A high-fat diet modulates iron metabolism but does not promote liver fibrosis in hemochromatotic Hjv<sup>−/−</sup> mice
Bibliographic record
Abstract
Hemojuvelin (Hjv) is a membrane protein that controls body iron metabolism by enhancing signaling to hepcidin. Hjv mutations cause juvenile hemochromatosis, a disease of systemic iron overload. Excessive iron accumulation in the liver progressively leads to inflammation and disease, such as fibrosis, cirrhosis, or hepatocellular cancer. Fatty liver (steatosis) may also progress to inflammation (steatohepatitis) and liver disease, and iron is considered as pathogenic cofactor. The aim of this study was to investigate the pathological implications of parenchymal iron overload due to Hjv ablation in the fatty liver. Wild-type (WT) and Hjv(-/-) mice on C57BL/6 background were fed a standard chow, a high-fat diet (HFD), or a HFD supplemented with 2% carbonyl iron (HFD+Fe) for 12 wk. The animals were analyzed for iron and lipid metabolism. As expected, all Hjv(-/-) mice manifested higher serum and hepatic iron and diminished hepcidin levels compared with WT controls. The HFD reduced iron indexes and promoted liver steatosis in both WT and Hjv(-/-) mice. Notably, steatosis was attenuated in Hjv(-/-) mice on the HFD+Fe regimen. Hjv(-/-) animals gained less body weight and exhibited reduced serum glucose and cholesterol levels. Histological and ultrastructural analysis revealed absence of iron-induced inflammation or liver fibrosis despite early signs of liver injury (expression of α-smooth muscle actin). We conclude that parenchymal hepatic iron overload does not suffice to trigger progression of liver steatosis to steatohepatitis or fibrosis in C57BL/6 mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".