Hepcidin knockout mice spontaneously develop chronic pancreatitis owing to cytoplasmic iron overload in acinar cells
Bibliographic record
Abstract
Iron is both an essential and a potentially toxic element, and its systemic homeostasis is controlled by the iron hormone hepcidin. Hepcidin binds to the cellular iron exporter ferroportin, causes its degradation, and thereby diminishes iron uptake from the intestine and the release of iron from macrophages. Given that hepcidin-resistant ferroportin mutant mice show exocrine pancreas dysfunction, we analysed pancreata of aging hepcidin knockout (KO) mice. Hepcidin and Hfe KO mice were compared with wild-type (WT) mice kept on standard or iron-rich diets. Twelve-month-old hepcidin KO mice were subjected to daily minihepcidin PR73 treatment for 1 week. Six-month-old hepcidin KO mice showed cytoplasmic acinar iron overload and mild pancreatitis, together with elevated expression of the iron uptake mediators DMT1 and Zip14. Acinar atrophy, massive macrophage infiltration, fatty changes and pancreas fibrosis were noted in 1-year-old hepcidin KO mice. As an underlying mechanism, 6-month-old hepcidin KO mice showed increased pancreatic oxidative stress, with elevated DNA damage, apoptosis and activated nuclear factor-κB (NF-κB) signalling. Neither iron overload nor pancreatic damage was observed in WT mice fed iron-rich diet or in Hfe KO mice. Minihepcidin application to hepcidin KO mice led to an improvement in general health status and to iron redistribution from acinar cells to macrophages. It also resulted in decreased NF-κB activation and reduced DNA damage. In conclusion, loss of hepcidin signalling in mice leads to iron overload-induced chronic pancreatitis that is not seen in situations with less severe iron accumulation. The observed tissue injury can be reversed by hepcidin supplementation. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".