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Using ambient mass spectrometry to elucidate treatment strategies for bovine dermatitis

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  • Full or part time
    Prof N O'Connell
    Dr R Donnelly
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Background and significance of research

Digital dermatitis is the leading infectious cause of lameness in dairy cows, resulting in reduced animal welfare and economic loss. The condition primarily manifests as skin lesions on the heels of cows, and topical treatments appear to offer the best solution. The problem persists globally, however, suggesting that treatments still need to be refined. Categorical scoring systems are widely used in the assessment of digital dermatitis lesions, but fail to acknowledge that lesions with a broadly similar appearance may have a very different structure and composition, and require different treatment. Using methodologies adopted from human cancer research, and in particular employing ambient mass spectrometry, this PhD will seek to better understand variation within and between bovine animals in the molecular profile of digital dermatitis lesion tissue. In addition, effects of different topical treatments on molecular profiles and on key markers linked to inflammation will also be assessed. Profiling of digital dermatitis lesion tissue in this manner is novel and may also have applications for other ulcerative skin conditions in farm animals.

Research aims
This research aims to advance understanding of the nature of skin lesion changes associated with the development and effective treatment of digital dermatitis. Tissue from lesions with a similar visual appearance will be evaluated to determine the level of variation in molecular composition. Key changes (particularly in inflammatory markers) associated with different treatment strategies will also be assessed.

How good is research at Queen’s University Belfast in Agriculture, Veterinary and Food Science?

FTE Category A staff submitted: 33.40

Research output data provided by the Research Excellence Framework (REF)

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