Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Estimating the association of antimicrobial resistance genes with minimum inhibitory concentration in Escherichia coli: an observational study.

The Lancet. Microbe - 1 Nov 2025

Lipworth Samuel, Chau Kevin, Oakley Sarah, Barrett Lucinda, Crook Derrick, Peto Tim, Walker A Sarah, Stoesser Nicole

Abstract excerpt

BACKGROUND: Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. We aimed to quantify the effect of acquiring specific genetic elements on minimum inhibitory concentrations (MICs) for particular antibiotic-species combinations, addressing the current scarcity of such data in existing databases. METHODS: For this observational study, we...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Anti-Bacterial Agents
Share to Drug Resistance, Bacterial

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Escherichia coli

Share to Escherichia coli Infections

Share to Microbial Sensitivity Tests

Share to United Kingdom

Share to Urinary Tract Infections

Share to Whole Genome Sequencing

Source

Identifiers

PMID: 41072458
DOI: 10.1016/j.lanmic.2025.101183

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Estimating the association of antimicrobial resistance genes with minimum inhibitory concentration in Escherichia coli: an observational study.PMID 41072458Read ↗

Estimating the effect of antimicrobial resistance genes on minimum inhibitory concentration in<i>Escherichia coli</i>Semantic proximity Read ↗

Machine learning prediction of resistance to sub-inhibitory antimicrobial concentrations from <i>Escherichia coli</i> genomesSemantic proximity Read ↗

Accessory genes define species-specific pathways to antibiotic resistanceSemantic proximity Read ↗

Exploring uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli: an observational analysis.Semantic proximity Read ↗

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.Semantic proximity Read ↗

Understanding and predicting ciprofloxacin minimum inhibitory concentration in <i>Escherichia coli</i> with machine learningSemantic proximity Read ↗

Exploring the extent of uncatalogued genetic variation in antimicrobial resistance gene families in<i>Escherichia coli</i>Semantic proximity Read ↗

Optimising machine learning prediction of minimum inhibitory concentrations in <i>Klebsiella pneumoniae</i>Semantic proximity Read ↗

Antibiotic resistance determination using Enterococcus faecium whole-genome sequences: a diagnostic accuracy study using genotypic and phenotypic data.Semantic proximity Read ↗

Precise prediction of antibiotic resistance in <i>Escherichia coli</i> from full genome sequencesSemantic proximity Read ↗

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniaeSemantic proximity Read ↗

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.