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Record W2936059048 · doi:10.3390/ijms20071781

Two Component Regulatory Systems and Antibiotic Resistance in Gram-Negative Pathogens

2019· review· en· W2936059048 on OpenAlexafffund
Anjali Y. Bhagirath, Yanqi Li, Rakesh Patidar, Katherine Yerex, Xiaoxue Ma, Ayush Kumar, Kangmin Duan

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAcinetobacter baumanniiPseudomonas aeruginosaAntibiotic resistanceAntibioticsBiologyKlebsiella pneumoniaeMicrobiologyAntimicrobialDrug resistanceAcinetobacterGram-negative bacteriaBacteriaGeneGeneticsEscherichia coli

Abstract

fetched live from OpenAlex

Gram-negative pathogens such as Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa are the leading cause of nosocomial infections throughout the world. One commonality shared among these pathogens is their ubiquitous presence, robust host-colonization and most importantly, resistance to antibiotics. A significant number of two-component systems (TCSs) exist in these pathogens, which are involved in regulation of gene expression in response to environmental signals such as antibiotic exposure. While the development of antimicrobial resistance is a complex phenomenon, it has been shown that TCSs are involved in sensing antibiotics and regulating genes associated with antibiotic resistance. In this review, we aim to interpret current knowledge about the signaling mechanisms of TCSs in these three pathogenic bacteria. We further attempt to answer questions about the role of TCSs in antimicrobial resistance. We will also briefly discuss how specific two-component systems present in K. pneumoniae, A. baumannii, and P. aeruginosa may serve as potential therapeutic targets.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.328
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations152
Published2019
Admission routes2
Has abstractyes

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