Impact of Western Diet on Enterohemorrhagic Escherichia coli Colonization in the Human In Vitro Mucosal Artificial Colon as Mediated by Gut Microbiota.

Enterohemorrhagic Escherichia coli (EHEC) is a major food-borne pathogen that causes human disease ranging from diarrhea to life-threatening complications. Accumulating evidence demonstrates that the Western diet enhances the susceptibility to enteric infection in mice, but the effect of diet on EHEC colonization and the role of human gut microbiota remains unknown. Our research aimed to investigate the effects of a Standard versus a Western diet on EHEC colonization in the human in vitro Mucosal ARtificial COLon (M-ARCOL) and the associated changes in the gut microbiota composition and activities. After donor selection using simplified fecal batch experiments, two M-ARCOL bioreactors were inoculated with a human fecal sample (n = 4) and were run in parallel, one receiving a Standard diet, the other a Western diet and infected with EHEC O157:H7 strain EDL933. EHEC colonization was dependent on the donor and diet in the luminal samples, but was maintained in the mucosal compartment without elimination, suggesting a favorable niche for the pathogen, and may act as a reservoir. The Western diet also impacted the bacterial short-chain fatty acid and bile acid profiles, with a possible link between high butyrate concentrations and prolonged EHEC colonization. The work demonstrates the application of a complex in vitro model to provide insights into diet, microbiota, and pathogen interactions in the human gut.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

A videogame intervention for tobacco product use prevention in adolescents.

PURPOSE: This pilot study evaluated the short-term effects of an interactive videogame on changing adolescent knowledge, beliefs and risk perceptions, and intentions to use e-cigarettes, cigarettes, and other tobacco products. A secondary aim was to evaluate players' game experience. METHODS: Participants (N = 80 11-14 year olds) were recruited from 7 community-based afterschool programs in New Haven, Connecticut and Los Angeles, California. The design was a single group pre-post design with replication. A pre-test survey was administered that included demographic variables and knowledge, risk perceptions, beliefs, and intentions to use e-cigarettes, cigarettes, and other tobacco products. An interactive videogame focusing on risky tobacco use situations was subsequently played in four 60-min sessions over a four-week period, followed by a post-test survey. Analyses included paired t-tests of pre-post videogame change, regression analyses, and path analyses testing mediational effects of beliefs and risk perceptions on the relationship between knowledge and intentions. RESULTS: The videogame changed knowledge of e-cigarettes and other tobacco products (p's < 0.001), risk perceptions of cigarettes and e-cigarettes (p < .01 and p < .001, respectively), and beliefs about e-cigarettes and other tobacco products (p's < 0.05), but not intentions. Older adolescents reported greater e-cigarette knowledge and risk perceptions (p's < 0.05), and females reported greater risk perception of cigarettes (p < .05). Beliefs mediated the relationship between knowledge and intentions to use e-cigarettes (indirect effect p < .05). CONCLUSION: Results suggest that brief exposure (4 h over 4 weeks) to a videogame focused on changing knowledge and attitudes towards tobacco products may have a promising effect on preventing risk for early adolescent tobacco product use, particularly for e-cigarettes.