Bioinformatics Image Recognition Reveals Bidirectional Enhancement Between Bifidobacteria and Host Protein Metabolism

Abstract

As a key representative of intestinal symbiotic bacteria, Bifidobacteria occupy a central place in the regulation of the nitrogen balance of the host organism. However, the mechanism of bilateral interaction between microflora and protein exchange of the organism is not yet clear. The lack of this knowledge prevents the development of appropriate intervention strategies, and optimizing protein use and improving metabolism also pose challenges. This study combines deep learning image recognition technology and visual metabolite analysis technology for in-depth analysis of the complex relationship between them. Thanks to the fluorescent marking of Bifidobacterium long using GFP - and using the ResNet50 model for microscopic examination to assess their reproduction and binding status, a high classification accuracy of over 94% was obtained, and based on 8,000 training samples, their real-time colonization process was monitored. Studies have shown that the use of chromatographic scanners and other instruments allows to determine which substances associated with casein are determined by the host organism, which reflects the high efficiency of the long-term metabolic link between the host organism and Bifidobacteria, and the dynamic response associated with physiological performance can be improved. Omics-based imaging technology allows accurate imaging of the characteristics of the interaction between probiotics and the carrier body. The development of the analysis platform contributes to the research of mechanisms, precise nutrition and the development of Clinical Interventions and is of enormous importance. The goal is to optimize the balance of nitrogen in the body and overall metabolic health.