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Record W4319656977 · doi:10.1093/lambio/ovad023

Colistin resistance mechanisms in Gram-negative bacteria: a Focus on <i>Escherichia coli</i>

2023· review· en· W4319656977 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.

Bibliographic record

VenueLetters in Applied Microbiology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEscherichia coliBacteriaMicrobiologyColistinGram-negative bacteriaMCR-1BiologyEnterobacteriaceaeFocus (optics)Antibiotic resistanceAntibioticsGeneticsPhysicsGene

Abstract

fetched live from OpenAlex

Multidrug-resistant (MDR) Escherichia coli strains have rapidly increased worldwide, and effective antibiotic therapeutic options are becoming more restricted. As a polymyxin antibiotic, colistin has a long history of usage, and it is used as a final line of treatment for severe infections by Gram-negative bacteria (GNB) with high-level resistance. However, its application has been challenged by the emergence of E. coli colistin resistance. Hence, determining the mechanism that confers colistin resistance is crucial for monitoring and controlling the dissemination of colistin-resistant E. coli strains. This comprehensive review summarizes colistin resistance mechanisms in E. coli strains and concentrates on the history, mode of action, and therapeutic implications of colistin. We have mainly focused on the fundamental mechanisms of colistin resistance that are mediated by chromosomal or plasmid elements and discussed major mutations in the two-component systems (TCSs) genes and plasmids that transmit the mobilized colistin resistance resistant genes in E. coli strains.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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