Enhanced concentrations of relevant markers of nitric oxide formation after exercise training in patients with metabolic syndrome

Metabolic syndrome (MetS) denotes a clustering of risk factors that may affect nitric oxide (NO) bioavailability and predispose to cardiovascular diseases, which are delayed by exercise training. However, no previous study has examined how MetS affects markers of NO formation, and whether exercise training increases NO formation in MetS patients. Here, we tested these two hypotheses. We studied 48 sedentary individuals: 20 healthy controls and 28 MetS patients. Eighteen MetS patients were subjected to a 3-month exercise training (E+group), while the remaining 10 MetS patients remained sedentary (E-group). The plasma concentrations of nitrite, cGMP, and ADMA (asymmetrical dimethylarginine: an endogenous nitric oxide synthase inhibitor), and the whole blood nitrite concentrations were determined at baseline and after exercise training using an ozone-based chemiluminescence assay, and commercial enzyme immunoassays. Thiobarbituric acid reactive species (TBA-RS) were measured in the plasma to assess oxidative stress using a fluorometric method. We found that, compared with healthy subjects, patients with MetS have lower concentrations of markers of NO formation, including whole blood nitrite, plasma nitrite, and plasma cGMP, and increased oxidative stress (all P < 0.05). Exercise training increased the concentrations of whole blood nitrite and cGMP, and decreased both oxidative stress and the circulating concentrations of ADMA (both P < 0.05). These findings show clinical evidence for lower endogenous NO formation in patients with MetS, and for improvements in NO formation associated with exercise training in MetS patients. (C) 2008 Elsevier Inc. All rights reserved.

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds. (C) 2010 Elsevier Masson SAS. All rights reserved.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.