A Change in the Epidemiology of Infections Due to Extended-Spectrum b-Lactamase–Producing Organisms

Extended-spectrum β-lactamases (ESBLs) form a heterogeneous group that share the property of hydrolytic activity against the oxyimino-β-lactams while remaining susceptible to inhibition by β-lactamase inhibitors, such as clavulanic acid. From a clinical point of view, they are important because they confer resistance to penicillins, aztreonam, and cephalosporins, and ESBL-producing organisms are typically also resistant to aminoglycosides, trimethoprim-sulfamethoxazole, and quinolones [1]. Until recently, the main problem posed by ESBLs was related to nosocomial outbreaks caused by ESBL-producing Klebsiella species. These outbreaks are usually clonal, the strains are mainly spread through cross-transmission, and the risk factors are similar to those found for other multidrug-resistant nosocomial pathogens [2]. In Europe and the United States, most ESBL-producing Klebsiella isolates harbored enzymes belonging to the TEM and SHV families [3]. Detection of colonized patients by performing surveillance cultures within affected units, isolation precautions for colonized patients, and restriction of oxyimino-β-lactam use are frequently useful for the control of these outbreaks [1]. There is no evidence that hospital-acquired ESBL-producing klebsiellae are decreasing in importance—in fact, data from the Centers for Disease Control and Prevention show that 20.6% of Klebsiella pneumoniae isolates from United States intensive care units in 2003 were probable producers of ESBL [4]. This represented a 47% increase, compared with the preceding 5 years. However, during the last few years, an impressive increase in the number of ESBL-producing Escherichia coli (and, less frequently, other Enterobacteriaceae) is being described in several parts of the world [5–8]. This emergent phenomenon shows some differences from the problem posed by Klebsiella species; many of these ESBL-producing E. coli are isolated …

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).