PubMed · 16130683
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The paradigm that chronic rejection causes all progressive late allograft failure has been replaced by a hypothesis of cumulative damage, where a series of time-dependent immune and nonimmune mechanisms injure the kidney and lead to chronic interstitial fibrosis and tubular atrophy, representing a final common pathway of injury and its consequent fibrotic healing response. Allograft damage is common, progressive, time-dependent, clinically important and modified by immunosuppression. Early after transplantation, tubulointerstitial damage is predominantly related to ischemia reperfusion injury, acute tubular necrosis, acute and subclinical rejection and/or calcineurin inhibitor nephrotoxicity, superimposed on preexisting donor disease. Later, cellular inflammation lessens and is replaced by microvascular and glomerular injury from calcineurin inhibitor nephrotoxicity, hypertension, immune-mediated fibrointimal vascular hyperplasia, transplant glomerulopathy and capillary injury, polyoma virus and/or recurrent glomerulonephritis. Additional mechanisms of injury include internal architectural disruption of the kidney, cortical ischemia, persistent chronic inflammation, replicative senescence, cytokine excess and fibrosis induced by epithelial-to-mesenchymal transition. Current understanding of the etiology, pathophysiology and evolution of pathological changes are detailed. An approach to histological assessment of the individual failing graft are presented and a series of postulates are defined for future studies of chronic allograft nephropathy.
Magnetic resonance imaging (MRI) offers the ability to non-invasively assess parenchymal and vascular renal disease. Indications for renal MRI include the evaluation of renal masses, urinary obstruction and infection, renal vasculature, and the health of transplant kidneys. The potential of MR angiography to replace invasive conventional x-ray angiography has been recognized for many years. Recent developments in MRI resulting from fast MR systems with faster gradients, new surface coil designs and the latest sequence developments coupled with innovative contrast agent administration strategies have prompted substantial progress of MRI in the diagnosis of renal disease. The goal of this article is to present the current state of MRI in diagnosing renal disease, with an emphasis on the latest developments in the evaluation of renal vascular disease.
Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.