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2,375 results for “Antibiotics”
Antibiotic Prescription in Children with Acute Upper Respiratory Tract Infections-Egypt
<p>This dataset for data collected to study knowledge , attitude and practice of non-specialized physician in Assiut, Egypt as regards antibiotic prescription in cases of children with acute upper respiratory tract infections.<br> Questionnaire of the study has been uploaded with the dataset and for each question the responses were coded in dataset as presented in the questionnaire (i.e No=0, Yes=1).</p>
Antibiotic administration without prescription dataset
<p>This data set includes information related to the administration of antibiotic without prescription antibiotic use (labeled as "administration_antibiotic_without_prescription") 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 within two 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>
Data for: Can heavy metal pollution induce soil bacterial community resistance to antibiotics in boreal forests?
<p>The emergence of microbial antibiotic resistance is a central threat to global health, food security, and development. It has been shown that heavy metal pollution can give rise to microbial resistance to antibiotics, but how wide-spread this phenomenon is remains an open question that urgently needs filling to enable appropriate environmental risk assessments. Here, we determined whether long-term differences in heavy metal pollution in boreal forests had affected soil microbial communities such that they had increased microbial resistance to antibiotics. First, we assessed variation in metal concentrations in samples collected across a distance trajectory from the pollution source, and also the microbial rates and levels of bacterial community resistance to the heavy metal Cu and the antibiotics tetracycline and vancomycin in those samples. Second, we tested if the exposure to Cu or tetracycline could increase bacterial community resistance to Cu and to antibiotics in soils with high versus low background levels of metal contamination. Metal pollution had affected microbial community structures and suppressed decomposer functioning. Importantly, bacterial community Cu resistance increased with higher metal concentrations, which coincided with an induced bacterial community resistance to tetracycline, but not to vancomycin. Laboratory experiments revealed that bacterial community Cu resistance could be further induced in both the low and high end of the pollution gradient, but also that these short-term inductions of community metal tolerance did not coincide with enhanced antibiotic resistance. This yielded a surprising negative correlation between long-term and short-term effects by metals on microbial metal and antibiotic resistances. One mechanism that could provide protection against both metal cations and tetracycline is the small multidrug resistance (SMR) family, which is an energy demanding physiological mechanism that may take time to confer protection. This may explain the different microbial responses to long-term gradients and metal addition experiments. Policy implications. We show that metal pollution in boreal forests will promote antibiotic resistance in soil bacterial communities, revealing an overlooked reservoir of antibiotic resistance. We recommend that environmental risk assessments for any activity giving rise to increased soil metal concentrations need to also consider the induction of microbial antibiotic resistance.</p>
Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome
<p>Sequencing data for spontaneous resistant mutants of <em>Pseudomonas aeruginosa </em>PAO1-GFP generated for the preprinted project "Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome". A total of nine resistant mutants selected on the clinical breakpoint concentrations for meropenem, ciprofloxacin and ceftazidime were sequenced, along with the ancestral GFP-tagged PAO1 background strain (PAO1-GFP). Libraries were sequenced using the Illumina NovaSeq6000 platform using a 250bp paired-end protocol (via microbesNG), submitted for x60 depth sequencing.<br>Preprint for this project found here: https://www.biorxiv.org/content/10.1101/2023.11.14.567137v1</p>
Rapid Inference of Antibiotic Susceptibility Phenotype of Uropathogens using Metagenomic Sequencing with Neighbour Typing - RASE Database for EuSCAPE
<p>RASE databases used for the prediction of antibiotic phenotype in the paper titled "Rapid Inference of Antibiotic Susceptibility Phenotype of Uropathogens using Metagenomic Sequencing with Neighbour Typing". A database for <em>Klebsiella pneuomiae </em>constructed from EuSCAPE isolates<em>.</em></p>
Dataset for "Formal Single Atom Editing of the Glycosylated Natural Product Fidaxomicin Improves Acid Stability and Retains Antibiotic Activity"
<p>ZIP File:</p> <p>Characterisation data (such as e.g. NMR, IR, MS spectra)</p> <p>NMR raw data, .mnova files</p> <p>DP4+ data (final conformer coordinate files, result tables)</p> <p>DFT simulation data (coordinate files, results table)</p> <p>PDF file:</p> <p>Supporting information for</p> <p>Formal Single Atom Editing of the Glycosylated Natural Product Fidaxomicin Improves Acid Stability and Retains Antibiotic Activity</p>
Fig 1 in Tail and fin rot disease and antibiotics resistance pattern in major rainbow trout farms of Nepal
Fig 1: Survey and Sample Collection site
Surface Enhanced Raman Spectroscopy and Machine Learning for Identification of Beta-Lactam Antibiotics Resistance Gene Fragment in Bacterial Plasmid
<p>Background: The appearance of antibiotic-resistant bacteria represents a critical medical problem with high risk to patient health. Therefore, simple, express, and reliable methods of antibiotic resistance detection should be developed.</p> <p>Results: In this work, we propose a combination of highly sensitive surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) for the detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and the created fragments were captured by functional SERS substrates without preliminary (bio)samples separation or purification. Based on subsequent SERS measurements, a database was created for the training and validation of ML.</p> <p>Significance: The reliability of the proposed method was tested on control samples and we showed the possibility of express SEPS-ML detection of bacterial plasmids containing a characteristic gene up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample (i.e. the presence of the excess of alternative plasmids or various biomolecules). The proposed approach provides a good alternative to modern methods for monitoring antibiotic-resistant bacteria and is favored by its simplicity, low detection limit, and the possibility of express and unpretentious analysis.</p>
Manuscript dataset - Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia
<p>This is a dataset associated with submission of the manuscript "Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia"</p>
Virulence and antibiotic resistance plasticity of Arcobacter butzleri: insights on the genomic diversity of an emerging human pathogen (genome assembly, annotation dataset, core- and pan-genome loci)
<p>This dataset refers to the analysis of 49 <em>Arcobacter butzleri</em> genomes and includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the predicted transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files), the respective amino acid sequences of the translated CDS sequences (.faa files), the nucleotide alignments of all the 1165 core-genome loci, the nucleotide alignments of the genes <em>hecA</em>, <em>tetR </em>and <em>porA</em>, the categorized amino acid sequences of the six hypervariable regions of PorA, and the nucleotide sequences of the first allele of each of the 7474 pan-genome loci with the respective complete allelic profile matrix.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB34441).</p>
Research data for the article "Membrane-Active Antibiotics Affect Domains in Bacterial Membranes as the First Step of Their Activity" by Adéla Melcrová, Christiaan Klein, and Wouter Roos, Nano Letters 2024.
<p>Research data for the publication Membrane-Active Antibiotics Affect Domains in Bacterial Membranes as the First Step of Their Activity, Nano Letters, 2024. <a href="https://doi.org/10.1021/acs.nanolett.4c01873" target="_blank" rel="noopener">https://doi.org/10.1021/acs.nanolett.4c01873</a></p> <p>The data set contains experimental data on the study of antimicrobial N-alkylamide 3d and its interaction with the membrane extracted from the bacteria Staphylococcus aureus. Experimental details can be found in the Methods section of the Supporting Information of the mentioned article. </p> <p>The data set contains underlying data for each Figure in the publication including the figures in the Supporting Information. </p> <ul> <li>Figure 1, S2, S3, S6, S8 - Atomic Force Microscopy images in the raw format as collected on JPK Nano Wizard Ultra Speed AFM.</li> <li>Figure 2, 3, S1 - High-Speed Atomic Force Microscopy images in the raw format as collected on high-speed AFM RIBM machine. The data could be opened in Kodec software. The full trajectories recorded on the high-speed AFM machine are presented here. Only snapshots were used in the publication Figures.</li> <li>Figure S7, S9 - Snapshots from the High-Speed AFM measurements showing the cross-section lines (.bmp) together with the cross-section profiles (.csv) that were used to measure the height of supramolecular structures on top of the bacterial membrane.</li> <li>Data underlying Figure S5 and Supporting Video 1 overlap with the data presented in Figure 2a.</li> </ul>
Data set "Correlation of in vitro biofilm formation capacity with persistence of antibiotic-resistant Escherichia coli on fresh leafy produce"
<p>This repository contains the metadata and raw data for the analysis of an <em>in vitro </em>screening of 174 antibiotic-resistance E. coli strains isolated from various sources to evaluate their ability and strength to form biofilms.</p> <p>This repository contains the raw data to characterise a subset of eleven <em>E. coli </em>strains in their population dynamics and persistance on lamb's lettuce (<em>Valerianella locusta</em>) leaves.</p> <p>Raw images (czi format) of live/dead stain of selected strains in <em>V. locusta </em>leaves are provided.</p> <p>Data analysis and image processing scripts can be found in the GitHub repository associated to the manuscript.</p>
TRADE4SD Deliverable 2.3: Database and infographics on standards rapprochement: STCs and bilateral measure of distance on pesticides and antibiotics
<p><span>One of the objectives of the Horizon2020 project “TRADE4SD” is to offer policy recommendations for improving trade policies at the national, European, and global levels, including reforms to the WTO, and to enhance policy alignment. To achieve this goal, it is essential to address the impact of NTMs, which, despite their increasing use, remain largely underexplored in terms of the effects on international trade. This limited understanding is due to the complexity of NTMs and their effects are difficult to generalise. Several critical areas related to NTMs and their implications for international trade require further investigation, which “TRADE4SD” seeks to address. This work explores how NTMs affect market access for developing countries, as these nations may face various challenges, such as limited capabilities, technological gaps, weaker infrastructures and institutions, and asymmetric information. More specifically, “TRADE4SD” aims to identify best practices for improving the management of SPS, which are essential for enhancing the competitiveness of agricultural and food exports. Strengthening SPS capacity is also vital for boosting productivity in the agricultural and food processing industries, contributing to agricultural and rural development, and helping to alleviate poverty. </span></p> <p><span>This deliverable consists of two main sections: one addressing Maximum Residue Levels (MRLs) for pesticides and antibiotics and the other focusing on Specific Trade Concerns (STCs). </span></p> <p><span>In the first section, we analysed the regulations and then acquired and processed the data to create the databases, which we later used to develop the indices and infographics (for both pesticides and antibiotics).</span></p> <p><span>In the STCs section, we analysed WTO documentation on all open disputes and verified their status. During our analysis, we identified the relationships between STCs and the Sustainable Development Goals (SDGs). Finally, we developed the infographics.</span></p>
Dataset for "Switching Residues: A Platform for the Synthesis of Fidaxomicin Antibiotics"
<p>ZIP File:</p> <p>Characterisation data (such as e.g. NMR, IR, MS spectra)</p> <p>NMR raw data, .mnova files</p> <p> </p>
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Processed data for Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling Listeria
<p>Processed/source data for the manuscript Evidence of horizontal gene transfer and environmental selection impacting antibiotic resistance evolution in soil-dwelling <em>Listeria</em>.</p>
Data from: Antibiotic uptake across gram-negative outer membranes: better predictions towards better antibiotics
<p>Crossing the gram-negative bacterial membrane poses a major barrier to antibiotic development, as many small molecules that can biochemically inhibit key bacterial processes are rendered microbiologically ineffective by their poor cellular uptake. The outer membrane is the major permeability barrier for many drug-like molecules, and the chemical properties that enable efficient uptake into mammalian cells fail to predict bacterial uptake. We have developed a computational method for accurate prospective prediction of outer-membrane uptake of drug-like molecules, which we combine with a new medium-throughput experimental assay of outer membrane vesicle swelling. Parallel molecular dynamics simulations of compound uptake through E. coli OmpF are used to successfully and quantitatively predict experimental permeabilities measured via either outer membrane swelling or via prior liposome swelling measurements. These simulations are analyzed using an inhomogeneous solubility-diffusion model to yield predictions of permeability. For most polar molecules we test, outer membrane permeability also correlates well with whole-cell uptake. The ability to accurately predict and measure outer-membrane uptake of a wide variety of small molecules will enable simpler determination of which molecular scaffolds and which derivatives are most promising prior to extensive chemical synthesis. It will also assist in formulating a more systematic understanding of the chemical determinants of outer-membrane permeability.</p>
Experimental data for Pseudomonas aeruginosa from experimental evolution under different bottleneck sizes and antibiotic selection pressures
<p>We here combined evolution experiments with genomic and genetic analyses to assess whether bottleneck size and antibiotic-induced selection influences the evolutionary path to resistance in pathogenic<i> Pseudomonas aeruginosa</i>, one of the most problematic opportunistic human pathogens. Two sets of evolution experiments were performed across 16 transfers, in which either the aminoglycoside gentamicin or the fluoroquinolone ciprofloxacin were used as antibiotic. The evolutionary response was studied using counts of bacterial cells at the end of each transfer period (i.e., yield) or by calculating the growth rate from regular optical density measurement during the respective transfer periods. For the populations at the end of the evolution experiments, we also determined their antibiotic resistance with the help of standardized dose response curves. Moreover, we performed whole genome sequencing to assess the frequency of variants, which emerged and spread during the evolution experiments. We further focused on variants in two specific genes, which were selectively favoured in the gentamicin evolution experiments, and assessed their relative fitness using competition experiments. We found that antibiotic resistance is favoured under high antibiotic selection and weak bottlenecks, but also under low antibiotic selection and severe bottlenecks. We found that the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks in combination with drug-induced selection are currently neglected key determinants of pathogen evolution and antibiotic treatment outcome.</p>
A model-based approach to characterize enzyme-mediated response to antibiotic treatments: towards a model-guided classification
<p>This dataset, taken together with the scripts at <a href="https://gitlab.inria.fr/Public/InBio/esbl-escape">https://gitlab.inria.fr/Public/InBio/esbl-escape</a>, allows one to reproduce the analyses and figures of the article "A model-based approach to characterize enzyme-mediated response to antibiotic treatments: towards a model-guided classification".</p>
Temporal and regional trends of antibiotic use in long-term aged care facilities across 39 countries, 1985-2019: systematic review and meta-analysis
<p><b>Background</b></p> <p>Antibiotic misuse is a key contributor to antimicrobial resistance and a concern in long-term aged care facilities (LTCFs). Our objectives were to: i) summarise key indicators of systemic antibiotic use and appropriateness of use, and ii) examine temporal and regional variations in antibiotic use, in LTCFs (PROSPERO registration CRD42018107125).</p> <p><b>Methods & Findings</b></p> <p>Medline and EMBASE were searched for studies published between 1990-2021 reporting antibiotic use rates in LTCFs. Random effects meta-analysis provided pooled estimates of antibiotic use rates (percentage of residents on an antibiotic on a single day [point prevalence] and over 12 months [period prevalence]; percentage of appropriate prescriptions). Meta-regression examined associations between antibiotic use, year of measurement and region. </p> <p>A total of 90 articles representing 78 studies from 39 countries with data between 1985-2019 were included. Pooled estimates of point prevalence and 12-month period prevalence were 5.2% (95% CI: 3.3-7.9; n=523,171) and 62.0% (95% CI: 54.0-69.3; n=946,127), respectively. Point prevalence varied significantly between regions (Q=224.1, df=7, p<0.001), and ranged from 2.4% (95% CI: 1.7-2.7) in Eastern Europe to 9.0% in the British Isles (95% CI: 7.6-10.5) and Northern Europe (95% CI: 7.7-10.5). Twelve-month period prevalence varied significantly between region (Q=15.1, df-3, p=0.002) and ranged from 53.9% (95% CI: 48.3-59.4) in the British Isles to 68.3% (95% CI: 63.6-72.7) in Australia. Meta-regression found no association between year of measurement and antibiotic use prevalence. The pooled estimate of the percentage of appropriate antibiotic prescriptions was 28.5% (95% CI: 10.3-58.0; n=17,245) as assessed by the McGeer criteria. Year of measurement was associated with decreasing appropriateness of antibiotic use over time (OR: 0.78, 95% CI: 0.67-0.91). The most frequently used antibiotic classes were penicillins (n=44 studies), cephalosporins (n=36), sulphonamides/trimethoprim (n=31), and quinolones (n=28). </p> <p><b>Conclusions</b></p> <p>Coordinated efforts focusing on LTCFs are required to address antibiotic misuse in LTCFs. Our analysis provides overall baseline and regional estimates for future monitoring of antibiotic use in LTCFs. </p>
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