<b><i>Helicobacter pylori</i></b>Induces Plasminogen Activator Inhibitor 2 in Gastric Epithelial Cells through Nuclear Factor-κB and RhoA
Bibliographic record
Abstract
The gastric pathogen Helicobacter pylori is associated with a progression to gastric cancer. The specific targets of H. pylori that might influence this progression are still unclear. Previous studies indicated that the gastric hormone gastrin, which may be increased in H. pylori infection, stimulates gastric expression of plasminogen activator inhibitor (PAI)-2, which is an inhibitor of the urokinase plasminogen activator and has previously been shown to be increased in gastric adenocarcinoma. Here, we report that H. pylori also increases PAI-2 expression. In gastric biopsies of H. pylori-positive subjects there was increased PAI-2, including subjects with plasma gastrin concentrations in the normal range. PAI-2 was expressed mainly in chief and mucous cells. In a gastric cancer cell line (AGS), H. pylori increased PAI-2 expression, which was associated with inhibition of H. pylori-stimulated cell invasion and apoptosis. The induction of PAI-2 by H. pylori was mediated by release of interleukin-8 and activation of cyclooxygenase-2, and interestingly, gastrin stimulated PAI-2 expression by similar paracrine pathways. The activation of NFkappaB was required for interleukin-8 and cyclooxygenase-2 activation but did not occur in cells responding to these paracrine mediators. The data suggest that induction of PAI-2 is a specific target in H. pylori infection, mediated at least partly by paracrine factors; induction of PAI-2 inhibits cell invasion and apoptosis and is a candidate for influencing the progression to gastric cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".