MétaCan
Menu
Back to cohort
Record W2209519554 · doi:10.1128/jb.00906-15

Pathogenic Acinetobacter: from the Cell Surface to Infinity and Beyond

2015· review· en· W2209519554 on OpenAlexafffund
Brent S. Weber, Christian M. Harding, Mario F. Feldman

Bibliographic record

VenueJournal of Bacteriology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Alberta
FundersWashington University School of Medicine in St. LouisNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAcinetobacterBiologyMicrobiologyAcinetobacter baumanniiVirulenceMultiple drug resistanceBiofilmBacterial adhesinAntimicrobialColonizationAntibiotic resistanceDrug resistanceAntibioticsBacteriaGenePseudomonas aeruginosaGenetics

Abstract

fetched live from OpenAlex

The genus Acinetobacter encompasses multiple nosocomial opportunistic pathogens that are of increasing worldwide relevance because of their ability to survive exposure to various antimicrobial and sterilization agents. Among these, Acinetobacter baumannii, Acinetobacter nosocomialis, and Acinetobacter pittii are the most frequently isolated in hospitals around the world. Despite the growing incidence of multidrug-resistant Acinetobacter spp., little is known about the factors that contribute to pathogenesis. New strategies for treating and managing infections caused by multidrug-resistant Acinetobacter strains are urgently needed, and this requires a detailed understanding of the pathobiology of these organisms. In recent years, some virulence factors important for Acinetobacter colonization have started to emerge. In this review, we focus on several recently described virulence factors that act at the bacterial surface level, such as the capsule, O-linked protein glycosylation, and adhesins. Furthermore, we describe the current knowledge regarding the type II and type VI secretion systems present in these 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.

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 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 score0.906
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations126
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of BacteriologySame topicAntibiotic Resistance in BacteriaFrench-language works237,207