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Record W4294754588 · doi:10.1128/iai.00223-22

Iron Acquisition Mechanisms and Their Role in the Virulence of Acinetobacter baumannii

2022· review· en· W4294754588 on OpenAlexafffund
Shoshana Cook-Libin, Ellen M. E. Sykes, Vanessa Kornelsen, Ayush Kumar

Bibliographic record

VenueInfection and Immunity · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAcinetobacter baumanniiSiderophoreBiofilmBiologyVirulenceMicrobiologyHemeBacteriaBiochemistryPseudomonas aeruginosaEnzymeGenetics

Abstract

fetched live from OpenAlex

Iron is an essential element for survival of most organisms. One mechanism of host defense is to tightly chelate iron to several proteins to limit its extracellular availability. This has forced pathogens such as Acinetobacter baumannii to adapt mechanisms for the acquisition and utilization of iron even in iron-limiting conditions. A. baumannii uses a variety of iron acquisition strategies to meet its iron requirements. It can lyse erythrocytes to harvest the heme molecules, use iron-chelating siderophores, and use outer membrane vesicles to acquire iron. Iron acquisition pathways, in general, have been seen to affect many other virulence factors such as cell adherence, cell motility, and biofilm formation. The knowledge gained from research on iron acquisition led to the synthesis of the antibiotic cefiderocol, which uses iron uptake pathways for entry into the cell with some success as a novel cephalosporin. Understanding the mechanisms of iron acquisition of A. baumannii allows for insight into clinical infections and offer potential targets for novel antibiotics or potentiators of current drugs.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.451

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 head0.276
Teacher spread0.257 · 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

Citations83
Published2022
Admission routes2
Has abstractyes

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