Anti-Proteolytic Peptide R7I Protects the Intestinal Barrier and Alleviates Fatty Acid Malabsorption in Salmonella typhimurium-Infected Mice.

With a wide range of hosts, environmental adaptation, and antibiotic resistance, Salmonella typhimurium is one of the most common causes of food poisoning in the world. Infection with Salmonella typhimurium not only results in intestinal inflammation but also damages the intestinal barrier and interferes with the host's ability to absorb nutrients. It is imperative to find alternatives to antibiotics for eradicating bacteria, reducing intestinal damage, and reestablishing nutrient absorption, especially given that antibiotics are currently prohibited. This research aims to understand the protective role of anti-proteolytic peptide R7I on the gut in the setting of Salmonella typhimurium infection and its impact on nutritional absorption, maybe offering an alternative to antibiotics for bacterial killing. The findings demonstrated that R7I reduced the production of inflammatory factors, including IL-6, TNF-α, and L-1ß in the jejunum and decreased the expression of genes like TLR4 and NF-κB in the jejunum (p < 0.05). R7I enhanced antioxidant capacity and preserved the antioxidant/pro-oxidant balance in the jejunum (p < 0.05). R7I also normalized intestinal shape and restored tight junction protein expression. Fatty acid binding protein 2 (FABP2) and fatty acid transport protein 4 (FATP4) expression in the jejunum was restored by R7I. In addition, serum-free fatty acids and lipid metabolites were significantly higher in the R7I group than in the control group (p < 0.05). Overall, the anti-enzyme peptide R7I maintained the healthy state of the intestine and alleviated the abnormal fatty acid absorption caused by bacterial infection.

Keyframe image processing of semantic 3D point clouds based on deep learning.

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a "semantic gap" between this and people's understanding of images. To address this "semantic gap," a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques.