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Abnormal glycosaminoglycan metabolism is involved in the onset of anatomo-functional derangements in diabetic nephropathy, and determines the loss of glomerular basement membrane anionic charges leading to albuminuria. Glycosaminoglycan administration was shown to increase the negative electrical potential of the vessel wall, inhibit mesangial cell proliferation, which is an anatomical hallmark of diabetic nephropathy, and slow down the progression to uremia in subtotally nephrectomized rats, a model that shares some pathogenetic key events with diabetic nephropathy. Based on these considerations, we verified the effect of exogenous glycosaminoglycans on renal involvement in streptozotocin diabetic rats. Long-term administration of two glycosaminoglycans (low-molecular weight heparin and dermatan sulphate) prevented glomerular basement membrane thickening, glomerular anionic charge reduction, as well as the onset of albuminuria without affecting glomerular filtration rate and metabolic control of the disease. Our data demonstrate that the long-term administration of glycosaminoglycans has a favorable effect on morphological and functional renal abnormalities in diabetic rats.

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions.