Serpinb3 is Overexpressed in the Liver in Presence of Iron Overload
Bibliographic record
Abstract
Iron overload results in cellular toxicity, tissue injury, organ fibrosis and increased risk of neoplastic transformation. SerpinB3 is a serine protease inhibitor overexpressed in the liver in oxidative stress conditions, able to induce fibrosis and increased risk of malignant transformation. Aim of the present study was to assess the effect of iron overload on SerpinB3 expression in the liver using in vivo and in vitro models.The expression of Serpinb3 was assessed in the liver of hemojuvelin knockout mice (Hjv-/-), an established model of hereditary hemochromatosis, and of wild type control mice, following dietary or pharmacological iron manipulation. To assess the direct effect of iron in vitro, cell lines were treated with different concentration of hemin or with an iron chelator.Hepatic Serpinb3 mRNA and protein were highly expressed in Hjv-/- mice, but not in wild type controls fed with a standard diet. Serpinb3 became detectable in wild type mice fed with a high iron diet or injected with iron dextran; these treatments further induced Serpinb3 expression in Hjv-/- mice. Livers expressing Serpinb3 showed a positive staining also for HIF-2α in the same areas. Hemin promoted induction of SerpinB3 mRNA in HeLa and HA22T/VGH cells, but a mild stimulation of SerpinB3 promoter activity in HeLa and Huh7 cells. In conclusion, Serpinb3 is strongly induced by iron in the mouse liver. The molecular link between iron, ROS and SerpinB3 seems to be HIF-2α, which is induced by iron overload and was previously found capable of up-regulating SerpinB3 at the transcriptional level.
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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) | 0.002 | 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".