Listeria monocytogenes is a food-borne pathogen that can result in adverse pregnancy outcomes, such as stillbirth or premature delivery. The Mongolian gerbil was recently proposed as the most appropriate small-animal model of listeriosis due to its susceptibility to the same invasion pathways as humans. The objectives of this study were to investigate invasion and adverse pregnancy outcomes in gerbils orally exposed to L. monocytogenes, to compare the dose-response data to those of other animal models, and to investigate differences in the responses of pregnant versus nonpregnant gerbils. Gerbils were orally exposed to 0 (control), 10 3 , 10 5 , 10 7 , or 10 9 CFU L. monocytogenes in whipping cream. L. monocytogenes was recovered in a dose-dependent manner from fecal samples, adult organs, and pregnancy-associated tissues. Dams exposed to 10 9 CFU had more invaded organs and higher concentrations of L. monocytogenes in almost all organs than nonpregnant animals, though no differences in fecal shedding were seen between the two groups. Adverse pregnancy outcomes occurred only in the dams treated with 10 9 CFU. A 50% infectivity dose (ID 50 ) of 2.60 ؋ 10 6 CFU for fetuses was calculated by fitting the data to a logistic model. Our results suggest that the 50% lethal dose (LD 50 ) falls within the range of 5 ؋ 10 6 to 5 ؋ 10 8 CFU. This range includes the guinea pig and nonhuman primate LD 50 s, but the observation that L. monocytogenes-induced stillbirths can be seen in guinea pigs and primates exposed to lower doses than those at which stillbirths were seen in gerbils indicates that gerbils are not more sensitive to L. monocytogenes invasion.

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The gastrointestinal (GI) tract is one the biggest mucosal surface in the body and one of the primary targets for the delivery of therapeutics, including immunotherapies. GI diseases, including, e.g., inflammatory bowel disease and intestinal infections such as cholera, pose a significant public health burden and are on the rise. Many of these diseases involve inflammatory processes that can be targeted by immune modulatory therapeutics. However, nonspecific targeting of inflammation systemically can lead to significant side effects. This can be avoided by locally targeting therapeutics to the GI tract and its mucosal immune system. In this review, we discuss nanomaterial-based strategies targeting the GI mucosal immune system, including gut-associated lymphoid tissues, tissue resident immune cells, as well as GI lymph nodes, to modulate GI inflammation and disease outcomes, as well as take advantage of some of the primary mechanisms of GI immunity such as oral tolerance.

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The use of single cell analysis methods has grown rapidly in the last two decades and has led to rapid discoveries in cell biology and beyond. Single cell analysis requires complex systems like tissues to be dissociated, separating individual cells from extracellular tissue materials. This requires manual processing of tissues and materials through chopping, pipetting, and suspension with enzymes for degradation of the structural elements of the tissue. Manual processing can be time consuming and lead to variability between scientists. Automating this process through motorized dissociation could thus improve reproducibility of research and reduce time of cell manipulation prior to analysis. Here, we have designed a low-cost, customizable automatic tissue dissociator device that can be easily assembled by research groups for individual use. Our device allows for customizable programmed dissociation protocols for ease of use and reproducibility between researchers and can be placed into heat or cold environments based on the protocol need. We have found this device comparable in cell viability and reproducibility to manual dissociation, while significantly reducing time spent and even enhancing cells extracted from more fibrous tissues. Broad dissemination and use of this device could enhance single cell analysis reproducibility and provide a time-saving alternative to the currently used manual dissociation protocols.