No abstract

Angioplasty with stents is the most important method for the treatment of coronary artery disease (CAD). However, the drug-eluting stents (DES) that are widely used have the increased risks of inflammatory reactions and late stent thrombosis (LST) because of the persistence of the polymer coatings. To improve the biosafety, a novel polymer-free-composite drug-eluting coating composed of magnetic mesoporous silica nanoparticles (MMSNs) and carbon nanotubes (CNTs) was constructed using the electrophoretic deposition (EPD) method in this study. A crack-free two-layered coating with impressive network nanotopologies was successfully obtained by regulating the composition and structures. This nanostructured coating exhibits excellent mechanical flexibility and blood compatibility in vitro, and the drug-loading and release performance is satisfactory as well. The in vivo study shows that this composite coating has the obvious advantage of rapid endothelialization because of its unique 3D nanostructured topology in comparison with the commercial polymer-coated DES. This study aims to provide new ideas and reliable data to design novel functional coatings that could accelerate the re-endothelialization process and avoid inflammatory reactions, thus improving the in vivo biosafety of DES.

Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.

In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs. Our previous work combined generative adversarial networks (GANs) and pseudolabeling methods to transfer labels from one specie to another to save labeling costs. However, only the color and texture features of images can be migrated, which still needs improvement in the accuracy of the data labeling. Therefore, this study proposes an EasyDAM_V2 model as an improved data labeling method for multishape and cross-species fruit detection. First, an image translation network named the Across-CycleGAN is proposed to generate fruit images from the source domain (fruit image with labels) to the target domain (fruit image without labels) even with partial shape differences. Then, a pseudolabel adaptive threshold selection strategy was designed to adjust the confidence threshold of the fruit detection model adaptively and dynamically update the pseudolabel to generate labels for images from the unlabeled target domain. In this paper, we use a labeled orange dataset as the source domain, and a pitaya, a mango dataset as the target domain, to evaluate the performance of the proposed method. The results showed that the average labeling precision values of the pitaya and mango datasets were 82.1% and 85.0%, respectively. Therefore, the proposed EasyDAM_V2 model is proven to be used for label transfer of cross-species fruit even with partial shape differences to reduce the cost of data labeling.

The release of cupric ion from copper intrauterine device (Cu-IUD) in human uterus is essential for contraception. However, excessive cupric ion will cause cytotoxic effect. In this paper, we investigated the influence of device characteristics (frame, copper surface area, shape, copper type and indomethacin) on copper release for the efficacy and adverse effects vary with IUD types which may correlate to their different release behaviors. Nine types of Cu-IUDs were selected and incubated in simulated uterine fluid. They were paired for comparison based on the device properties and the release of cupric ion was determined by flame atomic absorption spectrometer for about 160 days. The result showed that there was a burst release during the first month and the release rate tends to slow down and become steady afterwards. In addition, the copper release was mainly influenced by frame, indomethacin and copper type (copper wire and copper sleeve) while the shape variation had little effect on copper release throughout the experiment. Moreover, the influence of copper surface area was only noticeable during the first month. These findings were seldom reported before and may provide some useful information for the design of Cu-IUDs.