Evidence of altered fatty acid metabolism in dogs with naturally occurring valvular heart disease and congestive heart failure.

Introduction: Myxomatous mitral valve disease (MMVD) is the most common cardiac condition in adult dogs. The disease progresses over several years and affected dogs may develop congestive heart failure (HF). Research has shown that myocardial metabolism is altered in cardiac disease, leading to a reduction in β-oxidation of fatty acids and an increased dependence upon glycolysis.

Accuracy of history, physical examination, cardiac biomarkers, and biochemical variables in identifying dogs with stage B2 degenerative mitral valve disease.

Background: Treatment is indicated in dogs with preclinical degenerative mitral valve disease (DMVD) and cardiomegaly (stage B2). This is best diagnosed using echocardiography; however, relying upon this limits access to accurate diagnosis.

Objectives: To evaluate whether cardiac biomarker concentrations can be used alongside other clinical data to identify stage B2 dogs.

The effect of myxomatous mitral valve disease severity on packed cell volume in dogs.

Objectives: The aim of this study was to examine whether associations between disease severity and packed cell volume exist in dogs with myxomatous mitral valve disease.

Materials And Methods: Data were selected from 289 dogs that had been examined at a research clinic (2004-2017) on multiple occasions (n=1465). American College of Veterinary Internal Medicine stage and echocardiographic measurements were entered in separate multivariable linear mixed effects models with packed cell volume as the dependent variable.

The gut microbiome in dogs with congestive heart failure: a pilot study.

Compromised gut health and dysbiosis in people with heart failure has received a great deal of attention over the last decade. Whether dogs with heart failure have a similar dysbiosis pattern to what is described in people is currently unknown. We hypothesised that dogs with congestive heart failure have quantifiable dysbiosis compared to healthy dogs that are similar in sex and age.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alternative to animal models for assessing drug toxicity, thus reducing expensive and invasive animal testing during clinical trials, for drugs that are most likely to fail safety tests. Here we present a machine learning model to predict kidney dysfunction, as a proxy for drug induced renal toxicity, in rats.