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Record W2984504359 · doi:10.1128/msystems.00465-19

Chemogenomic Screen for Imipenem Resistance in Gram-Negative Bacteria

2019· article· en· W2984504359 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuemSystems · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsBiologyMutagenesisMicrobiologyGeneticsGeneEscherichia coliKlebsiella pneumoniaeWhole genome sequencingBacteriaPseudomonas aeruginosaAntibiotic resistanceImipenemEnterobacteriaceaeBacterial outer membraneGenomeMutation

Abstract

fetched live from OpenAlex

Gram-negative carbapenem-resistant bacteria are a major threat to global health. The use of genome-wide screening approaches to probe for genes or mutations enabling resistance can lead to identification of molecular markers for diagnostics applications. We describe an approach called Mut-Seq that couples chemical mutagenesis and next-generation sequencing for studying resistance to imipenem in the Gram-negative bacteria Escherichia coli , Klebsiella pneumoniae , and Pseudomonas aeruginosa . The use of this approach highlighted shared and species-specific responses, and the role in resistance of a number of genes involved in membrane biogenesis, transcription, and signal transduction was functionally validated. Interestingly, some of the genes identified were previously considered promising therapeutic targets. Our genome-wide screen has the potential to be extended outside drug resistance studies and expanded to other organisms.

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.

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 score0.364
Threshold uncertainty score0.710

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 head0.009
GPT teacher head0.237
Teacher spread0.229 · 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