Simple SummaryVeterinarians have an ethical obligation to provide good care for the animals that they see in practice. However, at times, there may be conflicts between the interests of animal caregivers or owners, the interests of veterinarians and the interests of animals. We provide an overview of why and how veterinary ethics is taught to veterinary students, as well as providing a context for thinking about veterinary ethical challenges and animal welfare issues. We argue that veterinarians are ethically obliged to speak up and ask questions when problems arise or are seen and provide a series of clinical case examples in which there is scope for veterinarians to improve animal welfare by ‘speaking up’.AbstractAlthough expectations for appropriate animal care are present in most developed countries, significant animal welfare challenges continue to be seen on a regular basis in all areas of veterinary practice. Veterinary ethics is a relatively new area of educational focus but is thought to be critically important in helping veterinarians formulate their approach to clinical case management and in determining the overall acceptability of practices towards animals. An overview is provided of how veterinary ethics are taught and how common ethical frameworks and approaches are employed—along with legislation, guidelines and codes of professional conduct—to address animal welfare issues. Insufficiently mature ethical reasoning or a lack of veterinary ethical sensitivity can lead to an inability or difficulty in speaking up about concerns with clients and ultimately, failure in their duty of care to animals, leading to poor animal welfare outcomes. A number of examples are provided to illustrate this point. Ensuring that robust ethical frameworks are employed will ultimately help veterinarians to “speak up” to address animal welfare concerns and prevent future harms.

Aquatic bird bornavirus (ABBV-1), an avian bornavirus, has been reported in wild waterfowl from North America and Europe that presented with neurological signs and inflammation of the central and peripheral nervous systems. The potential of ABBV-1to infect and cause lesions in commercial waterfowl species is unknown. The aim of this study was to determine the ability of ABBV-1 to infect and cause disease in day-old Muscovy ducks (n = 174), selected as a representative domestic waterfowl. Ducklings became infected with ABBV-1 through both intracranial and intramuscular, but not oral, infection routes. Upon intramuscular infection, the virus spread centripetally to the central nervous system (brain and spinal cord), while intracranial infection led to virus spread to the spinal cord, kidneys, proventriculus, and gonads (centrifugal spread). Infected birds developed both encephalitis and myelitis by 4 weeks post infection (wpi), which progressively subsided by 8 and 12 wpi. Despite development of microscopic lesions, clinical signs were not observed. Only five birds had choanal and/or cloacal swabs positive for ABBV-1, suggesting a low potential of Muscovy ducks to shed the virus. This is the first study to document the pathogenesis of ABBV-1 in poultry species, and confirms the ability of ABBV-1 to infect commercial waterfowl.

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Aquatic bird bornavirus (ABBV), a type of avian bornavirus, has been associated with inflammation of the central and peripheral nervous systems and neurological disease in wild waterfowl in North America and Europe. The potential of ABBV to infect and cause lesions in commercial waterfowl species is unknown. The aim of this study was to determine the ability of ABBV to infect and cause disease in day-old Muscovy ducks (n = 174), selected as a representative domestic waterfowl. Ducklings became infected with ABBV through both intracranial and intramuscular infection routes: upon intramuscular infection, the virus spread centripetally to the central nervous system (brain and spinal cord), while intracranial infection led to virus spread to the spinal cord, kidneys, proventriculus, and gonads (centrifugal spread). Infected birds developed both encephalitis and myelitis by 4 weeks post infection (wpi), which progressively subsided by 8 and 12 wpi. Despite development of microscopic lesions, clinical signs were not observed. Only five birds had choanal and/or cloacal swabs positive for ABBV, suggesting a moderate potential of Muscovy ducks to shed the virus. This is the first study to document the pathogenesis of ABBV in poultry species, and confirms the ability of ABBV to infect commercial waterfowl.

Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold is implemented to adjust for background overstaining, and an automated nuclei radius estimator is designed to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67− nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy.