Interleukin-37 protects against acinar cell pyroptosis in acute pancreatitis
Bibliographic record
Abstract
Acute pancreatitis (AP) is a local and/or systemic inflammatory disease that starts with acinar cell injury and necrosis; it has no effective medical treatment and thus remains a life-threatening condition. Interleukin-37 (IL-37), a natural immunomodulator, has demonstrated an antiinflammatory effect; however, the role of IL-37 in AP remains unknown. The serum IL-37 levels of 39 healthy controls and 94 patients with AP were measured. Cholecystokinin was applied to induce pancreatic acinar cell injury in vitro. Classical experimental AP models, such as caerulein, l-arginine, and taurolithocholic acid 3-sulfate disodium salt, were included in the in vivo study. A transgenic mouse model with the IL-37 gene and administration of recombinant IL-37 were used to further investigate the function of IL-37 in AP. Pancreas-specific gasdermin D-knockout (GSDMD-knockout) mice were used to explore the protective mechanism of IL-37. Our results showed that serum IL-37 levels in humans were negatively correlated with the severity of AP. Furthermore, IL-37-transgenic mice and supplementation with recombinant IL-37 could both protect against AP. Mechanistically, IL-37 was able to suppress pyroptosis of injured acinar cells, and specific depletion of GSDMD in the pancreas counteracted the protective effect of IL-37. Our study demonstrates that IL-37 protects against acinar cell pyroptosis in AP.
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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) | 0.001 | 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) | 0.001 | 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".