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Record W2992094086 · doi:10.15173/m.v1i33.1796

Identifying genes associated with biofilm production in Pseudomonas aeruginosa

2018· article· en· W2992094086 on OpenAlexaffvenue
Jessica Chee

Bibliographic record

VenueThe Meducator · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiofilmPseudomonas aeruginosaBiologyMicrobiologyGeneGeneticsBacteria

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa is an opportunistic pathogen associated with a variety of life-threatening diseases. Infections caused by P. aeruginosa can be nearly untreatable because of its multidrug-resistance. One of the characteristics of P. aeruginosa that helps it survive in high drug concentrations is its ability to form biofilms–large communities of cells encompassed by extracellular polymeric substances that defend against many antibiotics. In fact, sub-minimum inhibitory concentrations of antibiotics stimulate biofilm production. This project aims to identify genes associated with biofilm induction in P. aeruginosa by screening a transposon mutant library for mutants that fail to show increased biofilm production when exposed to sub-minimum inhibitory concentrations of cefixime, tobramycin, and thiostrepton. So far, we have identified one gene, PA2714, that encodes a predicted molybdopterin oxidoreductase required for biofilm production. Because of the strong association between biofilm production and antibiotic tolerance in P. aeruginosa, the gene identified in this screen may be a useful therapeutic target for novel antimicrobialsthat can disrupt biofilm formation.

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 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 score0.028
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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.

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

Citations0
Published2018
Admission routes2
Has abstractyes

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