Prevalence and Clinical Features of Isolated Clinical Hypertension and Dipping Profile in a Large Cohort of Untreated Hypertensive Subjects

Aim: To examine the prevalence and clinical features of isolated clinical hypertension (ICH) and the dipping patterns in a large cohort of untreated hypertensive subjects form the Spanish ABPM registry

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).

Consensus document on allergic conjunctivitis (DECA).

Allergic conjunctivitis (AC) is an inflammatory disease of the conjunctiva caused mainly by an IgE-mediated mechanism. It is the most common type of ocular allergy. Despite being the most benign form of conjunctivitis, AC has a considerable effect on patient quality of life, reduces work productivity, and increases health care costs. No consensus has been reached on its classification, diagnosis, or treatment. Consequently, the literature provides little information on its natural history, epidemiological data are scarce, and it is often difficult to ascertain its true morbidity. The main objective of the Consensus Document on Allergic Conjunctivitis (Documento dE Consenso sobre Conjuntivitis Alérgica [DECA]), which was drafted by an expert panel from the Spanish Society of Allergology and Spanish Society of Ophthalmology, was to reach agreement on basic criteria that could prove useful for both specialists and primary care physicians and facilitate the diagnosis, classification, and treatment of AC. This document is the first of its kind to describe and analyze aspects of AC that could make it possible to control symptoms.