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The microbiota from different body sites are dominated by different major groups of microbes, but the variations within a body site such as the mouth can be more subtle. Accurate predictive models can serve as useful tools for distinguishing sub-sites and understanding key organisms and their roles and can highlight deviations from expected distributions of microbes. Good classification depends on choosing the right combination of classifier, feature representation, and learning model. Machine-learning procedures have been used in the past for supervised classification, but increased attention to feature representation and selection may produce better models and predictions.
Modification of the gut microbiota has been reported to reduce the incidence of type 1 diabetes mellitus (T1D). We hypothesized that the gut microbiota shifts might also have an effect on cognitive functions in T1D. Herein we used a non-absorbable antibiotic vancomycin to modify the gut microbiota in streptozotocin (STZ)-induced T1D mice and studied the impact of microbial changes on cognitive performances in T1D mice and its potential gut-brain neural mechanism.
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