Untargeted Metabolomics on Skin Mucus Extract of Channa argus against Staphylococcus aureus: Antimicrobial Activity and Mechanism

Microbial contamination is one of the most common food safety issues that lead to food spoilage and foodborne illness, which readily affects the health of the masses as well as gives rise to huge economic losses. In this study, Channa argus was used as a source of antimicrobial agent that was then analyzed by untargeted metabolomics for its antibacterial mechanism against Staphylococcus aureus. The results indicated that the skin mucus extract of C. argus had great inhibitory action on the growth of S. aureus, and the morphology of S. aureus cells treated with the skin mucus extract exhibited severe morphological damage under scanning electron microscopy. In addition, metabolomics analysis revealed that skin mucus extract stress inhibited the primary metabolic pathways of S. aureus by inducing the tricarboxylic acid cycle and amino acid biosynthesis, which further affected the normal physiological functions of biofilms. In conclusion, the antimicrobial effect of the skin mucus extract is achieved by disrupting cell membrane functions to induce an intracellular metabolic imbalance. Hence, these results conduce to amass novel insights into the antimicrobial mechanism of the skin mucus extract of C. argus against S. aureus.

Medical Image Recognition Technology in the Effect of Substituting Soybean Meal for Fish Meal on the Diversity of Intestinal Microflora in Channa argus

Purpose. To study the application of medical image recognition technology based on backpropagation neural network (BPNN) in the effect of soybean meal replacing fish meal on intestinal microbial diversity of Channa argus and to evaluate the application value of this intelligent algorithm, Channa argus was fed with different contents of soybean meal instead of fish meal. Methods. After intestinal samples were collected and bacteria were isolated, microscopic imaging was performed, and the images were classified and identified. BPNN was constructed to perform denoising, smoothing, and segmentation. Results. After BPNN processing, the bacteria were completely separated from the original image background, and the bacteria was in the closed state, which was beneficial to feature extraction and species recognition. If there were 2 hidden layer nodes, the segmentation accuracy of bacterial microscopic images was the highest, up to 97.3%. With the replacement ratio of fish meal increased, the species of intestinal microbiome gradually enriched, and the relative abundance of intestinal microbiome was higher after fish meal was completely replaced by soybean meal (replacement). The intestinal microbial enzyme activities were affected by different fish meal and soybean meal contents in the diet. The glutamate transaminase and adenosine deaminase activities were increased after the replacement and were higher than those before the replacement, with statistically significant differences ( P < 0.05 ). Conclusion. Replacement of fish meal with soybean meal has a significant effect on the intestinal flora diversity of Channa argus, and there is a close relationship between them. The image recognition technology based on BPNN has high recognition rate and segmentation accuracy for microbiological microscopic images.