ArticleAntibiotics (Basel, Switzerland)2023
QUIRMIA-A Phenotype-Based Algorithm for the Inference of Quinolone Resistance Mechanisms in
Article in Antibiotics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Genomic determinants of fluoroquinolone resistance in Escherichia coli in Nigeria: dominance of QRDR mutations and limited contribution of PMQR in a cross-sectional study.BMC medical genomics · 2026Article
- Resistance Mechanisms of Fluoroquinolone inAntibiotics (Basel, Switzerland) · 2025Article
- Unraveling the Resistome, Virulome, and Pathogenicity ofInternational journal of microbiology · 2025Article
- High Genetic Diversity in Third-Generation Cephalosporin-ResistantPathogens (Basel, Switzerland) · 2024Article
- Antibacterial activity and mechanism of luteolin isolated fromFrontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectivesQuinolone resistance in
methodsPhenotypic susceptibility of 553 clinical
resultsQUIRMIA-based inference of resistance mechanisms and sequencing data were highly congruent (542/553, 98%). In contrast, EUCAST-based classification with its binary classification into "susceptible" and "resistant" isolates failed to recognize and properly categorize low-level resistant isolates.
conclusionsQUIRMIA provides a coherent genotype/phenotype categorization and may be integrated in the EUCAST expert rule set, thereby enabling reliable detection of low-level resistant isolates, which may help to better predict outcome and to prevent the emergence of clinical resistance.
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Identifiers
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Registered trials
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