Abstract: The present disclosure relates to a strain of Bifidobacterium animalis subsp. lactis HEM20-01 (KCTC14143BP) and a composition for treating, preventing or alleviating depression, comprising the same. A strain of Bifidobacterium animalis subsp. lactis HEM20-01 (KCTC14143BP) according to an embodiment of the present disclosure can treat or prevent depression by reducing corticosterone, endotoxin and pro-inflammatory cytokines. Therefore, the strain can be applied to pharmaceutical compositions, food compositions, health functional food compositions, and the like for treating or preventing depression.

Abstract: A method for determining whether enteritis is present by using a machine learning model may include a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition, a process of extracting multiple microbial data based on an analysis result of the mixture, a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm, a process of training the machine learning model with the microbe-related features, and a process of inputting, to the trained machine learning model, the microbial data collected from the subject to be tested and determining whether enteritis is present. The microbe-related features may include the amount of one or more microbes selected from genera included in families, Ruminococcaceae, Lactobacillaceae, Prevotellaceae, Barnesiellaceae, Bacteroidaceae, Lachnospiraceae, and UCG.

Abstract: A method for determining whether constipation is present by using a machine learning model. The method includes a process of analyzing a mixture of a gut-derived substance collected from a subject and a gut environment-like composition, a process of extracting multiple microbial data based on an analysis result of the mixture, a process of selecting microbe-related features to be used in the machine learning model from the multiple microbial data based on a predetermined feature selection algorithm, a process of training the machine learning model with the microbe-related features, and a process of inputting, to the trained machine learning model, the microbial data collected from the subject to be tested and determining whether constipation is present.

Abstract: The present disclosure relates to a strain of Lactobacillus sakei HEM224 (KCTC14065BP) and a composition for treating, preventing or alleviating inflammation or asthma, comprising the strain. A strain of Lactobacillus sakei HEM224 (KCTC14065BP) according to an embodiment of the present disclosure inhibits the production of pro-inflammatory factors, and exhibits an effect of treating asthma in an asthma animal model. Therefore, the strain can be applied to pharmaceutical compositions, food compositions, health functional food compositions and feed compositions for treating, preventing or alleviating inflammation or asthma.

Abstract: The present disclosure relates to a composition for screening an intestinal environment-improving material and a screening method using the composition, and according to the composition and the method of the present disclosure, it is possible to provide an effective analysis method for screening a microbiota-improving candidate material in a personalized manner by providing a method for verifying personalized probiotics, prebiotics, foods, health functional foods and drugs under in vitro conditions based on microbiota and microbiota metabolites.