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Record W2142316694 · doi:10.1158/0008-5472.can-06-4746

Regulation of <i>Helicobacter pylori cagA</i> Expression in Response to Salt

2007· article· en· W2142316694 on OpenAlexaff
John T. Loh, Victor J. Torres, Timothy L. Cover

Bibliographic record

VenueCancer Research · 2007
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsImmunovaccine (Canada)
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsCagAHelicobacter pyloriSecretionTyrosine phosphorylationBiologyGastric mucosaTyrosineMicrobiologyStomachGeneEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Helicobacter pylori infection and a high dietary salt intake are risk factors for the development of gastric adenocarcinoma. In this study, we tested the hypothesis that high salt concentrations might alter gene expression in H. pylori. Transcriptional profiling experiments indicated that the expression of multiple H. pylori genes, including cagA, was regulated in response to the concentrations of sodium chloride present in the bacterial culture medium. Increased expression of cagA in response to high salt conditions was confirmed by the use of transcriptional reporter strains and by immunoblotting. H. pylori CagA is translocated into gastric epithelial cells via a type IV secretion pathway, and on entry into target cells, CagA undergoes tyrosine phosphorylation and causes multiple cellular alterations. Coculture of gastric epithelial cells with H. pylori grown under high salt conditions resulted in increased tyrosine-phosphorylated CagA and increased secretion of interleukin-8 by the epithelial cells compared with coculture of the cells with H. pylori grown under low salt conditions. Up-regulation of H. pylori cagA expression in response to high salt concentrations may be a factor that contributes to the development of gastric adenocarcinoma.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.398
Teacher spread0.339 · 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 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

Citations145
Published2007
Admission routes1
Has abstractyes

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