PDI of 0.168 ± 0.036, n = 3) indicated that the targeted nanobubbles (tNBsv) have a spherical morphology and a vacant core. In vitro experiments found that the contrast enhancement abilities of tNBsv are similar to those of commercial SonoVue. In in vivo experiments, the orthotopic model of the rabbit VX2 hepatic tumor was used to evaluate the targeted binding ability of tNBsv toward tumor angiogenesis. Ultrasound sonograms revealed that tNBsv achieved the peak intensity of ultrasound imaging enhancement in the region of peripheral vasculature of VX2 tumors over non-targeted NBs or SonoVue, and the imaging time was longer than that of the other two. Ex vivo fluorescence imaging and examination using a confocal laser scanning microscope further verified that tNBsv were capable of binding to tumor angiogenesis. These results from our studies suggested that tNBsv are useful to develop an ultrasound imaging probe to evaluate anti-angiogenic cancer therapy by monitoring tumor angiogenesis.

A thermosensitive Pickering gel emulsion with a high oil-water ratio for long-term X-ray imaging and permanent embolization of arteries.

Iodized oil has an excellent X-ray imaging effect, but it shows poor embolization performance. When used as an embolic agent, it is easily washed off by the blood flow and eliminated from the body. Therefore, it is essential to use iodized oil in combination with solid embolic agents such as gelatin sponge or to perform multiple embolization procedures to achieve the therapeutic effect. In the present study, a poly(N-isopropyl acrylamide)-co-acrylic acid (PNCAA) temperature-sensitive nanogel was synthesized by emulsion polymerization; the nanogel was then emulsified with iodized oil to prepare a thermosensitive iodized oil Pickering gel emulsion (TIPE). The oil-water (O/W) ratio of an O/W emulsion system can reach 4 : 6. When injected into the body, TIPE transforms into a nonflowing coagulated state at physiological temperature; the iodized oil is locked in the emulsion structure, thereby achieving local embolization and continuous imaging effects, which not only retain the X-ray imaging effect of the iodized oil but also improve its embolization effect. Subsequently, we further evaluated renal artery embolization in a normal rabbit renal artery model, and the results showed that TIPE shows a long-term conformal embolization performance and excellent long-term X-ray imaging ability.

Synthesis of Prostate MR Images for Classification Using Capsule Network-Based GAN Model.

Prostate cancer remains a major health concern among elderly men. Deep learning is a state-of-the-art technique for MR image-based prostate cancer diagnosis, but one of major bottlenecks is the severe lack of annotated MR images. The traditional and Generative Adversarial Network (GAN)-based data augmentation methods cannot ensure the quality and the diversity of generated training samples. In this paper, we have proposed a novel GAN model for synthesis of MR images by utilizing its powerful ability in modeling the complex data distributions. The proposed model is designed based on the architecture of deep convolutional GAN. To learn the more equivariant representation of images that is robust to the changes in the pose and spatial relationship of objects in the images, the capsule network is applied to replace CNN used in the discriminator of regular GAN. Meanwhile, the least squares loss has been adopted for both the generator and discriminator in the proposed GAN to address the vanishing gradient problem of sigmoid cross entropy loss function in regular GAN. Extensive experiments are conducted on the simulated and real MR images. The results demonstrate that the proposed capsule network-based GAN model can generate more realistic and higher quality MR images than the compared GANs. The quantitative comparisons show that among all evaluated models, the proposed GAN generally achieves the smallest Kullback-Leibler divergence values for image generation task and provides the best classification performance when it is introduced into the deep learning method for image classification task.