Diagnosis and Staging of Equine Neoplasia.

The diagnosis of neoplasia in the horse is both simple and extremely challenging, depending on the type of neoplasm and its location. Obtaining an accurate diagnosis of a neoplastic condition is key to formulating an appropriate treatment plan if possible or developing a palliative plan if curative treatment options do not exist. A combination of historical features, clinical examination findings, and diagnostic testing typically allow a working diagnosis of neoplasia to be made, with a definitive diagnosis requiring the identification of neoplastic cells in a sample or tissue.

Paraneoplastic Syndromes in Horses.

This article discusses the reported paraneoplastic syndromes (PNSs) in horses, including the possible pathogenesis, diagnostic methods, and any treatment options. The more commonly reported PNSs in horses include cancer anorexia and cachexia, fever and increased acute phase protein concentrations, and hypercalcemia and monoclonal gammopathy. As these conditions can often be more commonly diagnosed in non-neoplastic conditions, the diagnosis of a PNS and the accompanying neoplasia can be challenging.

Quantitative Myocardial Perfusion Predicts Outcomes in Patients With Prior Surgical Revascularization.

Background: Patients with previous coronary artery bypass graft (CABG) surgery typically have complex coronary disease and remain at high risk of adverse events. Quantitative myocardial perfusion indices predict outcomes in native vessel disease, but their prognostic performance in patients with prior CABG is unknown.

Objectives: In this study, we sought to evaluate whether global stress myocardial blood flow (MBF) and perfusion reserve (MPR) derived from perfusion mapping cardiac magnetic resonance (CMR) independently predict adverse outcomes in patients with prior CABG.

The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers.

Background: Artificial intelligence (AI) technologies are increasingly used in clinical practice. Although there is robust evidence that AI innovations can improve patient care, reduce clinicians' workload and increase efficiency, their impact on medical training and education remains unclear.

Methods: A survey of trainee doctors' perceived impact of AI technologies on clinical training and education was conducted at UK NHS postgraduate centers in London between October and December 2020.