Background: 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.Results: We found that vancomycin exposure disrupted the gut microbiome, altered host metabolic phenotypes, and facilitated cognitive impairment in T1D mice. Long-term acetate deficiency due to depletion of acetate-producing bacteria resulted in the reduction of synaptophysin (SYP) in the hippocampus as well as learning and memory impairments. Exogenous acetate supplement or fecal microbiota transplant recovered hippocampal SYP level in vancomycin-treated T1D mice, and this effect was attenuated by vagal inhibition or vagotomy.Conclusions: Our results demonstrate the protective role of microbiota metabolite acetate in cognitive functions and suggest long-term acetate deficiency as a risk factor of cognitive decline.

In this study, the usefulness of different mathematical models to predict the retention of ascorbic acid (AA), total phenols (TP) and DPPH radical scavenging activity (SA) of pasteurized pineapple juices during storage has been evaluated. The results indicate that first-order kinetic model provided the best prediction of the degradation of AA (MAE = 0.0059; MRE = 0.0117; MSE = 0.0001; R 2 = 0.9995) whereas Weibull model provided the less satisfactory prediction (MAE = 0.2387; MRE = 0.2911; MSE = 0.0769; R 2 = 0.9993). In addition, zero-order (MAE = 0.0016; MRE = 0.0017; MSE = 0.0000; R 2 = 0.9992) and first-order (MAE = 0.0027; MRE = 0.0029; MSE = 0.0000; R 2 = 0.9989) kinetic models are adequate models for application in the prediction of TP loss, and first-order kinetic (MAE = 0.0294; MRE = 0.0549; MSE = 0.0015; R 2 = 0.9845) and Weibull (MAE = 0.0533; MRE = 0.0881; MSE = 0.0044; R 2 = 0.9823) models are recommended to predict the change of SA. These results show that the behaviors of AA, TP and SA during storage of juices can be accurately described by different prediction models. Therefore, the selection of the most adequate model is of great importance to predict nutritional deterioration in food products during storage.