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Governments around the world are keen on models to determine the future clinical and economic burden of antimicrobial resistance, to identify or monitor resistance, and to assist in resource allocation decision making due to the increasing threat to public health and the economy. Models' accuracy in predicting future resistance rates is dependent on the accuracy of forecasting future resistance rates. Although general assumptions regarding future resistance rates can be made based on common causes, the emergence, establishment and dissemination of new resistance genes adds to the amount of future economic burden, as well as studies assessing the effectiveness of interventions or policies to combat AMR. Although general assumptions can be made regarding some predictable causes contributing to future resistance rates, general assumptions regarding future resistance rates can be misleading, particularly in models assessing the effectiveness of interventions or policies to address AMR. While general assumptions can be made regarding AMR's To ensure model transparency and validation by policymakers, existing reporting frameworks for best practice in modelling should be expanded to support the reporting of AMR economic models.
Source link: https://doi.org/10.1007/s40258-022-00728-x
Purpose Artificial intelligence is a part of our daily life, and machine learning techniques in medicine have opened possibilities that were not available until now. Methods Data were collected from laboratory information system involving 239 patients with urolithiasis hospitalized in a tertiary hospital's Urology department over a 1-year period : age, gender, Gram stain, bacterial species, sample type, antibiotics, and antimicrobial susceptibility in a tertiary hospital's urology unit. Using a multinomial logistic regression model with a ridge estimator, the best results in the balanced dataset containing Gram stain yielded a weighted average receiver curve area of 0. 768 and F-measure of 0. 708. Conclusions (PDF) When identifying particular microorganisms, artificial intelligence techniques can be used to generate predictions on antibiotic resistance patterns when determining Gram staining with a sensitivity of 77% and nearly 87%.
Source link: https://doi.org/10.1007/s00345-022-04043-x
When assessing the benefits and costs of mass administration of azithromycin to reduce childhood mortality, it is important but difficult to measure, especially when assessing resistance that has evolved from antibiotic-treated individuals to other members of their community. This scoping report sought to determine how the existing literature on antibiotic resistance modeling could be used to better understand the effects of mass drug administration on antibiotic resistance. Conclusions based on statistical studies of antibiotic use and resistance may give more detailed and useful estimates of the potential effects of MDA on resistance. While being able to more precisely assess the effects of various MDA experiments on resistance, Mechanistic models of resistance, although being able to more precisely estimate the consequences of various MDA schemes on resistance, may require more data from MDA trials to be more reliable.
Source link: https://doi.org/10.1186/s40249-022-00997-7
However, with antimicrobial resistance becoming a global crisis, the dangers posed by widespread antimicrobial use must be investigated. AMR recruitment and dissemination has become more widely recognized, but it is also crucial to establish the role of MDA in environmental AMR pollution, as well as the potential effects of such pollution. This article presents the most recent review of the current state of knowledge regarding antimicrobial compounds, resistant organisms, and antimicrobial resistance genes in MDA studies, routes of these determinants into the atmosphere, and environmental effects, particularly in low- and middle-income countries, where these trials are most common. From the few studies that specifically examined AMR findings in azithromycin MDA studies, it is clear that MDA efforts can raise carriage and excretion of resistant pathogens in a long time.
Source link: https://doi.org/10.1186/s40249-022-01000-z
Antimicrobial resistance is a hot topic worldwide, and the presence of antimicrobial residues and antimicrobial resistance genes in the atmosphere, particularly in the water sources, is a threat to public health. In Southern Brazil, this research was done to determine the presence and diversity of AR and ARG in water sources from urban center. During two annual samplings, both winter and summer, a total of thirty-two water samples from drinking water treatment plants and sewage systems were obtained.
Source link: https://doi.org/10.1007/s42770-022-00786-2
Objectives: Antimicrobial resistance is a public health risk related to antibiotic use, and it is linked to antibiotic use. We wanted to assess the investments needed for a large-scale rollout of point-of-care diagnostic testing in Dutch primary care, as well as the impact on AMR due to decreased use of antibiotics. Methods We developed an individual-based simulation that simulates CA-ARTI consultations at GP practices in the Netherlands and compared to a situation where GPs observe all CA-ARTI patients with a hypothetical diagnostic approach to maintain the current standard-of-care for the years 2020-2030. Despite significant uncertainty, the predicted rise in Streptococcus pneumoniae resistance against penicillins can be partially restrained by the unconstitutional diagnostic strategy from 3. 8 to 3. 5 percent in 2030. Conclusions: Our findings reveal that implementing a hypothetical diagnostic plan for all CA-ARTI patients in primary care raises the cost of consultations, while decreasing antibiotic intake and AMR can be reduced.
Source link: https://doi.org/10.1007/s40273-022-01165-3
The aim of this study is to determine the effects of COVID-19 pandemic on antimicrobial resistance in paediatrics and the potential of antimicrobial resistance reductions. Introduction This review paper explores the impact of COVID-19 on antimicrobial resistance formation and prevention of it, with particular emphasis on paediatric population antimicrobial stewardship programs.
Source link: https://doi.org/10.1007/s40495-022-00298-5
In our university hospital, we performed a retrospective review of the prevalence of uropathogens in hospitalized children with a febrile UTI between 2000 and 2019 to gain further insight into trends and predictors of antimicrobial resistance over time. An increase in resistance against amoxicillin clavulanic acid in over time from 16% to 36% with an average rise of 2. 0%/year; this was + 1. 1%/year for third-generation cephalosporin. Prior antibiotic use was an additional risk factor for antimicrobial resistance in E. coli, according to a multivariate investigation. Conclusion : Within a short period of time, we observed a consistent pattern of increasing antimicrobial resistance of E. coli, making it more difficult to treat pediatric UTIs. U2022 is the first 20-year retrospective, longitudinal study on the characteristics of pediatric urinary tract infections in a single center.
Source link: https://doi.org/10.1007/s00431-022-04538-0
Background The World Health Organization's Global Action Plan on Antimicrobial resistance (GAP) was launched as a priority due to the growing threat posed to human health, animal health, and agriculture. Using a strategic analysis approach, the aim of this report was to examine the NAPs' content and determine alignment with the Global Action Plan on Antimicrobial Resistance. The WHO Library and systematically analysed national action plans for actors, process, location, and content, according to a policy analysis framework for actors, process, space, and content. To determine compliance with the five WHO Global Action Plans goals, information was collected using a u2018traffic light (u2019) device. High income countries showed greater progress with their targets, while low and middle-income countries demonstrated the need for human and financial support, while middle-income countries demonstrated a lack of human and financial resources. Conclusion The national action plans give an overview of the current efforts to combat AMR around the world.
Source link: https://doi.org/10.1186/s13756-022-01130-x
One of the most common uses of genomic and metagenomic sequencing is in determining the type and variety of antimicrobial resistance genes present in bacterial isolates in order to determine AMR phenotypes in order to make predictions regarding their AMR phenotype. A raw sequence reads mapped to a collection of 174 potentially pathogenic bacteria were found to this conclusion in this aftermath as part of the Microbial Bioinformatics Hackathon and Workshop 2021.
Source link: https://doi.org/10.1038/s41597-022-01463-7
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