Inefficacy of selegiline in treatment of canine pituitary-dependent hyper-adrenocorticism

Objective: To evaluate selegiline, a monoamine oxidase-B inhibitor, for treating dogs with pituitary-dependent hyperadrenocorticism. Design: Prospective clinical trial using client-owned dogs with pituitary-dependent hyperadrenocorticism treated at The University Veterinary Centre, Sydney, from September 1999 to July 2001. Procedure: Eleven dogs with pituitary-dependent hyperadrenocorticism treated with selegiline were monitored at days 10, 30 and 90 by clinical examination, tetracosactrin stimulation testing, urinary corticoid:creatinine ratio measurement and client questionnaire. Endogenous adrenocorticotropic hormone measurements were also performed on most dogs on days 0 and 90. Results: No dog treated with selegiline had satisfactory control of disease. Conclusion: Selegiline administration was safe and free of side-effects at the doses used, but did not satisfactorily control disease in pituitary-dependent hyperadrenocorticism affected dogs

Data from: Expected total thyroxine (TT4) concentrations and outlier values in 531,765 cats in the United States (2014-2015)

Background: Levels exceeding the standard reference interval (RI) for total thyroxine (TT4) concentrations are diagnostic for hyperthyroidism, however some hyperthyroid cats have TT4 values within the RI. Determining outlier TT4 concentrations should aid practitioners in identification of hyperthyroidism. The objective of this study was to determine the expected distribution of TT4 concentration using a large population of cats (531,765) of unknown health status to identify unexpected TT4 concentrations (outlier), and determine whether this concentration changes with age. Methodology/Principle Findings: This study is a population-based, retrospective study evaluating an electronic database of laboratory results to identify unique TT4 measurement between January 2014 and July 2015. An expected distribution of TT4 concentrations was determined using a large population of cats (531,765) of unknown health status, and this in turn was used to identify unexpected TT4 concentrations (outlier) and determine whether this concentration changes with age. All cats between the age of 1 and 9 years (n=141,294) had the same expected distribution of TT4 concentration (0.5-3.5ug/dL), and cats with a TT4 value >3.5ug/dL were determined to be unexpected outliers. There was a steep and progressive rise in both the total number and percentage of statistical outliers in the feline population as a function of age. The greatest acceleration in the percentage of outliers occurred between the age of 7 and 14 years, which was up to 4.6 times the rate seen between the age of 3 and 7 years. Conclusions: TT4 concentrations >3.5ug/dL represent outliers from the expected distribution of TT4 concentration. Furthermore, age has a strong influence on the proportion of cats. These findings suggest that patients with TT4 concentrations >3.5ug/dL should be more closely evaluated for hyperthyroidism, particularly between the ages of 7 and 14 years. This finding may aid clinicians in earlier identification of hyperthyroidism in at-risk patients

Detecting pulmonary Coccidioidomycosis with deep convolutional neural networks

Coccidioidomycosis is the most common systemic mycosis in dogs in the southwestern United States. With warming climates, affected areas and number of cases are expected to increase in the coming years, escalating also the chances of transmission to humans. As a result, developing methods for automating the detection of the disease is important, as this will help doctors and veterinarians more easily identify and diagnose positive cases. We apply machine learning models to provide accurate and interpretable predictions of Coccidioidomycosis. We assemble a set of radiographic images and use it to train and test state-of-the-art convolutional neural networks to detect Coccidioidomycosis. These methods are relatively inexpensive to train and very fast at inference time. We demonstrate the successful application of this approach to detect the disease with an Area Under the Curve (AUC) above 0.99 using 10-fold cross-validation. We also use the classification model to identify regions of interest and localize the disease in the radiographic images, as illustrated through visual heatmaps. This proof-of-concept study establishes the feasibility of very accurate and rapid automated detection of Valley Fever in radiographic images