A comparative study of the preventative effects exerted by two probiotics, Lactobacillus reuteri and Lactobacillus fermentum, in the trinitrobenzenesulfonic acid model of rat colitis

The intestinal anti-inflammatory effects of two probiotics isolated from breast milk, Lactobacillus reuteri and L. fermentum, were evaluated and compared in the trinitrobenzenesulfonic acid (TNBS) model of rat colitis. Colitis was induced in rats by intracolonic administration of 10 mg TNBS dissolved in 50% ethanol (0.25 ml). Either L. reuteri or L. fermentum was daily administered orally (5 x 10(8) colony-forming units suspended in 0.5 ml skimmed milk) to each group of rats (n 10) for 3 weeks, starting 2 weeks before colitis induction. Colonic damage was evaluated histologically and biochemically, and the colonic luminal contents were used for bacterial studies and for SCFA production. Both probiotics showed intestinal anti-inflammatory effects in this model of experimental colitis, as evidenced histologically and by a significant reduction of colonic myeloperoxidase activity (P<0.05). L. fermentum significantly counteracted the colonic glutathione depletion induced by the inflammatory process. In addition, both probiotics lowered colonic TNFalpha levels (P<0.01) and inducible NO synthase expression when compared with non-treated rats; however, the decrease in colonic cyclo-oxygenase-2 expression was only achieved with L.fermentum administration. Finally, the two probiotics induced the growth of Lactobacilli species in comparison with control colitic rats, but the production of SCFA in colonic contents was only increased when L. fermentum was given. In conclusion, L. fermentum can exert beneficial immunomodulatory properties in inflammatory bowel disease, being more effective than L. reuteri, a probiotic with reputed efficacy in promoting beneficial effects on human health.

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).