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      Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples

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          Abstract

          Rapid antimicrobial susceptibility testing (AST) is urgently needed for informing treatment decisions and preventing the spread of antimicrobial resistance resulting from the misuse and overuse of antibiotics. To date, no phenotypic AST exists that can be performed within a single patient visit (30 min) directly from clinical samples. We show that AST results can be obtained by using digital nucleic acid quantification to measure the phenotypic response of Escherichia coli present within clinical urine samples exposed to an antibiotic for 15 min. We performed this rapid AST using our ultrafast (~7 min) digital real-time loop-mediated isothermal amplification (dLAMP) assay [area under the curve (AUC), 0.96] and compared the results to a commercial (~2 hours) digital polymerase chain reaction assay (AUC, 0.98). The rapid dLAMP assay can be used with SlipChip microfluidic devices to determine the phenotypic antibiotic susceptibility of E. coli directly from clinical urine samples in less than 30 min. With further development for additional pathogens, antibiotics, and sample types, rapid digital AST (dAST) could enable rapid clinical decision-making, improve management of infectious diseases, and facilitate antimicrobial stewardship.

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          The operated Markov´s chains in economy (discrete chains of Markov with the income)

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            The causes of antibiotic resistance are complex and include human behaviour at many levels of society; the consequences affect everybody in the world. Similarities with climate change are evident. Many efforts have been made to describe the many different facets of antibiotic resistance and the interventions needed to meet the challenge. However, coordinated action is largely absent, especially at the political level, both nationally and internationally. Antibiotics paved the way for unprecedented medical and societal developments, and are today indispensible in all health systems. Achievements in modern medicine, such as major surgery, organ transplantation, treatment of preterm babies, and cancer chemotherapy, which we today take for granted, would not be possible without access to effective treatment for bacterial infections. Within just a few years, we might be faced with dire setbacks, medically, socially, and economically, unless real and unprecedented global coordinated actions are immediately taken. Here, we describe the global situation of antibiotic resistance, its major causes and consequences, and identify key areas in which action is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.
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              Stochastic simulation of chemical kinetics.

              Stochastic chemical kinetics describes the time evolution of a well-stirred chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to chemical kinetics in the analysis of cellular systems in biology, where the small molecular populations of only a few reactant species can lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. After reviewing the supporting theory of stochastic chemical kinetics, I discuss some recent advances in methods for using that theory to make numerical simulations. These include improvements to the exact stochastic simulation algorithm (SSA) and the approximate explicit tau-leaping procedure, as well as the development of two approximate strategies for simulating systems that are dynamically stiff: implicit tau-leaping and the slow-scale SSA.
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                Author and article information

                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                October 04 2017
                October 04 2017
                October 04 2017
                October 04 2017
                : 9
                : 410
                : eaal3693
                Article
                10.1126/scitranslmed.aal3693
                6765391
                28978750
                1bb7f1cf-99c4-4bab-aca5-cdb72d28bb7e
                © 2017

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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