Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling.

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

Validation of Iranian Smell Identification Test for screening of mild cognitive impairment and Alzheimer's disease.

BACKGROUND: According to the World Alzheimer Report 2019, an estimated 50 million people worldwide are living with dementia. The smell test is a method for early detection of Alzheimer's disease (AD) as an inexpensive, simple, and noninvasive screening tool. This study aimed to evaluate the accuracy of the Iran Smell Identification Test (Iran-SIT) in discriminating patients with AD, with mild cognitive impairment (MCI), and the healthy subjects. METHODS: In this study, 42 patients with AD, 33 with MCI, and 32 healthy controls were recruited from the referral Memory Clinic of Tehran University of Medical Sciences. The olfactory function was examined with six odors through Iran-SIT. RESULTS: We found a significant difference among the olfactory function in subjects with normal cognitive status, that of those with MCI and those with AD (p < 0.001). The cutoff point for the diagnosis of AD was (sensitivity and specificity were, respectively, 85.7 and 90.8%), and (Sensitivity and specificity were, respectively, 93.9 and 100%) for MCI. CONCLUSION: These results suggest that Iran-SIT is a valid biomarker and practical screening tool, with simple, inexpensive, and readily available for use in combination with neuropsychological tools and neuroimaging for early detection of AD.