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2,375 results for “Antibiotics”
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Microbiome depletion and recovery in the sea anemone, Aiptasia, following antibiotic exposure
<p>This provides data for the manuscript Microbiome depletion and recovery in the sea anemone, Aiptasia, following antibiotic exposure; 16smetadata.xlsx file used for in the DADA2 pipeline is provided, and the CFU count data is provided in cfu-counts-2022-01-20-modified.csv. The data for total protein content, pedal lacerate counts and algal abundance can all be found in the physio-2022-01-20.csv with the associated dataset (i.e. protein, algae, pedal lacerates) specified in the variable column.</p>
Dataset and figures: Antibiotic underdosing and disposal in NHS organisations across Great Britain
<p>This record contains the data collated and figures generated as part of the production of the "Antibiotic underdosing and disposal in NHS organisations across Great Britain: Research Report and Policy Brief" published by the Office of Baroness Bennett of Manor Castle.</p>
DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model
<p>Training and test images of <em>E. coli</em> cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example images show predictions of drug-treated <em>E. coli</em> cells.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (confocal fluorescence) and manual annotations</p> <p><strong>Microscopy data type</strong>: Confocal fluorescence images of fixed <em>E. coli</em> cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format</p> <p><strong>Microscope</strong>: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA)</p> <p><strong>Cell type</strong>: Chemically fixed <em>E. coli</em> NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated);</p> <p><strong>File format</strong>: .png (RGB)</p> <p><strong>Image size</strong>: 400 x 400 px² (Pixel size: 84 nm)</p> <p> </p> <p><strong>YOLOv2 model</strong></p> <p>The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping.</p> <p>The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Data from: The impact of long-term azithromycin on antibiotic resistance in HIV-associated chronic lung disease
<p><b>Background</b>: Selection for resistance to azithromycin (AZM) and other antibiotics such as tetracyclines and lincosamides remains a concern with long-term AZM use for treatment of chronic lung diseases (CLD). We investigated the impact of 48 weeks of AZM on the carriage and antibiotic resistance of common respiratory bacteria among children with HIV-associated CLD.</p> <p><b>Methods</b>: Nasopharyngeal (NP) swabs and sputa were collected at baseline, 48 and 72 weeks from participants with HIV-associated CLD randomised to receive weekly AZM or placebo for 48 weeks and followed post-intervention until 72 weeks. The primary outcomes were prevalence and antibiotic resistance of <i>Streptococcus pneumoniae</i> (SP), <i>Staphylococcus aureus </i>(SA), <i>Haemophilus influenzae </i>(HI), and <i>Moraxella catarrhalis </i>(MC) at these timepoints. Mixed-effects logistic regression and Fisher's exact test were used to compare carriage and resistance respectively.</p> <p><b>Results</b>: Of 347 (174 AZM, 173 placebo) participants (median age 15 years [IQR =13–18], females 49%),NP carriage was significantly lower in the AZM (n=159) compared to placebo (n=153) arm for SP (18% vs 41%, <i>p</i><0.001)<i>, </i>HI (7% vs 16%, p=0.01)<i>, </i>and MC (4% vs 11%, <i>p</i>=0.02); SP resistance to AZM (62% [18/29] vs 13%[8/63], <i>p</i><0.0001) or tetracycline (60%[18/29] vs 21%[13/63], <i>p</i><0.0001) were higher in the AZM arm. Carriage of SA resistant to AZM (91% [31/34] vs 3% [1/31],<i> p</i><0.0001), tetracycline (35% [12/34] vs 13% [4/31],<i> p</i>= 0.05) and clindamycin (79% [27/34] vs 3% [1/31],<i> p</i><0.0001) was also significantly higher in the AZM arm and persisted at 72 weeks. Similar findings were observed for sputa.</p> <p><b>Conclusions</b>: The persistence of antibiotic resistance and its clinical relevance for future infectious episodes requiring treatment needs further investigation.</p>
Insertion sequences and other mobile elements associated with antibiotic resistance genes in Enterococcus isolates from an inpatient with prolonged bacteremia.
<p>Insertion sequences (ISs) and other transposable elements are associated with the mobilization of antibiotic resistance determinants and the modulation of pathogenic characteristics. In this work, we aimed to investigate the association between ISs and antibiotic resistance genes, and their role in dissemination and modification of the antibiotic resistant phenotype. To that end, we leveraged fully resolved <em>Enterococcus faecium</em> and <em>Enterococcus faecalis</em> genomes of isolates collected over five days from an inpatient with prolonged bacteremia. Isolates from both species harbored similar IS family content but showed significant species-dependent differences in copy number and arrangements of ISs throughout their replicons. Here, we describe two inter-specific IS-mediated recombination events and IS-mediated excision events in plasmids of <em>E. faecium</em> isolates. We also characterize a novel arrangement of the ISs in a Tn1546-like transposon in <em>E. faecalis</em> isolates likely implicated in a vancomycin genotype-phenotype discrepancy. Furthermore, an extended analysis revealed a novel association between daptomycin resistance mutations in <em>liaSR</em> genes and a putative composite transposon in<em> E. faecium</em>, offering a new paradigm for the study of daptomycin resistance and novel insights into the dissemination of daptomycin resistance. In conclusion, our study highlights the role ISs and other transposable elements play in the rapid adaptation and response to clinically relevant stresses such as aggressive antibiotic treatment in enterococci.</p>
Data set of antibiotic study-1
<p>This data set includes the information related to appropriate antibiotic use (labeled as "appropriate_use_antibiotics") among the urban people of Bangladesh. The data set is included sociodemographic information such as age, sex, marital status, and educational status. The attitudes towards antibiotic use are included the information of within 2 months of antibiotic taking frequency (labeled as "took_antibiotics"), awareness of use (labeled as "attitude_awareness_use"), abuse of antibiotics (labeled as "attitude_abuse_antibiotic"), antibiotic resistance (labeled as "attitude_resistance"), and effect of resistance (labeled as "attitude_effect_resistance"). The knowledge of antibiotics treatable diseases such as COVID-19 (labeled as "knowledge_COVID-19"), dengue (labeled as "knowledge_dengue"), diabetics (labeled as "knowledge_diabetics"), pneumonia (labeled as "knowledge_pneumonia"), and tuberculosis (labeled as "knowledge_tuberculosis") are included. The knowledge of types of disease specification are also included bacterial (labeled as "knowledge_bacterial"), viral (labeled as "knowledge_viral"), parasitic (labeled as "knowledge_parasitic"), fungal (labeled as "knowledge_fungal"), and helminthic (labeled as "knowledge_helminthic"). Finally, knowledge of antimicrobials drugs specifications is included Penicillin (labeled as "knowledge_Penicillin"), Amoxicillin (labeled as knowledge_Amoxicillin), Cefixime (labeled as "knowledge_Cefixime"), Azithromycin (labeled as "knowledge_Azithromycin"), Remdisivir (labeled as "knowledge_Remdisivir"), and Albendazole (labeled as "knowledge_Albendazole").</p> <p>Note: The response "Yes_cor" or "No_cor", indicated that the responses were correct to the respective items. </p>
Gut microbiota inter-species interactions shape the response of Clostridioides difficile to clinically relevant antibiotics
<p>In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.</p>
Gut microbiota inter-species interactions shape the response of Clostridioides difficile to clinically relevant antibiotics
<p>In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.</p>
Joint project session at the International Conference on Responsible Use of Antibiotics in Animals
<p>AVANT, DISARM, HealthyLivestock and ROADMAP, projects joined their forces at the 5th International Conference on Responsible Use of Antibiotics in Animals on 8 June 2021. The joint project session "Socio-economic, technical and regulatory dimensions of sustainable change in antimicrobial use in animal production" was composed of 3 themes:</p> <p>- Theme 1: Social, economic, and regulatory factors of transitions</p> <p>- Theme 2: Promising technical innovations to reduce AMU</p> <p>- Theme 3: Stakeholder engagement and impact</p> <p>The video is available on YouTube: </p> <p>For any further questions please contact us at: <strong><a href="https://youtu.be/Dyf9Vq90XpE">https://youtu.be/Dyf9Vq90XpE</a></strong></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>
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
<p>Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured the effect of ARGs on host fitness in the absence of direct selection or determined if any costs are fixed or depend on the host strain, perhaps marking some ARG-host combinations as reservoirs that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse <em>Escherichia spp</em>. strains. Three ARGs (blaTEM-116, cat, and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions cause the success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.</p>
Data for: Solvent effects in hyperpolarization of 15N nuclei in [15N3]metronidazole and [15N3]nimorazole antibiotics via SABRE-SHEATH
<p>Raw 15N and 1H NMR spectra for the article which is under revision at the time posting this dataset.</p> <p>These results are also available as preprint at https://doi.org/10.26434/chemrxiv-2024-6pg8b</p> <p>15N NMR spectra were acquired using SpinSolve Expert Software (Magritek)</p> <p>1H NMR spectra were acquired using TopSpin (Bruker)</p>
Figure 2 in Isolation and characterization of bacteria associated with silkworm gut under antibiotic-treated larval feeding
Figure 2. Phylogenetic relationship and identification of bacterial strains isolated in this study based on 16S rRNA gene sequence through Neighbor Joining method using 1000 bootstrap replicates.
Insights into Acinetobacter baumannii AMA205's Unprecedented Antibiotic Resistance
<p><span>The rise of antibiotic-resistant bacteria in clinical settings has become a significant global concern. Among these bacteria, <em>Acinetobacter baumannii</em> stands out due to its remarkable ability to acquire resistance genes and persist in hospital environments, leading to some of the most challenging infections. Horizontal gene transfer (HGT) plays a crucial role in the evolution of this pathogen. The <em>A. baumannii</em> AMA205 strain, belonging to sequence type ST79, was isolated from a COVID-19 patient in Argentina in 2021. This strain’s antimicrobial resistance profile is notable as it harbors multiple resistance genes, some of which had not been previously described in this species. The AmpC family β-lactamase <em>bla</em><sub>CMY-6</sub>, commonly found in Enterobacterales, had never been detected in <em>A. baumannii </em>before. Furthermore, this is the first ST79 strain known to carry the carbapenemase <em>bla</em><sub>NDM-1 </sub>gene. Other acquired resistance genes include the carbapenemase <em>bla</em><sub>OXA-23</sub>, further complicating treatment. Susceptibility testing revealed high resistance to most antibiotic families, including cefiderocol, with significant contributions from <em>bla</em><sub>CMY-6 </sub>and <em>bla</em><sub>NDM-1 </sub>genes to the cephalosporin and carbapenem resistance profiles. The <em>A. baumannii</em> AMA205 genome also contains genetic traits coding for 111 potential virulence factors, such as the iron-uptake system and biofilm-associated proteins. This study underscores <em>A. baumannii's </em>ability to acquire multiple resistance genes and highlights the need for alternative therapies and effective antimicrobial stewardship to control the spread of these highly resistant strains.</span></p>
Figure 3 in Isolation and characterization of bacteria associated with silkworm gut under antibiotic-treated larval feeding
Figure 3. Phylogenetic relationship of bacterial strains isolated in this study with each other based on 16S rRNA gene sequence through Neighbor-Joining method using 1000 bootstrap replicates.
Figure 1 in Isolation and characterization of bacteria associated with silkworm gut under antibiotic-treated larval feeding
Figure 1. Amplification of 16S rRNA gene (1500 bp) of isolated bacterial strains; lane 1 = HG1, lane 2 = HG2, lane 3 = HG3, lane 4 = DG1, lane 5 = DG2, lane 6 = DG3, -ve = negative control, +ve = positive control, M = 1kb DNA marker.
Dataset for paper "The naphthylated LEGO-lipophosphonoxin antibiotics used as a fluorescent tool for observation of target membrane perturbations preceding its disruption"
<p><span><span><span>The individual text files contain all the numerical data shown in Figures 2-10 in the publication.</span></span></span></p>
Data for "Unravelling the collateral damage of antibiotics on gut bacteria"
<p>This dataset encompasses all data needed to reproduce the analyses presented in the paper "Unravelling the collateral damage of antibiotics on gut bacteria", available here: https://doi.org/10.1038/s41586-021-03986-2</p> <p>You can also check the GitLab repository: https://git.embl.de/maier/abxbug/</p>
Data from: Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics
<p>Understanding the mechanisms by which populations of bacteria resist antibiotics has implications for evolution, microbial ecology, and public health. The inoculum effect (IE), where antibiotic efficacy declines as the density of a bacterial population increases, has been observed for multiple bacterial species and antibiotics. Several mechanisms to account for IE have been proposed, but most lack experimental evidence or cannot explain IE for multiple antibiotics. We show that growth productivity, the combined effect of growth and metabolism, can account for IE for multiple bactericidal antibiotics and bacterial species. Guided by flux balance analysis and whole genome modeling, we show that the carbon source supplied in the growth medium determines growth productivity. If growth productivity is sufficiently high, IE is eliminated. Our results may lead to approaches to reduce IE in the clinic, help standardize the analysis of new antibiotics, and further our understanding of how bacteria evolve resistance. </p>
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at <a href="http://bench.cs.vt.edu/deeparg">http://bench.cs.vt.edu/deeparg</a>.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.