Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action

Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice

Background texture extraction for the classification of mammographic parenchymal patterns

We have developed an approach to the separation of background texture and structures in images. The developed approach is based on the statistical difference between local and median co-occurrence matrices. It is our assertion that the classification of mammographic parenchymal patterns can be improved if anatomical structures can be removed from the image and the classification is based only on the background texture information. We compare the results of the classification between original images and images composed of their reconstructed background texture. 265 mammograms from the MIAS database [1] have been used for our experiment and the classification of the parenchymal patterns is based on Karssemeijer’s model [2]

Using ¢¡¤£¦ ¥ for Risk Assessment in Mammography: a Feasibility Study

Abstract. Appearance of breast parenchyma in mammography provides information on the risk of developing breast cancer. Wolfe was the first to show the relation between mammographic parenchymal patterns and the risk of developing cancer using four classes. Since this discovery automated classification has been investigated. The methods developed can be separated in first order statistical models and texture analysis models. A novel approach based on the percentage of glandular tissue in the breast interpolated from 2D mammographic images is presented. This model is based on the ©����� � model introduced by Highnam ������ �. We investigate the effect of a number of parameters in our model and indicate that the robustness for possible clinical use depends on the uncertainty with which the parameters are determined.

Automated Quality Assurance Applied to Mammographic Imaging

Quality control in mammography is based upon subjective interpretation of the image quality of a test phantom. In order to suppress subjectivity due to the human observer, automated computer analysis of the Leeds TOR(MAM) test phantom is investigated. Texture analysis via grey-level co-occurrence matrices is used to detect structures in the test object. Scoring of the substructures in the phantom is based on grey-level differences between regions and information from grey-level co-occurrence matrices. The results from scoring groups of particles within the phantom are presented