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270 results for “antimicrobial resistance”
PanRes - Collection of antimicrobial resistance genes
<p><strong>PanRes database of antimicrobial resistance genes</strong></p><p>Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.</p><p>For details, please see the methods section in the following publication:</p><p><strong> "ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)</strong></p><p>Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.<br><br>A number of previously published collections of ARGs were used in the creation of PanRes (See references):</p><p><strong>ResFinder</strong> (downloaded 2023-01-20, (Bortolaia et al. 2020)),<br><strong>ResFinderFG</strong> (version 2.0, (Gschwind et al. 2023))<br><strong>CARD</strong> (version 3.2.5, (Alcock et al. 2023))<br><strong>MegaRes</strong> (version 3.0.0, (Bonin et al. 2023))<br><strong>AMRFinderPlus</strong> (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))<br><strong>ARGANNOT</strong> (V6_July2019, (Gupta et al. 2014))<br><strong>The 'CsabaPal' collection</strong> (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))<br><strong>BacMet</strong> (version 1.1, (Pal et al. 2014))</p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Genomic Typing, Antimicrobial Resistance Gene, Virulence Factor and Plasmid Replicon Dataset for the Important Pathogenic Bacteria Klebsiella pneumoniae
<p>The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p> <p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p> <p>In this dataset, we present the results of a genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen <em>Klebsiella pneumoniae</em> obtained from NCBI Genbank database. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), capsular (KL) and oligosaccharide (OL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to clonal groups (CG). The presence of antimicrobial resistance and virulence genes, as well as plasmid replicons, within the genomes is also reported. </p> <p>These data will be useful for researchers in the field of <em>K. pneumoniae</em> genomic epidemiology, resistance analysis and prevention measure development.</p>
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E. Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</p> <p> </p>
Antimicrobial resistance - Salmonella, E. Coli, prevalence ESBL data
<p>The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E.Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.</p> <p> </p> <p><strong>Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)</strong></p>
Supplementary material Comparative Genomic Analysis of Antimicrobial-Resistant Escherichia coli from South American Camelids in Central Germany
<p>Supplementary material for publication González-Santamarina, B.; Weber, M.; Menge, C.; Berens, C. Comparative Genomic Analysis of Antimicrobial-Resistant <i>Escherichia coli</i> from South American Camelids in Central Germany. <i>Microorganisms</i> <strong>2022</strong>, <i>10</i>, 1697. https://doi.org/10.3390/microorganisms10091697 </p>
Datasets for Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar
<p>These datasets accompany the analyses conducted under the study title "<span>Metagenome-wide characterization of shared antimicrobial resistance genes in sympatric people and lemurs in rural Madagascar</span>"</p> <p>Accompanying R scripts with commentary to run these data can be found in the Github repository under release v1.0.0: https://github.com/bmtalbot/Humans_and_Lemurs_2017</p>
When does antimicrobial resistance increase bacterial fitness? Effects of dosing, social interactions and frequency dependence on the benefits of AmpC β-lactamases in broth, biofilms and a gut infection model.
<p><span>One of the longstanding puzzles of antimicrobial resistance is why the frequency of resistance persists at intermediate levels.<span> </span>Theoretical explanations for the lack of fixation of resistance include cryptic costs of resistance or negative frequency-dependence but are seldom explored experimentally. <span> </span><em>β</em>-lactamases, which detoxify penicillin-related antibiotics, have well-characterized frequency-dependent dynamics driven by cheating and cooperation.<span> </span>However, bacterial physiology determines whether <em>β</em>-lactamases are cooperative and we know little about the sociality or fitness of <em>β</em>-lactamase producers in infections.<span> </span>Moreover, media-based experiments constrain how we measure fitness, and ignore important parameters such as infectivity and transmission among hosts.<span> </span>Here, we investigated the fitness effects of broad-spectrum AmpC <em>β</em>-lactamases in <em>Enterobacter cloacae</em> in broth, biofilms and gut infections in a model insect. <span> </span>We quantified frequency- and dose-dependent fitness using cefotaxime, a third-generation cephalosporin.<span> </span>We predicted that infection dynamics would be similar to those observed in biofilms, with social protection extending over a wide dose range.<span> </span>We found evidence for the sociality of <em>β</em>-lactamases in all contexts with negative frequency-dependent selection ensuring the persistence of wild-type bacteria although cooperation was less prevalent in biofilms, contrary to predictions.<span> </span>While competitive fitness in gut infections and broth had similar dynamics, incorporating infectivity into measurements of fitness in infections<em> </em>significantly affected conclusions. <span> </span>Resistant bacteria had reduced infectivity which limited the fitness benefits of resistance to infections challenged with low antibiotic doses and having low initial frequencies of resistance. <span> </span>The fitness of resistant bacteria in more physiologically tolerant states (in biofilms, in infections) could be constrained by the presence of wild-type bacteria, high antibiotic doses and limited availability of <em>β</em>-lactamases.<span> </span>One conclusion is that increased tolerance of <em>β</em> -lactams does not necessarily increase selection pressure for resistance.<span> </span>Overall, both cryptic fitness costs and frequency-dependence curtailed the fitness benefits of resistance in this study.<span> </span></span></p> <p><span> </span></p>
Alterations of antimicrobial resistance genes in the clinical multi-drug resistance Acinetobacter baumannii isolates in Vietnam
<p><strong>FIGURE 1 </strong>List of antibiotic resistance gene expression.</p> <p><strong>TABLE 1 </strong>Primers used in this study.</p> <p><strong>TABLE 2 </strong>Clinical characteristics of 30 patients with <em>A. baumannii </em>isolates.</p> <p><strong>TABLE 3 </strong>Antimicrobial resistance values of <em>A. baumannii</em> isolated during 2017 to 2019 year in the Military Hospital 103.</p> <p><strong>TABLE 4 </strong>Relationship between genotype and phenotype antimicrobial resistance of isolates in this study.</p> <p><strong>TABLE 5 </strong>List of altered antibiotic resistance genes expression in isolates of <em>A. baumannii</em> (up or down two-fold changes of related genes compare with <em>16S rRNA</em> gene).</p> <p><strong>TABLE 6 </strong>The multiple antimicrobial resistance gene profile of 30 <em>A. baumannii</em> isolates.</p> <p> </p>
Code and Additional Files for the Manuscript "The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand"
<p>These scripts were used in the"The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand" manuscript. The scripts are ordered for ease of use. It also contains intermediate files and files necessary for the mapping. Table S1 containing the metadata is available in the supplementary information of the manuscript.</p>
ArMoR Cluster: 5 research projects fight Antimicrobial Resistance in livestock farming
<p>Within Horizon Results Booster programme (HRB), 4 Horizon 2020 projects (AVANT, DISARM, HealthyLivestock and ROADMAP) and 1 BBSRC funded project (AMRILS) have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects.</p> <p>The video is available on YouTube: <strong><a href="https://www.youtube.com/watch?v=rnU35ytdEuM">https://www.youtube.com/watch?v=rnU35ytdEuM</a></strong></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT),</li> <li><strong><a href="https://zenodo.org/record/info@disarmproject.eu">info@disarmproject.eu</a></strong> (project DISARM),</li> <li><strong><a href="https://zenodo.org/record/healthylivestockproject@yahoo.com">healthylivestockproject@yahoo.com</a></strong> (project Healthy Livestock) or</li> <li><strong><a href="mailto:roadmap.communication@gmail.com">roadmap.communication@gmail.com</a></strong> (project ROADMAP). </li> </ul>
The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures
<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>
Figure 3 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 3. Locations and copy numbers for human- and ruminant-indicative Bacteroides spp. DNA extracted from the material retained from filtration of 500 ml was used for qPCR determination of rDNA copy number. The size of the indicated shapes in the figure is proportional to the copy number/ml at that point as indicated in the legend. Inset shows points 12 and 13 at the same scale as the main figure.
Figure 2 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 2. Locations and concentrations of coliforms and E. coli at water collection points. A volume of water (100 µl – 1 ml) collected mid-stream was plated using the Coliscan culture system. Colonies were identified and counted at 48h. The size of the indicated shapes in the figure is proportional to the number of colonies/ml cultured as indicated in the legend. Inset shows points 12 and 13 at the same scale as the main figure.
Figure 1 in Microbial source tracking and antimicrobial resistance in one river system of a rural community in Bahia, Brazil
Figure 1. Study area, rivers and water collection sites. The collection points on the Jiquiriçá River are P1-5; collection points on the Brejões P6-8. P3 is at the junction of the 2 rivers, and P9 and P10 are from the water treatment plant and an outside faucet, respectively. Left inset – Location of Bahia state, Salvador and Jenipapo within Brazil based on Wikimedia Commons (2011). Right inset – relationship of P12 and P13 to Jenipapo. These 2 points represent the source of piped water for the community and the furthest point upstream for collection on the Brejões River, respectively. Inset modified from Wikimedia Commons (2011).
Figure 1 in Antimicrobial resistance profile of Aeromonas spp. isolated from asymptomatic Colossoma macropomum cultured in the Amazonas State, Brazil
Figure 1. Records of Aeromonas spp. isolated from tambaqui (Colossoma macropomum) by fish farms in the rainy season (bars with stripes) and the dry season (bars with dots).
Tables and Figures complementing the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2016
<p>All tables, figures and maps produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2016 are provided.</p>
Tables and Figures complementing the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2017
<p>All tables, figures and maps produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2017 are provided.</p>
MS-UMG: MALDI-TOF Mass Spectra and Resistance Information on Antimicrobials from University Medical Center Göttingen
<p>During routine diagnostic procedures, we aggregated MALDI-TOF MS data of organisms isolated from clinical specimens from the University Medical Center Göttingen (UMG) in 2020 / 2021. We integrated these with corresponding antimicrobial susceptibility profiles. This amounted to 26,961 mass spectra and 26,961 corresponding metadata entries for the year 2020, and 50,381 mass spectra and 50,381 corresponding metadata entries for 2021, respectively. The dataset reflects 348 different species of bacterial and fungal organisms and 72 different antimicrobial susceptibility testing (AST) results.</p> <p> </p> <p>Please cite: </p> <div> <div>Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning</div> </div> <div><span><span><span>Youngjun</span> <span>Park</span></span>, <span><span>Michael</span> <span>Weig</span></span>, <span><span>Christine</span> <span>Noll</span></span>, <span><span>Oliver</span> <span>Bader</span></span>, <span><span>Anne-Christin</span> <span>Hauschild</span></span></span></div> <div><span>bioRxiv </span><span>2024.10.18.617592; </span><span><span>doi:</span> https://doi.org/10.1101/2024.10.18.617592</span></div>
European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2020/2021
<p>All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2021:</p> <p>- <em>Campylobacter</em></p> <p><em>- E. coli</em></p> <p>- MRSA</p> <p>- <em>Salmonella</em></p> <p>- ESBL</p> <p>Annexes A to F are also included.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.