University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Colonization and infection with methicillin-resistant Staphylococcus aureus: associated factors and eradication.

Abstract: Objectives To identify characteristics associated with methicillin-resistant Staphylococcus aureus (MRSA) colonization and infection, and to evaluate the efficacy of systemic and topical antimicrobials in the eradication of MRSA carriage among hospitalized patients. Design A case-control study was done to identify associations. Odds ratios were estimated by unconditional multiple logistic regression. Cohort study was done to evaluate MRSA decolonization efficacy by an oral regimen. Patients infected or colonized with MRSA received a 5-day course of oral (160 mg/800 mg) trimethoprim-sulfametroxazole twice daily and 600 mg of rifampin once daily as decolonization treatment. The proportion of MRSA-free patients after decolonization treatment was determined. Persistence of clearing was estimated by the Kaplan-Meier method. Setting Ramon y Cajal Hospital, a 1,249-bed, tertiary-care teaching hospital in Madrid, Spain. Patients One hundred ninety-two patients with hospital-acquired MRSA infection/colonization and 195 MRSA-free random controls. Results Six factors were associated independently with MRSA infection/colonization: age (every 10 years of age, odds ratio [OR] = 1.3); ward (surgical, OR = 1; medical, OR = 3.1; intensive care unit, OR = 60); previous hospitalization (OR = 6.9); coma (OR = 25.3); invasive procedures (each, OR = 1.7); 3 or more weeks of hospitalization (OR = 3.8). We failed to show antibiotic therapy to be an independent risk factor for MRSA hospital infection/colonization. Overall, MRSA eradication was 64.2% by day 2 to 9 after completion of treatment. Kaplan-Meier product limit survival analysis showed that the probability of remaining MRSA-free was 65.3% (SE = 0.09) at 32 days after completion of treatment. Conclusions The results offer a rationale for reduction of MRSA infection/colonization in the hospital by interventions aimed at early identification of patients at higher risk, at prompt discharge of patients, and at preventing dissemination while performing invasive procedures. They also provide estimates of antibiotic treatment efficacy to reduce the reservoir of MRSA in the hospital.

Magnetocatalytic Graphene Quantum Dots Janus Micromotors for Bacterial Endotoxin Detection.

Abstract: Magnetocatalytic hybrid Janus micromotors encapsulating phenylboronic acid (PABA) modified graphene quantum dots (GQDs) are described herein as ultrafast sensors for the detection of deadly bacteria endotoxins. A bottom-up approach was adopted to synthesize an oil-in-water emulsion containing the GQDs along with a high loading of platinum and iron oxide nanoparticles on one side of the Janus micromotor body. The two different "active regions" enable highly efficient propulsion in the presence of hydrogen peroxide or magnetic actuation without the addition of a chemical fuel. Fluorescence quenching was observed upon the interaction of GQDs with the target endotoxin (LPS), whereby the PABA tags acted as highly specific recognition receptors of the LPS core polysaccharide region. Such adaptive hybrid operation and highly specific detection hold considerable promise for diverse clinical, agrofood, and biological applications and integration in future lab-on-chip technology.

Combination of Feature Extraction Methods for SVM Pedestrian Detection

Abstract: This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in Intelligent Transportation Systems. The basic components of pedestrians are first located in the image and then combined with a support-vector-machine-based classifier. This poses the problem of pedestrian detection in real cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A components-based learning approach is proposed in order to better deal with pedestrian variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes at either daytime or nighttime. The results achieved to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance