ReviewAntibiotics (Basel, Switzerland)2017
Establishing Genotype-to-Phenotype Relationships in Bacteria Causing Hospital-Acquired Pneumonia: A Prelude to the Application of Clinical Metagenomics.
Review in Antibiotics (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Building the Tools for Enhanced Waterway Surveillance: Prediction of Antimicrobial-ResistantJournal of the Royal Society of New Zealand · 2026Article
- No evidence of multidrug-resistantMicrobiology spectrum · 2025Observational
- Shotgun Metagenomic Sequencing Analysis as a Diagnostic Strategy for Patients with Lower Respiratory Tract Infections.Microorganisms · 2025Article
- Genomics for antimicrobial resistance-progress and future directions.Antimicrobial agents and chemotherapy · 2025Review
- Article
- Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis.International journal of molecular sciences · 2024Review
- Optimising machine learning prediction of minimum inhibitory concentrations inMicrobial genomics · 2024Article
- Predicting tigecycline susceptibility in multidrug-resistant Klebsiella species and Escherichia coli strains of environmental origin.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Article
- Outbreak report of polymyxin-carbapenem-resistant Klebsiella pneumoniae causing untreatable infections evidenced by synergy tests and bacterial genomes.Scientific reports · 2023Article
- An ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance.Nature communications · 2023Article
- Advantages and challenges of metagenomic sequencing for the diagnosis of pulmonary infectious diseases.The clinical respiratory journal · 2022Review
- Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units.Genome medicine · 2021Article
- Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles.Scientific reports · 2021Article
- Case Report: About a Case of Hyperammonemia Syndrome Following Lung Transplantation: Could Metagenomic Next-Generation Sequencing Improve the Clinical Management?Frontiers in medicine · 2021Article
- Multidrug Resistance (MDR) and Collateral Sensitivity in Bacteria, with Special Attention to Genetic and Evolutionary Aspects and to the Perspectives of Antimicrobial Peptides-A Review.Pathogens (Basel, Switzerland) · 2020Review
- MEGARes 2.0: a database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data.Nucleic acids research · 2020Article
- Bacterial Genome Wide Association Studies (bGWAS) and Transcriptomics Identifies Cryptic Antimicrobial Resistance Mechanisms inFrontiers in public health · 2020Article
- Direct molecular detection of amoxicillin-susceptibleJournal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada · 2019Article
- Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical metagenomics (CMg), referred to as the application of next-generation sequencing (NGS) to clinical samples, is a promising tool for the diagnosis of hospital-acquired pneumonia (HAP). Indeed, CMg allows identifying pathogens and antibiotic resistance genes (ARGs), thereby providing the information required for the optimization of the antibiotic regimen. Hence, provided that CMg would be faster than conventional culture, the probabilistic regimen used in HAP could be tailored faster, which should lead to an expected decrease of mortality and morbidity. While the inference of the antibiotic susceptibility testing from metagenomic or even genomic data is challenging, a limited number of antibiotics are used in the probabilistic regimen of HAP (namely beta-lactams, aminoglycosides, fluoroquinolones, glycopeptides and oxazolidinones). Accordingly, based on the perspective of applying CMg to the early diagnostic of HAP, we aimed at reviewing the performances of whole genomic sequencing (WGS) of the main HAP-causing bacteria (Enterobacteriaceae,
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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.