<i>Helicobacter pylori</i> Activates Toll-Like Receptor 4 Expression in Gastrointestinal Epithelial Cells
Bibliographic record
Abstract
Helicobacter pylori activates the transcription factor NF-kappaB, leading to proinflammatory cytokine production by gastric epithelial cells. However, the receptors for the initial bacterial interaction with host cells which activate downstream signaling events have not been completely defined. Recently, it has been shown that microbial components activate Toll-like receptors (TLRs), thereby leading to AP-1- and NF-kappaB-dependent transcription and resulting in the production of proinflammatory cytokines. The aim of this study was to determine whether H. pylori activates TLR4. Reverse transcription-PCR showed that both type I and type II H. pylori clinical isolates induced TLR4 mRNA expression in AGS cells compared with that by uninfected controls. H. pylori upregulated TLR4 protein expression in two gastric epithelial cell lines (AGS and MKN45) and one intestinal epithelial cell line (T84). Monoclonal TLR4 antibody inhibited lipopolysaccharide-induced interleukin-8 secretion from THP-1 macrophages but not from gastric epithelial cells infected with H. pylori. H. pylori demonstrated increased adherence to CHO TLR4-transfected cells compared with that to both CHO TLR2-transfected and nontransfected CHO cells (P < 0.01). These results indicate that H. pylori activates TLR4 expression in epithelial cells and that TLR4 can serve as a receptor for H. pylori binding.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".