Guidelines for specialized nutritional and metabolic support in the critically-ill patient. Update. Consensus SEMICYUC-SENPE: Septic patient

Nutritional metabolic management, together with other treatment and support measures used, is one of the mainstays of the treatment of septic patients. Nutritional support should be started early, after initial life support measures, to avoid the consequences of malnutrition, to provide adequate nutritional intake and to prevent the development of secondary complications such as superinfection or multiorgan failure. As in other critically-ill patients, when the enteral route cannot be used to ensure calorie-protein requirements, the association of parenteral nutrition has been shown to be safe in this subgroup of patients. Studies evaluating the effect of specific pharmaconutrients in septic patients are scarce and are insufficient to allow recommendations to be made. To date, enteral diets with a mixture of substrates with distinct pharmaconutrient properties do not seem to be superior to standard diets in altering the course of sepsis, although equally there is no evidence that these diets are harmful. There is insufficient evidence to recommend the use of glutamine in septic patients receiving parenteral nutrition. However, given the good results and absence of glutamine-related adverse effects in the various studies performed in the general population of critically-ill patients, these patients could benefit from the use of this substance. Routine use of omega-3 fatty acids cannot be recommended until further evidence has been gathered, although the use of lipid emulsions with a high omega-6 fatty acid content should be avoided. Septic patients should receive an adequate supply of essential trace elements and vitamins. Further studies are required before the use of high-dose selenium can be recommended.

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).