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Record W1991601642 · doi:10.1099/mic.0.046870-0

Parallel evolution and local differentiation in quinolone resistance in Pseudomonas aeruginosa

2011· review· en· W1991601642 on OpenAlexafffund
Alex Wong, Rees Kassen

Bibliographic record

VenueMicrobiology · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsOntario GenomicsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsDNA gyrasePseudomonas aeruginosaEffluxBiologyQuinoloneMicrobiologyTopoisomerase IVAntibiotic resistanceGeneticsMutationAntibioticsPathogenDrug resistanceBacteriaGeneEscherichia coli

Abstract

fetched live from OpenAlex

The emergence and spread of antibiotic resistance in pathogens is a major impediment to the control of microbial disease. Here, we review mechanisms of quinolone resistance in Pseudomonas aeruginosa, an important nosocomial pathogen and a major cause of morbidity in cystic fibrosis (CF) patients. In this quantitative literature review, we find that mutations in DNA gyrase A, the primary target of quinolones in Gram-negative bacteria, are the most common resistance mutations identified in clinical samples of all origins, in keeping with previous observations. However, the identities of non-gyrase resistance mutations vary systematically between samples isolated from CF patients and those isolated from acute infections. CF-derived strains tend to harbour mutations in the efflux pump regulator nfxB, while non-CF strains tend to bear mutations in the efflux regulator mexR or in parC, which encodes one of two subunits of DNA topoisomerase IV. We suggest that differences in resistance mechanisms between CF and non-CF strains result either from local adaptation to different sites of infection or from differences in mutational processes between different environments. We further discuss the therapeutic implications of local differentiation in resistance mechanisms to a common antibiotic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.257
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations60
Published2011
Admission routes2
Has abstractyes

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