Development of the FECPAKG2 system to manage fluke infection in sheep and cattle

  • Claire Reigate

Student thesis: Doctoral ThesisDoctor of Philosophy


Good diagnostics are fundamental to the war on disease. Fasciola hepatica (liver fluke) and Calicophoron daubneyi (rumen fluke) are parasitic flukes that can infect sheep and cattle reared on pasture-based systems, and are a challenge to livestock farmers globally. Currently control on-farm largely relies heavily on anthelmintic treatments, often without a confirmed diagnosis. There are multiple immunological and coprological tests available to diagnose liver fluke infection (fasciolosis), however no single test can be considered as having adequately high sensitivity and specificity in the field setting, and none can be considered as ‘pen-side’ for use directly on farms. Rumen fluke infection (paramphistomosis) is considered an emerging yet widespread issue in the UK, and diagnosis of patent infection is limited to faecal egg counts (FECs). Fluke faecal egg counts (flukeFECs) are a simple, non-invasive method used to detect the presence of patent liver and rumen fluke infection. A range of flukeFEC protocols exist but they vary in complexity, precision and accuracy and have not yet been adapted for on-farm use. The FECPAKG2 system is a commercial FEC kit developed for farmers, allowing them to perform regular FECs on their livestock to diagnose and monitor nematode infection. Flukefinder® is a commercially available flukeFEC kit. The current work has developed and tested a protocol that amalgamates a modified Flukefinder® method with the FECPAKG2 system to detect F. hepatica and C. daubneyi eggs in artificially spiked sheep and cattle faeces. A sedimentation cassette was designed for the purposes of this project and two protocols were developed (FECPAK-ALL and FECPAK-GRID), dependent on the area of the slide that was observed. The egg recovery capabilities of these methods were compared with two other flukeFEC methods (Flukefinder® and the Becker Sedimentation) at different egg concentrations (2, 5, 10 and 20 epg). Overall, fluke eggs were more likely to be recovered from cattle faeces than sheep (P < 0.001). FECPAK-ALL and FECPAK-GRID had fluke egg detection limits of 10 epg and 5 epg in sheep and cattle, respectively. Liver fluke eggs are more readily recovered from cattle and sheep faeces than rumen fluke eggs (P < 0.001). Following the eggspiking experiments, the Flukefinder®-FECPAK (FF-FEC) method was evaluated in naturally infected sheep in an abattoir setting. A Bayesian Latent Class Modelling approach was used to estimate the diagnostic characteristics of fluke-FECPAKG2, gall bladder examination and liver inspections in the absence of a gold standard test under different modelling scenarios. Model 3 reported a sensitivity estimate for FF-FEC of 0.690 (BCI 0.573 – 0.797). To investigate the dynamics of liver and rumen fluke egg shedding in naturally infected ewes, a 7-day longitudinal study was undertaken. The flukeFEC variability within and between animals, and within faecal sample was highlighted. Lastly, the potential of using machine learning to identify fluke eggs in digital images was explored. Preliminary testing suggests the machine learning algorithm has the capacity to recognise liver fluke eggs in an image as it had a sensitivity and specificity of the 85.5% and 94.0% respectively. The machine learning algorithm was able to successfully discriminate between liver and rumen fluke eggs. The results from this research have provided the tools to develop a commercial flukeFEC kit that farmers, veterinarians and researchers can use to detect and monitor liver and rumen fluke infection on farms. It has also highlighted the complexity of interpretating flukeFECs results and presented encouraging results exposing the exciting possibility of employing machine learning in parasite diagnostics.
Date of Award2021
Original languageEnglish
Awarding Institution
  • Aberystwyth University
SponsorsKnowledge Economy Skills Scholarships
SupervisorPeter Brophy (Supervisor) & Hefin Williams (Supervisor)

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