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Record W2919872889 · doi:10.1128/msphere.00102-19

Loss of the Two-Component System TctD-TctE in <i>Pseudomonas aeruginosa</i> Affects Biofilm Formation and Aminoglycoside Susceptibility in Response to Citric Acid

2019· article· en· W2919872889 on OpenAlexafffund
Patrick K. Taylor, Li Zhang, Thien‐Fah Mah

Bibliographic record

VenuemSphere · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversity of Ottawa
FundersCystic Fibrosis CanadaNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaHealth CanadaUniversity of Ottawa
KeywordsCitric acidPseudomonas aeruginosaBiofilmTobramycinMicrobiologyAminoglycosideMutantChemistryBiochemistryBiologyBacteriaAntibioticsGeneGenetics

Abstract

fetched live from OpenAlex

Nutrient availability is an important contributor to the ability of bacteria to establish successful infections in a host. Pseudomonas aeruginosa is an opportunistic pathogen in humans causing infections that are difficult to treat. In part, its success is attributable to a high degree of metabolic versatility. P. aeruginosa is able to sense and respond to varied and limited nutrient stress in the host environment. Two-component systems are important sensors-regulators of cellular responses to environmental stresses, such as those encountered in the host. This work demonstrates that the response by the two-component system TctD-TctE to the presence of citric acid has a role in biofilm formation, aminoglycoside susceptibility, and growth in P. aeruginosa .

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 score0.004

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)0.0010.000

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations26
Published2019
Admission routes2
Has abstractyes

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