Social inequalities and mortality in Europe-results from a large multi-national cohort

BACKGROUND Socio-economic inequalities in mortality are observed at the country level in both North America and Europe. The purpose of this work is to investigate the contribution of specific risk factors to social inequalities in cause-specific mortality using a large multi-country cohort of Europeans. METHODS A total of 3,456,689 person/years follow-up of the European Prospective Investigation into Cancer and Nutrition (EPIC) was analysed. Educational level of subjects coming from 9 European countries was recorded as proxy for socio-economic status (SES). Cox proportional hazard model's with a step-wise inclusion of explanatory variables were used to explore the association between SES and mortality; a Relative Index of Inequality (RII) was calculated as measure of relative inequality. RESULTS Total mortality among men with the highest education level is reduced by 43% compared to men with the lowest (HR 0.57, 95% C.I. 0.52-0.61); among women by 29% (HR 0.71, 95% C.I. 0.64-0.78). The risk reduction was attenuated by 7% in men and 3% in women by the introduction of smoking and to a lesser extent (2% in men and 3% in women) by introducing body mass index and additional explanatory variables (alcohol consumption, leisure physical activity, fruit and vegetable intake) (3% in men and 5% in women). Social inequalities were highly statistically significant for all causes of death examined in men. In women, social inequalities were less strong, but statistically significant for all causes of death except for cancer-related mortality and injuries. DISCUSSION In this European study, substantial social inequalities in mortality among European men and women which cannot be fully explained away by accounting for known common risk factors for chronic diseases are reported.

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).

Safety and immunomodulatory effects of three probiotic strains isolated from the feces of breast-fed infants in healthy adults: SETOPROB study.

We previously described the isolation and characterization of three probiotic strains from the feces of exclusively breast-fed newborn infants: Lactobacillus paracasei CNCM I-4034, Bifidobacterium breve CNCM I-4035 and Lactobacillus rhamnosus CNCM I-4036. These strains were shown to adhere to intestinal mucus in vitro, to be sensitive to antibiotics and to resist biliary salts and low pH. In the present study, a multicenter, randomized, double-blind, placebo-controlled trial with 100 healthy volunteers in three Spanish cities was carried out to evaluate the tolerance, safety, gut colonization and immunomodulatory effects of these three probiotics. Volunteers underwent a 15-day washout period, after which they were randomly divided into 5 groups that received daily a placebo, a capsule containing one of the 3 strains or a capsule containing a mixture of two strains for 30 days. The intervention was followed by another 15-day washout period. Patients did not consume fermented milk for the entire duration of the study. Gastrointestinal symptoms, defecation frequency and stool consistency were not altered by probiotic intake. No relevant changes in blood and serum, as well as no adverse events occurred during or after treatment. Probiotic administration slightly modified bacterial populations in the volunteers' feces. Intestinal persistence occurred in volunteers who received L. rhamnosus CNCM I-4036. Administration of B. breve CNCM I-4035 resulted in a significant increase in fecal secretory IgA content. IL-4 and IL-10 increased, whereas IL-12 decreased in the serum of volunteers treated with any of the three strains. These results demonstrate that the consumption of these three bacterial strains was safe and exerted varying degrees of immunomodulatory effects.