The CD4+ T Cell-Mediated IFN-γ Response to <i>Helicobacter</i> Infection Is Essential for Clearance and Determines Gastric Cancer Risk
Bibliographic record
Abstract
Chronic infection with the bacterial pathogen Helicobacter pylori is a risk factor for the development of gastric cancer, yet remains asymptomatic in the majority of individuals. We report here that the C57BL/6 mouse model of experimental infection with the closely related Helicobacter felis recapitulates this wide range in host susceptibility. Although the majority of infected animals develop premalignant lesions such as gastric atrophy, compensatory epithelial hyperplasia, and intestinal metaplasia, a subset of mice is completely protected from preneoplasia. Protection is associated with a failure to mount an IFN-gamma response to the infection and with a concomitant high Helicobacter burden. Using a vaccine model as well as primary infection and adoptive transfer models, we demonstrate that IFN-gamma, secreted predominantly by CD4(+)CD25(-) effector T(H) cells, is essential for Helicobacter clearance, but at the same time mediates the formation of preneoplastic lesions. We further provide evidence that IFN-gamma triggers a common transcriptional program in murine gastric epithelial cells in vitro and in vivo and induces their preferential transformation to the hyperplastic phenotype. In summary, our data suggest a dual role for IFN-gamma in Helicobacter pathogenesis that could be the basis for the differential susceptibility to H. pylori-induced gastric pathology in the human population.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".