Non-typhoidal Salmonella serovars are recognized as zoonotic pathogens. Although human salmonellosis is frequently associated with ingestion of contaminated foods of animal origin, contact with animals may also be a significant source of Salmonella infection, especially contact with turtles, which have shown to be an important reservoir of Salmonella, specifically through their intestinal tracts. Turtles are among the most common reptiles kept as house pets that may pose a public health risk associated with Salmonella exposure, especially among infants and young children. This review discusses the literature reporting the link between turtles and Salmonella as well as turtle-associated human salmonellosis in the last ten years. In most outbreaks, a high proportion of patients are children under five years of age, which indicates that children are at the greatest risk of turtle-associated salmonellosis. Therefore, turtles should not be preferred as recommended pets for children under five years of age. Reducing turtle stress to minimise Salmonella shedding as well as providing client education handouts at the points of sale of these animals may reduce the risk of transmitting such significant pathogen to humans. Further studies are required to investigate the role of both direct contact with turtles as well as indirect contact through cross-contamination in the transmission of turtles-associated Salmonella to humans.

The purpose of this review was to investigatethe microbial and chemical safety of camel meat and the zoonotic diseases associated with camels in the Middle East over the past decade, emphasizing the crucial role of a One Health approach. By systematically analyzing recent studies (in the past decade, from 2014), we assessed pathogen prevalence, contamination with heavy metals and pesticide residues, and the impact of zoonotic diseases like Middle East respiratory syndrome coronavirus (MERS-CoV). The findings revealed significant variability in pathogen prevalence, with the frequent detection of traditional foodborne pathogens (e.g., Salmonella and E. coli O157), as well as antibiotic-resistant strains like methicillin-resistant and vancomycin-resistant Staphylococcus aureus and extended-spectrum β-lactamase (ESBL)-producing E. coli, underscoring the need for stringent antibiotic use policies and robust food safety measures. Additionally, the review highlighted substantial contamination of camel meat with heavy metals and pesticide residues, posing significant public health concerns that necessitate stringent regulatory measures and regular monitoring. The persistent occurrence of zoonotic diseases, particularly MERS-CoV, along with other threats like trypanosomiasis, brucellosis, and Clostridium perfringens, emphasizes the importance of strengthening ongoing surveillance. Enhancing investment in diagnostic infrastructures, training programs, and planning capabilities is crucial to address these issues at the camel–human interface in the Middle East. Adopting a One Health perspective is vital to ensuring the safety and quality of camel meat and managing zoonotic risks effectively to ultimately safeguard public health and promote sustainable livestock practices.

Introduction: Non-typhoidal Salmonella are a significant global public health concern, which are responsible for considerable morbidity, mortality, and economic burden around the world. Among more than 2,600 S. enterica serovars Enteritidis and Typhimurium are the most prevalent in human infections globally. Identifying the genetic determinants that contribute to the pathogenic potential of these serovars is essential for developing prevention, control, and treatment strategies. The virulence mechanism for individual Salmonella serovars is still not well recognized. Diversity of virulence factors among serovars plays a critical role in disease severity and epidemiological success. Serovar-specific genes may influence infection strategies, host adaptation, and epidemiological patterns. Methods: Using public repositories of the NCBI Pathogen Detection database, this study aimed to determine genes that are shared and specific to each serovar and also identify significantly enriched genes in each serovar using statistical analysis. Results: While both serovars shared a conserved set of core virulence genes, significant serovar-specific differences were identified, which may reflect distinct strategies for host interaction, immune modulation, and nutrient acquisition. Conclusion: Our findings may have potential clinical and public health implications. Knowledge of serovar-specific virulence patterns may assist risk-based surveillance and targeted outbreak investigations. These findings enhance our understanding of Salmonella virulence profiling, particularly for two highly prevalent serovars in human infections, and may assist state, national, and international public health authorities in their efforts for future surveillance, risk assessment, and targeted intervention strategies.

Campylobacter spp. are leading causes of bacterial gastroenteritis globally. Swine are recognized as an important reservoir for this pathogen. The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) in Campylobacter is a global health concern. Traditional methods for detecting AMR and MDR, such as phenotypic testing or whole-genome sequencing, are resource-intensive and time-consuming. In the present study, we developed and validated a supervised machine learning model to predict MDR status in Campylobacter isolates from swine, using publicly available phenotypic AMR data collected by NARMS from 2013 to 2023. Resistance profiles for seven antimicrobials were used as predictors, and MDR was defined as resistance to at least one agent in three or more antimicrobial classes. The model was trained on 2013–2019 isolates and externally validated using isolates from 2020, 2021, and 2023. Random Forest showed the highest performance (accuracy = 99.87%, Kappa = 0.9962) among five evaluated algorithms, which achieved high balanced accuracy, sensitivity, and specificity in both training and external validation. Our feature importance analysis identified erythromycin, azithromycin, and clindamycin as the most influential predictors of MDR among Campylobacter isolates from swine. Our temporally validated, interpretable model provides a robust, cost-effective tool for predicting MDR in Campylobacter spp. and supports surveillance and early detection in food animal production systems.