Mast cells, TGF-beta 1 and alpha-SMA expression in IgA nephropathy

IgA nephropathy (IgAN) is a kidney disease with a varying renal prognosis. Recently, many studies have demonstrated that renal alpha-smooth muscle actin (alpha-SMA) and transforming growth factor (TGF-beta 1) expression, as well interstitial mast cell infiltrates could represent a prognostic marker in several renal diseases. The aim of our study was to analyze the prognostic value of mast cell, TGF-beta 1 and alpha-SMA expression in IgAN. A survey of the medical records and renal biopsy reports of 62 patients with a diagnosis of IgAN followed-up from 1987 to 2003 was performed. The mean follow-up time was 74.7 +/- 50.0 months. The immunohistochemical studies were performed using a monoclonal antibody anti-human mast cell tryptase, a polyclonal antibody anti-human TGF-beta 1, and a monoclonal antibody anti-human alpha-SMA. An unfavorable clinical course of IgAN was related to interstitial mast cell infiltrates and alpha-SMA expression in the tubulointerstitial area. Expression of glomerular TGF-beta 1 and alpha-SMA, and interstitial TGF-beta 1 is not correlated with clinical course in IgAN. In conclusion, the increased number of mast cells and higher alpha-SMA expression in the tubulointerstitial area may be predictive factors for the poor prognosis of patients with IgAN.

Peripheral Oxidative Stress Biomarkers in Mild Cognitive Impairment and Alzheimer`s Disease

Oxidative stress has been associated with normal aging and Alzheimer`s disease (AD). However, little is known about oxidative stress in mild cognitive impairment (MCI) patients who present a high risk for developing AD. The aim of this study was to investigate plasma production of the lipid peroxidation marker, malonaldehyde (MDA) and to determine, in erythrocytes, the enzymatic antioxidant activity of catalase, glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione S-transferase (GST) in 33 individuals with MCI, 29 with mild probable AD and 26 healthy aged subjects. GR/GPx activity ratio was calculated to better assess antioxidant defenses. The relationship between oxidative stress and cognitive performance was also evaluated by the Mini Mental State Examination (MMSE). AD patients showed higher MDA levels than both MCI and healthy elderly subjects. MCI subjects also exhibited higher MDA levels compared to controls. Catalase and GPx activity were similar in MCI and healthy individuals but higher in AD. GR activity was lower in MCI and AD patients than in healthy aged subjects. Additionally, GR/GPx ratio was higher in healthy aged subjects, intermediate in MCI and lower in AD patients. No differences in GST activity were detected among the groups. MMSE was negatively associated with MDA levels (r = -0.31, p = 0.028) and positively correlated with GR/GPx ratio in AD patients (r = 0.68, p < 0.001). MDA levels were also negatively correlated to GR/GPx ratio (r = -0.31, p = 0.029) in the AD group. These results suggest that high lipid peroxidation and decreased antioxidant defenses may be present early in cognitive disorders.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Quality indices for (practical) clustering evaluation

Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters` compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.