Molecular and cellular level analysis of the mechanism of nutritional intervention in preventing epidemic virus infection
Abstract
Molecular nutrition encompasses a wider range of investigations than nutritional genomics, which can be considered a scientific investigation of how different nutrients and dietary components influence the cellular and molecular mechanisms of the body and health. In effect, increasing antioxidant elements in daily diets can assist with combating the inflammatory response caused by cytokines in the human body. Antioxidants can impede the oxidation process and hence avoid the creation of free radicals in the cytoplasm that can damage the cells via chain reactions. Contamination sequences, batch effects, uneven sampling, unreported taxa, technological biases, and heteroscedasticity are all significant issues in molecular microbial biology. Artificial Intelligence (AI) methodologies to uncover hidden patterns, connections, and interactions within metabolomics data, allowing them to provide personalized food recommendations based on individual health profiles. A multi-scale convolutional neural network (MCNN) classifies cellular images into phenotypes in a single coherent step utilizing the image’s pixel intensity values. Hence, the proposed AI-MCNN has been an essential nutrient that assists the immune system in various ways, including acting as an antioxidant to protect healthy cells, promoting immune cell development and function, and creating antibodies. According to epidemiological research, people who are undernourished are more likely to get bacterial, viral, and other diseases. Nutritional epidemiology investigates dietary or nutritional parameters concerning illness prevalence in communities. Nutritional epidemiology results often add to the evidence that provides dietary recommendations for preventing disease and related disorders.
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