ReviewAnnals of the New York Academy of Sciences2019
Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.
Review in Annals of the New York Academy of Sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Prioritization of Critical Factors for Surveillance of the Dissemination of Antibiotic Resistance inInternational journal of molecular sciences · 2023Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Genomic Insights into Carbapenem-ResistantJournal of clinical medicine · 2026Article
- Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections.Antibiotics (Basel, Switzerland) · 2025Review
- Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.ACS bio & med chem Au · 2025Article
- Risk analysis approaches for microbial ingredients in microbial-based cleaning products.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Microbial cell-free DNA-sequencing as an addition to conventional diagnostics in neonatal sepsis.Pediatric research · 2025Article
- Whole-Genome Sequencing of Resistance, Virulence and Regulation Genes in Extremely Resistant Strains ofMedical sciences (Basel, Switzerland) · 2025Article
- An explainable machine learning pipeline for prediction of antimicrobial resistance inBioinformatics advances · 2025Article
- Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae.Scientific reports · 2024Article
- Monitoring of Pseudomonas aeruginosa mutational resistome dynamics using an enrichment panel for direct sequencing of clinical samples.EBioMedicine · 2024Article
- Tracking of Bacteriophage Predation onSensors (Basel, Switzerland) · 2024Article
- Genome-Scale Characterization ofInternational journal of molecular sciences · 2023Article
- Mutation Analysis in Regulator DNA-Binding Regions for Antimicrobial Efflux Pumps in 17,000Microorganisms · 2023Article
- Potassium Chloride, Sodium Lactate and Sodium Citrate Impaired the Antimicrobial Resistance and Virulence ofMolecules (Basel, Switzerland) · 2023Article
- Mechanisms of Antibiotic and Biocide Resistance That Contribute toBiomedicines · 2023Review
- Article
- β-lactam Resistance inPathogens (Basel, Switzerland) · 2021Review
- Predictive Antibiotic Susceptibility Testing by Next-Generation Sequencing for Periprosthetic Joint Infections: Potential and Limitations.Biomedicines · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.