Cardiovascular disease and PPARdelta: Targeting the risk factors

Metabolism, in part, is regulated by the peroxisome proliferator-activated receptors (PPARs). The PPARs act as nutritional lipid sensors and three mammalian PPAR subtypes designated PPARalpha (NR1C1), PPARgamma (NR1C3) and PPARdelta (NR1C2) have been identified. This subgroup of nuclear hormone receptors binds DNA and controls gene expression at the nexus of pathways that regulate lipid and glucose homeostasis, energy storage and expenditure in an organ-specific manner. Recent evidence has demonstrated activation of PPARdelta in the major mass peripheral tissue (ie, adipose and skeletal muscle). It enhances glucose tolerance, insulin-stimulated glucose disposal, lipid catabolism, energy expenditure, cholesterol efflux and oxygen consumption. These effects positively influence the blood-lipid profile. Furthermore, PPARdelta activation produces a predominant type I/slow twitch/oxidative muscle fiber phenotype that leads to increased endurance, insulin sensitivity and resistance to obesity. PPARdelta has rapidly emerged as a potential target in the battle against dyslipidemia, insulin insensitivity, type II diabetes and obesity, with therapeutic efficacy in the treatment of cardiovascular disease risk factors. GW-501516 is currently undergoing phase II safety and efficacy trials in human volunteers for the treatment of dyslipidemia. The outcome of these clinical trials are eagerly awaited against a background of conflicting reports about cancer risks in genetically predisposed animal models. This review focuses on the potential pharmacological utility of selective PPARdelta agonists in the context of risk factors associated with metabolic and cardiovascular disease.

Distinct roles for interleukin-12p40 and tumour necrosis factor in resistance to oral candidiasis defined by gene-targeting

Cell-mediated immunity is important for anti-Candida host defence in mucosal tissues. In this study we used cytokine-specific gene knockout mice to investigate the requirement for T helper type 1 (Th1) and Th2 cytokines in recovery from oral candidiasis. Knockout mice used in this study included interleukin-4 (IL-4), IL-10, IL-12p40, interferon-gamma (IFN-gamma), and tumour necrosis factor (TNF). The mice were challenged either orally or systemically with Candida albicans yeasts, and levels of colonization were determined. IL-12p40 knockout mice developed chronic oropharyngeal candidiasis, but were not more susceptible to systemic challenge. On the other hand, TNF knockout mice displayed increased susceptibility to both oral and systemic challenge, but only in the acute stages of infection. TNF apparently has a protective effect in the acute stages of both oral and systemic candidiasis, whereas IL-12p40 is essential for recovery from oral but not systemic candidiasis. The role of IL-12p40, and its relation to T-cell-mediated responses remain to be determined.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

A new instrument for targeting falls prevention interventions was accurate and clinically applicable in a hospital setting

Background and Objective: To describe the diagnostic accuracy and practical application of the Peter James Centre Falls Risk Assessment Tool (PJC-FRAT), a multidisciplinary falls risk screening and intervention deployment instrument. Methods: In phase 1, the accuracy of the PJC-FRAT was prospectively compared to a gold standard (the STRATIFY) on a cohort of subacute hospital patients (n = 122). In phase 2, the PJC-FRAT was temporally reassessed using a subsequent cohort (n = 316), with results compared to those of phase 1. Primary outcomes were falls (events), fallers (patients who fell), and hospital completion rates of the PJC-FRAT. Results: In phase 1, PJC-FRAT accuracy of identifying falters showed sensitivity of 73% (bootstrap 95% confidence interval CI = 55, 90) and specificity of 75% (95% CI = 66, 83), compared with the STRATIFY (cutoff >= 2/5) sensitivity of 77% (95% CI = 59, 92) and specificity of 51% (95% CI = 41, 61). This difference was not significant. In phase 2, accuracy of nursing staff using the PJC-FRAT was lower. PJC-FRAT completion rates varied among disciplines over both phases: nurses and physiotherapists, >= 90%; occupational therapists, >= 82%; and medical officers, >= 57%. Conclusion: The PJC-FRAT was practical and relatively accurate as a predictor of falls and a deployment instrument for falls prevention interventions, although continued staff education may be necessary to maintain its accuracy. (c) 2006 Elsevier Inc. All rights reserved.