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.

An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle.

The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by "Jinghai VII" USV developed by Shanghai University, China, under levels 1-4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments.