Current Research Status of Azaspiracids.

The presence and impact of toxins have been detected in various regions worldwide ever since the discovery of azaspiracids (AZAs) in 1995. These toxins have had detrimental effects on marine resource utilization, marine environmental protection, and fishery production. Over the course of more than two decades of research and development, scientists from all over the world have conducted comprehensive studies on the in vivo metabolism, in vitro synthesis methods, pathogenic mechanisms, and toxicology of these toxins. This paper aims to provide a systematic introduction to the discovery, distribution, pathogenic mechanism, in vivo biosynthesis, and in vitro artificial synthesis of AZA toxins. Additionally, it will summarize various detection methods employed over the past 20 years, along with their advantages and disadvantages. This effort will contribute to the future development of rapid detection technologies and the invention of detection devices for AZAs in marine environmental samples.

U-Net combined with multi-scale attention mechanism for liver segmentation in CT images.

BACKGROUND: The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).