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Record W2909118422 · doi:10.1038/s41598-018-37095-4

VacA promotes CagA accumulation in gastric epithelial cells during Helicobacter pylori infection

2019· article· en· W2909118422 on OpenAlexafffund
Majd Abdullah, Laura Greenfield, Dana M. Bronte-Tinkew, Mariana Capurro, David Rizzuti, Nicola L. Jones

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsCagAHelicobacter pyloriVirulenceAutophagyVirulence factorMicrobiologyBiologyProteasomePathogenicity islandGeneCell biologyApoptosisGenetics

Abstract

fetched live from OpenAlex

Helicobacter pylori (H. pylori) is the causative agent of gastric cancer, making it the only bacterium to be recognized as a Class I carcinogen by the World Health Organization. The virulence factor cytotoxin associated gene A (CagA) is a known oncoprotein that contributes to the development of gastric cancer. The other major virulence factor vacuolating cytotoxin A (VacA), disrupts endolysosomal vesicular trafficking and impairs the autophagy pathway. Studies indicate that there is a functional interplay between these virulence factors by unknown mechanisms. We show that in the absence of VacA, both host-cell autophagy and the proteasome degrade CagA during infection with H. pylori. In the presence of VacA, CagA accumulates in gastric epithelial cells. However, VacA does not affect proteasome function during infection with H. pylori suggesting that VacA-disrupted autophagy is the predominant means by which CagA accumulates. Our studies support a model where in the presence of VacA, CagA accumulates in dysfunctional autophagosomes providing a possible explanation for the functional interplay of VacA and CagA.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

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