Modulation of the Endocannabinoids N-Arachidonoylethanolamine (AEA) and 2-Arachidonoylglycerol (2-AG) on Executive Functions in Humans.

Animal studies point to an implication of the endocannabinoid system on executive functions. In humans, several studies have suggested an association between acute or chronic use of exogenous cannabinoids (Δ9-tetrahydrocannabinol) and executive impairments. However, to date, no published reports establish the relationship between endocannabinoids, as biomarkers of the cannabinoid neurotransmission system, and executive functioning in humans. The aim of the present study was to explore the association between circulating levels of plasma endocannabinoids N-arachidonoylethanolamine (AEA) and 2-Arachidonoylglycerol (2-AG) and executive functions (decision making, response inhibition and cognitive flexibility) in healthy subjects. One hundred and fifty seven subjects were included and assessed with the Wisconsin Card Sorting Test; Stroop Color and Word Test; and Iowa Gambling Task. All participants were female, aged between 18 and 60 years and spoke Spanish as their first language. Results showed a negative correlation between 2-AG and cognitive flexibility performance (r = -.37; p<.05). A positive correlation was found between AEA concentrations and both cognitive flexibility (r = .59; p<.05) and decision making performance (r = .23; P<.05). There was no significant correlation between either 2-AG (r = -.17) or AEA (r = -.08) concentrations and inhibition response. These results show, in humans, a relevant modulation of the endocannabinoid system on prefrontal-dependent cognitive functioning. The present study might have significant implications for the underlying executive alterations described in some psychiatric disorders currently associated with endocannabinoids deregulation (namely drug abuse/dependence, depression, obesity and eating disorders). Understanding the neurobiology of their dysexecutive profile might certainly contribute to the development of new treatments and pharmacological approaches.

Use of the internet as a source of health information by Spanish adolescents

The Internet is a fundamental part of the daily life of adolescents, they consider it as a safe and confidential source of information on health matters. The aims is to describe the experience of Spanish adolescents searching for health information on the Internet. Methods A cross-sectional study of 811 school-age adolescents in Granada was carried out. An adapted and piloted questionnaire was used which was controlled by trained personnel. Sociodemographic and health variables were included together with those concerning the conditions governing access to and use of information and communication technologies (ICT). Results 811 adolescents were surveyed (99.38% response rate), mean age was 17 years old. Of these, 88% used the Internet; 57.5% used it on a daily or weekly basis and 38.7% used it occasionally. Nearly half the sample group (55.7%) stated that they used the Internet to search for health-related information. The main problems reported in the search for e-health were the ignorance of good web pages (54.8%) and the lack of confidence or search skills (23.2%). Conclusions In conclusion, it seems plausible to claim that websites designed and managed by health services should have a predominant position among interventions specifically addressed to young people.

Comunicación paciente-médico y toma de decisiones en salud en Andalucía. Expectativas de pacientes crónicos del Sistema Sanitario Público Andaluz

The purpose of the study was to know about the expectations had by chronic patients within th e Public Healthcare System in Andalusi a, concerning commu nication and making deci sions with the med ical professionals who take care of them duri ng the entire treatment process. A qualitative study was carried out in Andalusia, based on 19 i n-depth interviews and 17 focus groups with 15 4 chronic patients and their relatives, suffering from seven different types of illness, of which the content was later analyzed based on the type of disease (pathologies), and the level of care (Primary, Sp eci aliz ed or Em ergen cy Ser vices). The study shows that patients i ncl uded in all healthcare processes want information about their illness. In Primary Care, they value continued attention, closeness and trust as well as being referred to a proper specialist. In Specialized Care, they expect to receive trustworthy information, proper tests and a correct diagnosis, friendly treatment and to be listened to. In the Emergency Room, lack of tim e and overcrowding d i sru pt proper communic ation. The type of chronic illne ss i nfluenc es patients’ expectations in terms of communication and decisionmaking. Communic ation between patients and medical personnel and the decision-making process related to chronic illness are crucial for the improvement of the qualit y o f healthcare. The type of chr onic disease marks diffe rent expectation s about the communication and decision-making. The communication and the decision making in the chronic assistance is a priority to improve the quality of the assistance

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).