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      An effective antibiofilm strategy based on bacteriophages armed with silver nanoparticles

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          Abstract

          The emerging antibiotic resistance in pathogenic bacteria is a key problem in modern medicine that has led to a search for novel therapeutic strategies. A potential approach for managing such bacteria involves the use of their natural killers, namely lytic bacteriophages. Another effective method involves the use of metal nanoparticles with antimicrobial properties. However, the use of lytic phages armed with nanoparticles as an effective antimicrobial strategy, particularly with respect to biofilms, remains unexplored. Here, we show that T7 phages armed with silver nanoparticles exhibit greater efficacy in terms of controlling bacterial biofilm, compared with phages or nanoparticles alone. We initially identified a novel silver nanoparticle-binding peptide, then constructed T7 phages that successfully displayed the peptide on the outer surface of the viral head. These recombinant, AgNP-binding phages could effectively eradicate bacterial biofilm, even when used at low concentrations. Additionally, when used at concentrations that could eradicate bacterial biofilm, T7 phages armed with silver nanoparticles were not toxic to eukaryotic cells. Our results show that the novel combination of lytic phages with phage-bound silver nanoparticles is an effective, synergistic and safe strategy for the treatment of bacterial biofilms.

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          Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

          (2022)
          Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
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            Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.

            Many different definitions for multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR) bacteria are being used in the medical literature to characterize the different patterns of resistance found in healthcare-associated, antimicrobial-resistant bacteria. A group of international experts came together through a joint initiative by the European Centre for Disease Prevention and Control (ECDC) and the Centers for Disease Control and Prevention (CDC), to create a standardized international terminology with which to describe acquired resistance profiles in Staphylococcus aureus, Enterococcus spp., Enterobacteriaceae (other than Salmonella and Shigella), Pseudomonas aeruginosa and Acinetobacter spp., all bacteria often responsible for healthcare-associated infections and prone to multidrug resistance. Epidemiologically significant antimicrobial categories were constructed for each bacterium. Lists of antimicrobial categories proposed for antimicrobial susceptibility testing were created using documents and breakpoints from the Clinical Laboratory Standards Institute (CLSI), the European Committee on Antimicrobial Susceptibility Testing (EUCAST) and the United States Food and Drug Administration (FDA). MDR was defined as acquired non-susceptibility to at least one agent in three or more antimicrobial categories, XDR was defined as non-susceptibility to at least one agent in all but two or fewer antimicrobial categories (i.e. bacterial isolates remain susceptible to only one or two categories) and PDR was defined as non-susceptibility to all agents in all antimicrobial categories. To ensure correct application of these definitions, bacterial isolates should be tested against all or nearly all of the antimicrobial agents within the antimicrobial categories and selective reporting and suppression of results should be avoided. © 2011 European Society of Clinical Microbiology and Infectious Diseases. No claim to original US government works.
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              A Threshold Selection Method from Gray-Level Histograms

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                Author and article information

                Contributors
                piotr.golec@uw.edu.pl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 April 2024
                20 April 2024
                2024
                : 14
                : 9088
                Affiliations
                [1 ]Department of Molecular Virology, Faculty of Biology, Institute of Microbiology, University of Warsaw, ( https://ror.org/039bjqg32) Miecznikowa 1, 02-096 Warsaw, Poland
                [2 ]GRID grid.413454.3, ISNI 0000 0001 1958 0162, Dioscuri Centre for Physics and Chemistry of Bacteria, Institute of Physical Chemistry, , Polish Academy of Sciences, ; Kasprzaka 44/52, 01-224 Warsaw, Poland
                [3 ]GRID grid.413454.3, ISNI 0000 0001 1958 0162, Nalecz Institute of Biocybernetics and Biomedical Engineering, , Polish Academy of Sciences, ; Ks. Trojdena 4, 02-109 Warsaw, Poland
                Author information
                http://orcid.org/0000-0002-0593-0069
                Article
                59866
                10.1038/s41598-024-59866-y
                11032367
                38643290
                fdbe508a-6b77-4f1a-bf7f-c08351032036
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 March 2023
                : 15 April 2024
                Funding
                Funded by: Narodowe Centrum Nauki (National Science Centre)
                Award ID: 2016/23/B/NZ6/02537
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                bacteriophages,nanoparticles
                Uncategorized
                bacteriophages, nanoparticles

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