Dissemination and spread of New Delhi metallo-beta-lactamase-1 superbugs in hospital settings

Objective: To find out frequency of isolation of carbapenem-resistant enterobacteriaceae and the predominantly responsible metallo-beta-lactamasegene in a hospital setting. Methods: The descriptive, cross-sectional study was conducted from May 2009 to June 2012 at the Aga Khan University Hospital, Karachi, and comprised non-duplicate clinical carbapenem-resistant enterobacteriaceae isolates obtained from different collection units across Pakistan. Kirby-Bauer disk diffusion screening of carbapenem-resistant enterobacteriaceae was confirmed by minimum inhibitory concentration using E-test. Polymerase chain reaction assay was performed to detect blaKPC, blaNDM-1, blaIMP, and blaVIM genes. In addition variable number tandem repeat typing was performed on selected cluster of New Delhi metallo-beta-lactamase-1- positive Klebsiella pneumoniae. Results: Of the 114 carbapenem-resistant enterobacteriaceae isolates, 104(94%) tested positive for blaNDM-1 gene. At 68(66%), Klebsiella pneumoniae was the most frequent species isolated, followed by E.coli 33(31%). Moreover, 89(78%) of the blaNDM-1 gene positive Klebsiella pneumonia isolates were from the clinical samples of patients admitted to the critical care units and 75(66%) were from neonates and the elderly. Of the 65(67%) patientssuffering from bacteraemia and sepsis, 32(57%) had expired, of which 22(60%) were aged \u3c1 month. Variable number tandem repeat analysis of hospital-acquired New Delhi metallo-beta-lactamase-1-positive Klebsiella pneumoniae showed similarities between the isolates. Conclusion:New Delhi metallo-beta-lactamase-1-positive enterobacteriaceae was found widely disseminated in major hospitals across Pakistan. Patients at extreme ages and those in critical care units were found to be the most affected with fatal outcome

Mixed salmonella infection: a case series from Pakistan

Enteric fever remains a major health problem in the developing world, including Pakistan. Poor sanitation and hygienic conditions are the major predisposing factors. Salmonella infection with different strains in the same patient has rarely been reported previously. We are reporting two cases of bacteraemia with simultaneous detection of two strains of Salmonella in a single episode of infection. In both the cases, 2 different serotypes of Salmonella were causing bacteraemia leading to fever. In highly endemic area, one must be aware of mixed Salmonella infections as inappropriate diagnosis of such infections may lead to treatment failure

Iron, copper and silver nanoparticles: green synthesis using green and black tea leaves extracts and evaluation of antibacterial, antifungal and aflatoxin B1 adsorption activity

peer-reviewedThe present study was aimed to account an eco-friendly synthesis of iron (Fe), copper (Cu) and silver (Ag) nanoparticles (NPs) using green tea and black tea leaves extracts. Synthesized NPs were characterized using SEM, FTIR, EDX and UV/Vis spectroscopy techniques. Antibacterial activity of NPs was assessed against methicillin- and vancomycin-resistance Staphylococcus aureus strains. Antifungal activity was investigated against Aspergillus flavus and A. parasiticus fungal species. Adsorbent capability with aflatoxin B1 (AFB1) was also assessed in solution. Ag-NPs showed superior antibacterial/antifungal activities and reduced the aflatoxins production in comparison to Fe-NPs and Cu-NPs. Adsorption capability of all NPs with AFB1 contamination was found in the order of Fe-NPs > Cu-NPs > Ag-NPs. The equilibrium data showed the favorability of Langmuir isotherm with the adsorption capacity (131–139 ng/mg), Cu-NPs (114–118 ng/mg) and Ag-NPs (110–115 ng/mg). Thermodynamic parameters and kinetic studies revealed that adsorption process is spontaneous, endothermic and followed the pseudo-second order. These results suggest that the synthesized NPs could be effectively utilize as an alternative antibacterial/antifungal agent against diseases caused by multiple drug resistant pathogens. In addition, these metal NPs may be utilize as a possible aflatoxins adsorbent in human food and animal feed such as rice, wheat, maize, red chillies and poultry feed

Depth-of-field-based alpha-matte extraction

In compositing applications, objects depicted in images frequently have to be separated from their background, so that they can be placed in a new environment. Alpha mattes are important tools aiding the selection of objects, but cannot normally be created in a fully automatic way. We present an algorithm that requires as input two images—one where the object is in focus, and one where the background is in focus—and then automatically produces an alpha matte indicating which pixels belong to the object. This algorithm is inspired by human visual processing and involves nonlinear response compression, center-surround mechanisms as well as a filling-in stage. The output can then be refined with standard computer vision techniques

GHOST REMOVAL IN HIGH DYNAMIC RANGE IMAGES

High dynamic range images may be created by capturing multiple images of a scene with varying exposures. Images created in this manner are prone to ghosting artifacts, which appear if there is movement in the scene at the time of capture. This paper describes a novel approach to removing ghosting artifacts from high dynamic range images, without the need for explicit object detection and motion estimation. Weights are computed iteratively and then applied to pixels to determine their contribution to the final image. We use a non-parametric model for the static part of the scene, and a pixel’s membership in this model determines its weight. In contrast to previous approaches, our technique does not rely on explicit object detection, tracking, or pixelwise motion estimates. Ghost-free images of different scenes demonstrate the effectiveness of our technique. Index Terms — Image generation, Pattern recognition 1

Mode-Seeking By Medoidshifts

We present a nonparametric mode-seeking algorithm, called medoidshift, based on approximating the local gradient using a weighted estimate of medoids. Like meanshift, medoidshift clustering automatically computes the number of clusters and the data does not have to be linearly separable. Unlike meanshift, the proposed algorithm does not require the definition of a mean. This property allows medoidshift to find modes even when only a distance measure between samples is defined. In this sense, the relationship between the medoidshift algorithm and the meanshift algorithm is similar to the relationship between the k-medoids and the k-means algorithms. We show that medoidshifts can also be used for incremental clustering of growing datasets by recycling previous computations. We present experimental results using medoidshift for image segmentation, incremental clustering for shot segmentation and clustering on nonlinearly separable data. ©2007 IEEE

Ghost Removal In High Dynamic Range Images

High dynamic range images may be created by capturing multiple images of a scene with varying exposures. Images created in this manner are prone to ghosting artifacts, which appear if there is movement in the scene at the time of capture. This paper describes a novel approach to removing ghosting artifacts from high dynamic range images, without the need for explicit object detection and motion estimation. Weights are computed iteratively and then applied to pixels to determine their contribution to the final image. We use a non-parametric model for the static part of the scene, and a pixel\u27s membership in this model determines its weight. In contrast to previous approaches, our technique does not rely on explicit object detection, tracking, or pixelwise motion estimates. Ghost-free images of different scenes demonstrate the effectiveness of our technique. ©2006 IEEE

Ghost Removal in High Dynamic Range Images

High dynamic range images may be created by capturing multiple images of a scene with varying exposures. Images created in this manner are prone to ghosting artifacts, which appear if there is movement in the scene at the time of capture. This paper describes a novel approach to removing ghosting artifacts from high dynamic range images, without the need for explicit object detection and motion estimation. Weights are computed iteratively and then applied to pixels to determine their contribution to the final image. We use a non-parametric model for the static part of the scene, and a pixel's membership in this model determines its weight. In contrast to previous approaches, our technique does not rely on explicit object detection, tracking, or pixelwise motion estimates. Ghost-free images of different scenes demonstrate the effectiveness of our technique