Ghrelin-induced orexigenic effect in rats depends on the metabolic status and is counteracted by peripheral CB1 receptor antagonism.

Ghrelin is an endogenous regulator of energy homeostasis synthesized by the stomach to stimulate appetite and positive energy balance. Similarly, the endocannabinoid system is part of our internal machinery controlling food intake and energy expenditure. Both peripheral and central mechanisms regulate CB1-mediated control of food intake and a functional relationship between hypothalamic ghrelin and cannabinoid CB1 receptor has been proposed. First of all, we investigated brain ghrelin actions on food intake in rats with different metabolic status (negative or equilibrate energy balance). Secondly, we tested a sub-anxiogenic ultra-low dose of the CB1 antagonist SR141716A (Rimonabant) and the peripheral-acting CB1 antagonist LH-21 on ghrelin orexigenic actions. We found that: 1) central administration of ghrelin promotes food intake in free feeding animals but not in 24 h food-deprived or chronically food-restricted animals; 2) an ultra-low dose of SR141716A (a subthreshold dose 75 folds lower than the EC50 for induction of anxiety) completely counteracts the orexigenic actions of central ghrelin in free feeding animals; 3) the peripheral-restricted CB1 antagonist LH-21 blocks ghrelin-induced hyperphagia in free feeding animals. Our study highlights the importance of the animaĺs metabolic status for the effectiveness of ghrelin in promoting feeding, and suggests that the peripheral endocannabinoid system may interact with ghrelińs signal in the control of food intake under equilibrate energy balance conditions.

Impact of dietary betaine and conjugated linoleic acid on insulin sensitivity, protein and fat metabolism of obese pigs.

To determine possible mechanisms of action that might explain the nutrient partitioning effect of betaine and conjugated linoleic acid (CLA) in Iberian pigs and to address potential adverse effects, twenty gilts were restrictively fed from 20 to 50 kg BW Control, 0.5% betaine, 1% CLA or 0.5% betaine + 1% CLA diets. Serum hormones and metabolites profile were determined at 30 kg BW and an oral glucose test was performed before slaughter. Pigs were slaughtered at 50 kg BW and livers were obtained for chemical and histological analysis. Decreased serum urea in pigs fed betaine and betaine + CLA diets (11%; P = 0.0001) indicated a more efficient N utilization. The increase in serum triacylglycerol (58% and 28%, respectively; P = 0.0098) indicated that CLA and betaine + CLA could have reduced adipose tissue triacylglycerol synthesis from preformed fatty acids. Serum glucose, low-density lipoprotein (LDL) cholesterol and non-esterified fatty acids were unaffected. CLA and betaine + CLA altered serum lipids profile, although liver of pigs fed CLA diet presented no histopathological changes and triglyceride content was not different from Control pigs. Compared with controls, serum growth hormone decreased (20% to 23%; P = 0.0209) for all treatments. Although serum insulin increased in CLA, and especially in betaine + CLA pigs (28% and 83%; P = 0.0001), indices of insulin resistance were unaffected. In conclusion, CLA, and especially betaine + CLA, induced changes in biochemical parameters and hormones that may partially explain a nutrient partitioning effect in young pigs. Nevertheless, they exhibited weak, although detrimental, effects on blood lipids. Moreover, although livers were chemically and histologically normal, pigs fed CLA diet challenged with a glucose load had higher serum glucose than controls.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).