Advanced Organotypic In Vitro Model Systems for Host-Microbial Coculture

Coculture systems to study interactions between anaerobic bacteria and intestinal epithelium

Coculture systems (CCSs) are experimental tools used to study the interactions of anaerobic bacteria among themselves and the gut epithelial cells under conditions simulating the human gut, unlike those in animal models. Although the studies on animal models are useful in determining the relationship between the causative agents of infections and human infections, they have disadvantages, such as ethical issues, in addition to the differences in the microbiota of the animal and humans. Therefore, the results obtained using animal models cannot be directly extrapolated to humans. CCSs can more completely reflect in vivo gut homeostasis and contribute to better understanding of the interplay between the intestinal cells and anaerobes, prevalent among the gut bacteria. Moreover, they provide new insights on the pathogenesis of infections and aid in assessing the usefulness of new probiotics and antibacterials. Therefore, CCSs, including the gut-on-a-chip models, can significantly improve microbiota-based therapy. Moreover, they can also be used to detect microbiota-derived metabolites such as those with mutagenic properties. The aim of this review was to explore selected CCS models of anaerobes with intestinal epithelium and their application in investigating intestinal homeostasis. The focus was to highlight the application of different CCSs and important data obtained from their implementation.

Uncovering layer by layer the risk of nanoplastics to the environment and human health

Nanoplastics (NPs), defined as plastic particles with dimensions less than 100 nm, have emerged as a persistent environmental contaminant with potential risk to both environment and human health. Nanoplastics might translocate across biological barriers and accumulate in vital organs, leading to inflammatory responses, oxidative stress, and genotoxicity, already reported in several organisms. Disruptions to cellular functions, hormonal balance, and immune responses were also linked to NPs exposure in in vitro assays. Further, NPs have been found to adsorb other pollutants, such as persistent organic pollutants (POPs), and leach additives potentially amplifying their advere impacts, increasing the threat to organisms greater than NPs alone. However, NPs toxic effects remain largely unexplored, requiring further research to elucidate potential risks to human health, especially their accumulation, degradation, migration, interactions with the biological systems and long-term consequences of chronic exposure to these compounds. This review provides an overview of the current state-of-art regarding NPs interactions with environmental pollutants and with biological mechanisms and toxicity within cells.

Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra

Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.