Melanie Prarat

This method comparison demonstrated equivalent analytical and diagnostic performance of IndiMix JOE with intype IC-RNA as an alternative real-time polymerase chain reaction (RT-PCR) chemistry compared to the National Animal Health Laboratory Network (NAHLN) influenza A virus (IAV) RT-PCR surveillance assay using AgPath-ID One-Step RT-PCR Reagents in avian swabs and tissues and bovine milk. In avian samples, IndiMix JOE with intype IC-RNA using a fast reduced-volume protocol had comparable results to the NAHLN reference RT-PCR with and without intype IC-RNA using standard NAHLN thermocycling conditions. A six-way comparison of RT-PCR chemistries and exogenous internal controls in milk samples illustrated equivalent mean CT values (ANOVA, p = 0.9938). Additional experiments in avian swabs and tissues, bovine milk and semen samples resulted in comparable analytical sensitivity in limits of detection (LOD), linearity (R² > 0.977), and PCR efficiencies, and no significant differences in mean CT values (ANOVA, p > 0.05). Diagnostic performance showed 100% agreement across field and spiked sample matrices compared to the NAHLN reference method. The study supports the use of IndiMix JOE and intype IC-RNA as an alternative, with practical improvements relevant to surveillance workflows including enhanced testing flexibility, efficiency, outbreak response capacity, and reduced time to results.

This study evaluated IndiMix JOE with intype IC-RNA as an alternative PCR chemistry to the National Animal Health Laboratory Network (NAHLN) Influenza A virus (IAV) PCR surveillance assay using AgPath-ID One Step PCR reagents in avian swabs and tissues and bovine milk. In avian samples, IndiMix JOE with intype IC-RNA using a fast reduced-volume (FRV, 20 µL) protocol had comparable results to NAHLN standard PCR assays (with and without intype IC) using standard NAHLN thermocycling conditions (25 µL). Precision was high, with coefficients of variation ≤ 2.61% for IAV targets and ≤ 2.03% for intype IC-RNA. Following detection of IAV in U.S. dairy cattle, a six-way comparison of PCR chemistries and exogenous internal controls in milk illustrated no significant differences in mean CT values (ANOVA, p = 0.9938). Additional experiments in avian, milk and semen samples were performed, resulting in comparable analytical sensitivity in limits of detection (LOD), linearity (R² > 0.977), and PCR efficiencies, and lacked significant differences in mean CT values (ANOVA, p > 0.05). Diagnostic sensitivity and specificity were 100% across matrices. These findings validate IndiMix JOE and intype IC-RNA as a reliable alternative reagents that enhances testing flexibility, efficiency, and outbreak response capacity.

Swine producers frequently encounter polymicrobial disease challenges, with co-infections exacerbating clinical disease and complicating response strategies. This study aimed to characterize co-diagnosis patterns in swine by integrating confirmed diagnosis cases using a standardize diagnosis system (Dx code) from the Iowa State University Veterinary Diagnostic Laboratory. As a secondary objective, the study developed the concept and implemented a framework for the technological transfer of the Dx code system from ISU-VDL to the Ohio Animal Disease Diagnostic Laboratory, and to gather Dx code data through an animal disease monitoring program, thereby creating a multi-institutional, confirmed tissue disease diagnosis database. The final collated database was harmonized and used to analyze 45,310 confirmed tissue diagnosis cases submitted between 2020 and 2025. Co-diagnosis was defined as the presence of two or more distinct etiologies within a single case. Overall, 52.62% of cases were co-diagnosed in 42 U.S. states, with a seasonal variation indicating reduced submissions and co-diagnosis rates during the summer months. The wean-to-market production phase accounted for 86.45% of co-diagnosed cases. The co-diagnosed cases were more abundant in respiratory and systemic anatomic systems, with porcine reproductive and respiratory syndrome virus (PRRSV) and Streptococcus suis being the predominant co-diagnosed pathogens. Age-specific trends revealed respiratory co-diagnoses peaking in nursery and grow-finish pigs, while digestive co-diagnoses were more common in suckling piglets. Statistical modeling using Conway-Maxwell-Poisson regression revealed that co-diagnosis cases involving bacterial and viral insults had significantly higher numbers of distinct etiologies (IRR = 2.48; CI: 1.66–3.66) compared with co-diagnosis cases without bacterial or viral involvement, with an expected count of 2.95 distinct etiologies per case. The study demonstrated the value of standardized diagnostic coding for epidemiological surveillance and highlights the complexity of co-infections in swine. Additionally, the findings underscore the importance of collaborative data sharing in enhancing swine health management strategies.

Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs). We found that most Salmonella isolates (46/69, 67%) had no known resistance genes. Several isolates from both food and companion animals, however, showed genetic relatedness to isolates from humans. For pathogenic E. coli, no resistance genes were identified in 60% (38/63) of the isolates. Diverse resistance patterns were observed, and one of the isolates had predicted resistance to fluoroquinolones and cephalosporins, important antibiotics in human and veterinary medicine. For S. pseudintermedius, we observed a bimodal distribution of resistance genes, with some isolates having a diverse array of resistance mechanisms, including the mecA gene (19/60, 32%). The findings from this study highlight the critical importance of veterinary diagnostic laboratory data as part of any national antimicrobial resistance surveillance program. The finding of some highly resistant bacteria from companion animals, and the observation of isolates related to those isolated from humans demonstrates the public health significance of incorporating companion animal data into surveillance systems. Vet-LIRN will continue to build the infrastructure to collect the data necessary to perform surveillance of resistant bacteria as part of fulfilling its mission to advance human and animal health. A One Health approach to AMR surveillance programs is crucial and must include data from humans, animals, and environmental sources to be effective.