Novel selective β1-adrenoceptor antagonists for concomitant cardiovascular and respiratory disease

β-Blockers reduce mortality and improve symptoms in people with heart disease. However, current clinically available β-blockers have poor selectivity for the cardiac β1-adrenoceptor (AR) over the lung β2-AR. Unwanted β2-blockade risks causing life-threatening bronchospasm and a reduction in the efficacy of β2-agonist emergency rescue therapy. Thus current life-prolonging β-blockers are contraindicated in people with both heart disease and asthma. Here we describe NDD-713 and NDD-825, novel highly β1-selective neutral antagonists with good pharmaceutical properties that can potentially overcome this limitation. Radioligand binding studies and functional assays using human receptors expressed in CHO cells demonstrate that NDD-713 and NDD-825 have nanomolar β1-AR affinity, greater than 500-fold β1-AR vs β2-AR selectivity and no agonism. Studies in conscious rats demonstrated that they are orally bioavailable and cause pronounced β1-mediated reduction of heart rate while showing no effect on β2-mediated hindquarters vasodilatation. The compounds also have good disposition properties and show no adverse toxicological effects. They potentially offer a truly cardioselective β-blocker therapy for the large number of people with heart and respiratory, or peripheral vascular comorbidities

Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder

This is the published version. Copyright De GruyterAmblyopia, or lazy eye, is a neurological vision disorder that studies have shown to affect two to five percent of the population. Current methods of treatment produce the best visual outcome, if the condition is identified early in the patient's life. Several early screening procedures are aimed at finding the condition while the patient is a child, including an automated vision screening system. This paper aims to use artificial intelligence techniques to automatically identify children who are at risk for developing the amblyopic condition and should therefore be referred to a specialist, i.e., pediatric ophthalmologist. Three techniques, namely, decision tree learning, random forest, and artificial neural network, are studied in this paper in terms of their effectiveness, using metrics of sensitivity, specificity, and accuracy. The features used by the techniques are extracted from images of patient eyes and are based on the color information. The efficacy of pixel color data is investigated with respect to the measurement of the rate of change of the color in the iris and pupil, i.e., color slope features. A 10-fold stratified cross validation procedure is used to compare the effectiveness of the three AI techniques in this medical application domain