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Record W2136414961 · doi:10.1086/367896

Gastric Mucosal Recognition of<i>Helicobacter pylori</i>Is Independent of Toll‐Like Receptor 4

2003· article· en· W2136414961 on OpenAlexaff
Fredrik Bäckhed, Bachra Rokbi, Elisabeth Torstensson, Ying Zhao, Christina Nilsson, Delphine Seguin, Staffan Normark, A.M.J. Buchan, Agneta Richter‐Dahlfors

Bibliographic record

VenueThe Journal of Infectious Diseases · 2003
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHelicobacter pyloriBiologyGastric mucosaTLR4Immune systemCytokineToll-like receptorCell cultureImmunologyTumor necrosis factor alphaMucinReceptorInterleukin 8EpitheliumGastritisHelicobacterInnate immune systemStomach

Abstract

fetched live from OpenAlex

Little is known about the interactions between Helicobacter pylori, which specializes in colonizing the mucin layer that covers the gastric mucosa, and primary gastric epithelial cells. The expression pattern of Toll-like receptors (TLRs) in primary gastric epithelial cells and cell lines was compared. Primary cells did not express TLR4, whereas all cell lines expressed a nonsignaling form of TLR4. Because other cells within the mucosa expressed TLR4, it was next investigated whether H. pylori can be recognized by TLR4--they cannot. Moreover, H. pylori infection of primary cells induced a regulated production of interleukin (IL)-6, IL-8, and tumor necrosis factor-alpha, whereas infection of cell lines only resulted in IL-8 production. The cytokine production in all cell types was strictly cag dependent. These findings indicate that, although the epithelium is important in directing the immune response against H. pylori infections, the response is independent of TLR4.

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 score0.006

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)0.0020.001

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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

Citations137
Published2003
Admission routes1
Has abstractyes

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