Nerve pathology in the type 1 diabetic dog: effects of treatment with sulindac

The purpose of this study was to define pathological abnormalities in the peripheral nerve of a large animal model of long-duration type 1 diabetes and also to determine the effects of treatment with sulindac. Detailed morphometric studies were performed to define nerve fiber and endoneurial capillary pathology in 6 control dogs, 6 type 1 diabetic dogs treated with insulin, and 6 type 1 diabetic dogs treated with insulin and sulindac for 4 years. Myelinated fiber and regenerative cluster density showed a non-significant trend toward a reduction in diabetic compared to control animals, which was prevented by treatment with sulindac. Unmyelinated fiber density did not differ among groups. However, diabetic animals showed a non-significant trend toward an increase in axon diameter (p <0.07), with a shift of the size frequency distribution towards larger axons, which was not prevented by treatment with sulindac. Endoneurial capillary density and luminal area showed a non-significant trend toward an increase in diabetic animals, which was prevented with sulindac treatment. Endoneurial capillary basement membrane area was significantly increased (p <0.05) in diabetic animals, but was not prevented with sulindac treatment. We conclude that the type 1 diabetic dog demonstrates minor structural abnormalities in the nerve fibers and endoneurial capillaries of the sciatic nerve, and treatment with sulindac ameliorates some but not all of these abnormalities.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.