Characterization of dsRNA-induced pancreatitis model reveals the regulatory role of <i>IFN regulatory factor 2</i> ( <i>Irf2</i> ) in <i>trypsinogen5</i> gene transcription
Bibliographic record
Abstract
Mice deficient for interferon regulatory factor (Irf)2 (Irf2(-/-) mice) exhibit immunological abnormalities and cannot survive lymphocytic choriomeningitis virus infection. The pancreas of these animals is highly inflamed, a phenotype replicated by treatment with poly(I:C), a synthetic double-stranded RNA. Trypsinogen5 mRNA was constitutively up-regulated about 1,000-fold in Irf2(-/-) mice compared with controls as assessed by quantitative RT-PCR. Further knockout of IFNα/β receptor 1(Ifnar1) abolished poly(I:C)-induced pancreatitis but had no effect on the constitutive up-regulation of trypsinogen5 gene, indicating crucial type I IFN signaling to elicit the inflammation. Analysis of Ifnar1(-/-) mice confirmed type I IFN-dependent transcriptional activation of dsRNA-sensing pattern recognition receptor genes MDA5, RIG-I, and TLR3, which induced poly(I:C)-dependent cell death in acinar cells in the absence of IRF2. We speculate that Trypsin5, the trypsinogen5 gene product, leaking from dead acinar cells triggers a chain reaction leading to lethal pancreatitis in Irf2(-/-) mice because it is resistant to a major endogenous trypsin inhibitor, Spink3.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".