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Record W4383711299 · doi:10.1111/1348-0421.13089

Knock‐out of multidrug efflux pump MexXY‐OprM results in increased susceptibility to antimicrobial peptides in <i>Pseudomonas aeruginosa</i>

2023· article· en· W4383711299 on OpenAlexafffund
Anke Neidig, Nikola Strempel, Nadine B Waeber, Waleska Stephanie da Cruz Nizer, Joerg Overhage

Bibliographic record

VenueMicrobiology and Immunology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsCarleton University
FundersKarlsruhe Institute of TechnologyCarleton University
KeywordsEffluxPseudomonas aeruginosaBiologyMultiple drug resistanceMicrobiologyAntimicrobialAntibioticsAntimicrobial peptidesAntibiotic resistanceBacteriaGenetics

Abstract

fetched live from OpenAlex

Abstract Multidrug efflux systems of the resistance‐nodulation‐cell division family play a crucial role in resistance of Pseudomonas aeruginosa to a large variety of antibiotics. Here, we investigated the role of clinically relevant efflux pumps MexAB−OprM, MexCD−OprJ, and MexXY−OprM in resistance against different cationic antimicrobial peptides (AMPs). Our results indicate that a knock‐out in efflux pump MexXY‐OprM increased susceptibility to some AMPs by two‐ to eightfold. Our data suggest a contribution of MexXY‐OprM in resistance to certain AMPs in P. aeruginosa, which should be considered in the future development of new and highly active antimicrobial peptides to fight multidrug resistant infections.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes2
Has abstractyes

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