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Record W4381950600 · doi:10.1016/j.engmic.2023.100104

The diverse landscape of AB5-type toxins

2023· review· en· W4381950600 on OpenAlexafffund
Paris I. Brown, Adaobi Ojiakor, Antonio J. Chemello, Casey C. Fowler

Bibliographic record

VenueEngineering Microbiology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsShigella dysenteriaeVirulenceVibrio choleraeBiologyCholera toxinToxinMicrobiologySalmonella entericaShiga toxinProtein subunitSalmonellaEscherichia coliBacteriaGeneticsGene

Abstract

fetched live from OpenAlex

AB5-type toxins are a group of secreted protein toxins that are central virulence factors for bacterial pathogens such as Shigella dysenteriae, Vibrio cholerae, Bordetella pertussis, and certain lineages of pathogenic Escherichia coli and Salmonella enterica. AB5 toxins are composed of an active (A) subunit that manipulates host cell biology in complex with a pentameric binding/delivery (B) subunit that mediates the toxin's entry into host cells and its subsequent intracellular trafficking. Broadly speaking, all known AB5-type toxins adopt similar structural architectures and employ similar mechanisms of binding, entering and trafficking within host cells. Despite this, there is a remarkable amount of diversity amongst AB5-type toxins; this includes different toxin families with unrelated activities, as well as variation within families that can have profound functional consequences. In this review, we discuss the diversity that exists amongst characterized AB5-type toxins, with an emphasis on the genetic and functional variability within AB5 toxin families, how this may have evolved, and its impact on human disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.029
GPT teacher head0.306
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2023
Admission routes2
Has abstractyes

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