Unraveling the Antimicrobial Effectiveness of Coridothymus capitatus Hydrolate against Listeria monocytogenes in Environmental Conditions Encountered in Foods: An In Vitro Study

Disclosing the potential of Cupressus leylandii A.B. Jacks & Dallim, Eucalyptus globulus Labill., Aloysia citrodora Paláu, and Melissa officinalis L. hydrosols as eco-friendly antimicrobial agents

Antimicrobial resistance is a major global health concern, threatening the effective prevention and treatment of infections caused by microorganisms. These factors boosted the study of safe and green alternatives, with hydrosols, the by-products of essential oils extraction, emerging as promising natural antimicrobial agents. In this context, four hydrosols obtained from Cupressus leylandii A.B. Jacks & Dallim, Eucalyptus globulus Labill., Aloysia citrodora Paláu and Melissa officinalis L. were studied. Their chemical composition comprises neral, geranial, 1,8-cineole, terpinen-4-ol, and oplopanonyl acetate, compounds with recognised antimicrobial activity. Concerning antimicrobial activity, significant differences were found using different hydrosol concentrations (10-20% v/v) in comparison to a control (without hydrosol), showing the potential of the tested hydrosols to inhibit the microbial growth of Escherichia coli, Staphylococcus aureus, and Candida albicans. A. citrodora hydrosol was the most effective one, inhibiting 90% of E. coli growth and 80% of C. albicans growth, for both hydrosol concentrations (p < 0.0001). With hydrosol concentration increase, it was possible to observe an improved antimicrobial activity with significant reductions (p < 0.0001). The findings of this work indicate the viability of reusing and valuing the hydrosols, encouraging the development of green applications for different fields (e.g., food, agriculture, pharmaceuticals, and cosmetics).

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.