The application of antimicrobials in aquaculture primarily aims to prevent and treat bacterial infections in fish, but their inappropriate use may result in the emergence of zoonotic antibiotic-resistant bacteria and the subsequent transmission of resistant strains to humans via food consumption. The aquatic environment serves as a potential reservoir for resistant bacteria, providing an ideal breeding ground for development of antimicrobial resistance (AMR). The mutual inter-connection of intensive fish-farming systems with terrestrial environments, the food processing industry and human population creates pathways for the transmission of resistant bacteria, exacerbating the problem further. The aim of this study was to provide an overview of the most effective and available risk mitigation strategies to tackle AMR in aquaculture, based on the One Health (OH) concept. The stringent antimicrobial use guidelines, promoting disease control methods like enhanced farm biosecurity measures and vaccinations, alternatives to antibiotics (ABs) (prebiotics, probiotics, immunostimulants, essential oils (EOs), peptides and phage therapy), feeding practices, genetics, monitoring water quality, and improving wastewater treatment, rather than applying excessive use of antimicrobials, can effectively prevent the development of AMR and release of resistant bacteria into the environment and food. The contribution of the environment to AMR development traditionally receives less attention, and, therefore, environmental aspects should be included more prominently in OH efforts to predict, detect and prevent the risks to health. This is of particular importance for low and middle-income countries with a lack of integration of the national AMR action plans (NAPs) with the aquaculture-producing environment. Integrated control of AMR in fisheries based on the OH approach can contribute to substantial decrease in resistance, and such is the case in Asia, where in aquaculture, the percentage of antimicrobial compounds with resistance exceeding 50% (P50) decreased from 52% to 22% within the period of the previous two decades.

Meat production is projected to double by 2020 due to increased, per capita global consumption of meat and population growth. The livestock sector is one of the most significant contributors to urgent environmental problems. In Europe, food consumption is responsible for approximately 30% of total greenhouse gas (GHG) emissions. Meat generally has a considerably higher carbon footprint than plant-based food. This is especially true for beef, due to the emissions of methane (CH4) from enteric fermentation in ruminants

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity and specificity for the rapid and accurate point-of-care (POC) quantitative detection of selected biomolecules. In the meat production chain, their application has gained attention due to the increasing demand for enhanced food safety, quality assurance, food fraud detection, and regulatory compliance. Biosensors can detect foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria and indicators, contaminants (pesticides, dioxins, and mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters and stress hormones), and metabolites (acute-phase proteins as inflammation markers) at different modules along the meat chain, from livestock farming to packaging in the farm-to-fork (F2F) continuum. By providing real-time data from the meat chain, biosensors enable early interventions, reducing the health risks (foodborne outbreaks) associated with contaminated meat/meat products or sub-standard meat products. Recent advancements in micro- and nanotechnology, microfluidics, and wireless communication have further enhanced the sensitivity, specificity, portability, and automation of biosensors, making them suitable for on-site field applications. The integration of biosensors with blockchain and Internet of Things (IoT) systems allows for acquired data integration and management, while their integration with artificial intelligence (AI) and machine learning (ML) enables rapid data processing, analytics, and input for risk assessment by competent authorities. This promotes transparency and traceability within the meat chain, fostering consumer trust and industry accountability. Despite biosensors’ promising potential, challenges such as scalability, reliability associated with the complexity of meat matrices, and regulatory approval are still the main challenges. This review provides a broad overview of the most relevant aspects of current state-of-the-art biosensors’ development, challenges, and opportunities for prospective applications and their regular use in meat safety and quality monitoring, clarifying further perspectives.