MétaCan
Menu
Back to cohort
Record W2914802931 · doi:10.3389/fmicb.2019.00049

Signal Transduction Proteins in Acinetobacter baumannii: Role in Antibiotic Resistance, Virulence, and Potential as Drug Targets

2019· review· en· W2914802931 on OpenAlexafffund
P. Malaka De Silva, Ayush Kumar

Bibliographic record

VenueFrontiers in Microbiology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of ManitobaCystic Fibrosis Foundation
KeywordsAcinetobacter baumanniiVirulenceBiologyDrug resistanceAntibioticsAntibiotic resistancePathogenDrugMicrobiologyPhenotypeSignal transductionComputational biologyBacteriaGeneticsGenePharmacologyPseudomonas aeruginosa

Abstract

fetched live from OpenAlex

Acinetobacter baumannii is a notorious pathogen in health care settings around the world, primarily due to high resistance to antibiotics. A. baumannii also shows an impressive capability to adapt to harsh conditions in clinical settings, which contributes to its persistence in such conditions. Following their traditional role, the Two Component Systems (TCSs) present in A. baumannii play a crucial role in sensing and adapting to the changing environmental conditions. This provides A. baumannii with a greater chance of survival even in unfavourable conditions. Since all the TCSs characterised to date in A. baumannii play a role in its antibiotic resistance and virulence, understanding the underlying molecular mechanisms behind TCSs can help with a better understanding of the pathways that regulate these phenotypes. This can also guide efforts to target TCSs as novel drug targets. In this review, we discuss the roles of TCSs in A. baumannii, their molecular mechanisms, and most importantly, the potential of using small molecule inhibitors of TCSs as potential novel drug 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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.

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

Citations75
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in MicrobiologySame topicAntibiotic Resistance in BacteriaFrench-language works237,207