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).

Concentrations of resveratrol and derivatives in foods and estimation of dietary intake in a Spanish population: European Prospective Investigation into Cancer and Nutrition (EPIC)-Spain cohort

Resveratrol has been shown to have beneficial effects on diseases related to oxidant and/or inflammatory processes and extends the lifespan of simple organisms including rodents. The objective of the present study was to estimate the dietary intake of resveratrol and piceid (R&P) present in foods, and to identify the principal dietary sources of these compounds in the Spanish adult population. For this purpose, a food composition database (FCDB) of R&P in Spanish foods was compiled. The study included 40 685 subjects aged 35–64 years from northern and southern regions of Spain who were included in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Spain cohort. Usual food intake was assessed by personal interviews using a computerised version of a validated diet history method. An FCDB with 160 items was compiled. The estimated median and mean of R&P intake were 100 and 933 μg/d respectively. Approximately, 32 % of the population did not consume R&P. The most abundant of the four stilbenes studied was trans-piceid (53·6 %), followed by trans-resveratrol (20·9 %), cis-piceid (19·3 %) and cis-resveratrol (6·2 %). The most important source of R&P was wines (98·4 %) and grape and grape juices (1·6 %), whereas peanuts, pistachios and berries contributed to less than 0·01 %. For this reason the pattern of intake of R&P was similar to the wine pattern. This is the first time that R&P intake has been estimated in a Mediterranean country.

Estimation of the real population and its impact on the utilisation of healthcare services in Mediterranean resort regions: an ecological study.

BACKGROUND The demographic structure has a significant influence on the use of healthcare services, as does the size of the population denominators. Very few studies have been published on methods for estimating the real population such as tourist resorts. The lack of information about these problems means there is a corresponding lack of information about the behaviour of populational denominators (the floating population or tourist load) and the effect of this on the use of healthcare services. The objectives of the study were: a) To determine the Municipal Solid Waste (MSW) ratio, per person per day, among populations of known size; b) to estimate, by means of this ratio, the real population in an area where tourist numbers are very significant; and c) to determine the impact on the utilisation of hospital emergency healthcare services of the registered population, in comparison to the non-resident population, in two areas where tourist numbers are very significant. METHODS An ecological study design was employed. We analysed the Healthcare Districts of the Costa del Sol and the island of Menorca. Both are Spanish territories in the Mediterranean region. RESULTS In the two areas analysed, the correlation coefficient between the MSW ratio and admissions to hospital emergency departments exceeded 0.9, with p < 0.001. On the basis of MSW generation ratios, obtained for a control zone and also measured in neighbouring countries, we estimated the real population. For the summer months, when tourist activity is greatest and demand for emergency healthcare at hospitals is highest, this value was found to be double that of the registered population. CONCLUSION The MSW indicator, which is both ecological and indirect, can be used to estimate the real population in areas where population levels vary significantly during the year. This parameter is of interest in planning and dimensioning the provision of healthcare services.