Assessment of rapid on-farm diagnostic and prognostic technologies for infectious and production-limiting diseases in dairy and beef cattle

Abstract

The objective of this dissertation is to explore and assess diagnostic technologies that enable rapid, on-farm detection and prognosis of diseases in cattle. This research primarily focuses on utilizing electronic nose (eNose) technology to detect infectious and production-limiting diseases. The first chapter is dedicated to the review of volatile organic compound (VOC)-based detection technologies for disease diagnosis in cattle. The review assessed three broad categories of VOC-based detection methods, including analytical chemistry instrumentation, sensor-based devices, and biological detectors. The diagnostic performance of these VOC-based detection methods was evaluated across diseases such as bovine respiratory disease (BRD), bovine tuberculosis, Johne’s disease, mastitis, metritis, ketosis, and trypanosomiasis. The review highlighted current success and existing knowledge gaps to guide future research within this field. The second chapter evaluates the use of an eNose to diagnose BRD in Holstein calves before and after an experimental challenge with bovine herpes virus-1 and Mannheimia haemolytica. Twelve calves were followed over 13 days with nasal swabs and expired air collected once daily in the pre-challenge (days 1-3) and post-challenge (days 6-13) periods. The eNose was able to correctly identify pre- and post-challenge nasal swab and expired air samples with a high degree of accuracy. The eNose demonstrated potential as a field-based diagnostic tool for the detection of BRD with nasal swabs as the optimal sample type. The third chapter expands on the work in chapter two by using the eNose for the detection of naturally occurring BRD in 363 crossbred beef cattle. The observation of clinical respiratory signs served as a comparator test to the analysis of nasal swabs by the eNose. Cattle evaluated in this study were diagnosed as either BRD or non-respiratory controls. Multiple eNose training sets were developed and tested throughout this study for assessment of diagnostic performance. Ultimately, the eNose training set developed in chapter two provided the best agreement with clinical signs for diagnosing BRD. This research supports the development and application of universal eNose training sets that can be applied across different cattle populations. The fourth chapter shifted the focus of chapters two and three from the diagnosis to the prognosis of BRD with an eNose. Crossbred beef cattle (n = 258) had nasal swabs collected at the time of their first treatment for BRD and analyzed by the eNose. Cattle were followed for 60 days post-sampling for determination of treatment-related outcomes. The eNose training sets were created and tested based on two classification methods: a two-outcome classification (first treatment success or first treatment failure) and a three-outcome classification (first treatment success, retreatment, or did not finish the 60-day follow-up due to BRD-related death or culling). Multiple eNose optimization methods were tested to increase the accuracy of the eNose's ability to correctly predict the observed treatment outcome. Unfortunately, the overall accuracy of the predicted treatment outcomes was poor for the eNose, regardless of which method was employed. The fifth chapter further evaluates BRD prognosis in crossbred beef cattle; however, a different rapid detection device was utilized. Cattle (n = 84) that were evaluated for BRD a second or greater time had whole blood samples collected, which were then analyzed using a point-of-care blood analyzer to measure cardiac troponin I (cTnI) concentrations. Cattle were followed for 60 days post collection for determination of either recovery from BRD or did not finish the 60-day follow-up period due to BRD-related death or culling. Two cTnI thresholds were independently associated with an increased probability of BRD-related culling or death. However, careful consideration regarding the test’s limitations and strategic implementation is essential for effective integration into BRD management practices. The sixth chapter compares an eNose to existing cow-side diagnostic tools for the detection of ketosis in dairy cattle. Postpartum serum, whole blood, urine, and milk samples were collected from 60 Holstein dairy cows. Laboratory serum beta-hydroxybutyrate concentrations were used to determine the true ketosis status of each cow. The eNose was used to characterize the cow’s ketosis status by analyzing milk and urine samples. These eNose results were compared with the results from urine ketone test strips and a handheld blood ketone meter. Results indicated the eNose underperformed compared to conventional cow-side ketosis detection tools. Further optimization of the eNose is needed before deployment as a field diagnostic tool. The seventh and final chapter highlights key considerations and outlines future direction for applying VOC-based detection technologies for the diagnosis and prognosis of diseases in cattle. Knowledge gleaned from chapters one, two, three, four, and six provided the framework and primary areas of focus. Emphasis is placed on the need for standardized methodologies to improve reproducibility, comparability, and eventual translation of VOC-based diagnostics into practical field applications. In conclusion, the culmination of this work has contributed to the field of rapid, on-farm diagnostics for disease detection and prognosis in cattle. Research related to an eNose as a VOC-based detection method for both ketosis and BRD represents novel approaches for the use of this tool in live animal studies. This work supports early and accurate identification of infectious and production-limiting diseases with the goal of providing tools that help veterinarians and cattle producers improve disease management, animal welfare, and overall herd productivity.