The Dose-Dependent Effects of Chronic Iron Overload on the Production of Oxygen Free Radicals and Vitamin E Concentrations in the Liver of a Murine Model
Bibliographic record
Abstract
Genetic disorders of iron metabolism such as primary and secondary hemochromatosis affect thousands of individuals worldwide and are major causes of liver dysfunction, morbidity, and mortality. Although the exact mechanism of hepatic injury associated with these genetic disorders is not fully understood, the propagation of excess concentrations of iron-catalyzed oxygen free radicals (OFRs) may play a role. The authors hypothesized that chronic iron burden would result in dose-dependent (a) increases in hepatic iron stores, (b) increases in hepatic OFR-mediated hepatic cellular injury as quantified by the cytotoxic aldehydes malondialdehyde (MDA) and hexanal, and (c) decreases in protective antioxidant reserve status as quantified by plasma vitamin E (alpha-tocopherol) levels in a murine model. Twenty B(6)D(2)F1 male mice were randomized to the (a) saline control (0.05 mL intraperiotoneal [i.p.]/mouse/day, n = 5), (b) 100 mg total iron burden (n = 5), (c) 200 mg total iron burden (n = 5), or (d) 400 mg total iron burden (n = 5) group. Iron burden was achieved by daily injections of iron dextran (Imferon, 0.05 mL i.p./mouse/day). In comparison to control mice and in support of the hypothesis, the authors observed significant dose-dependent increases in total hepatic iron burden (p < .001) with corresponding increases in MDA and hexanal concentrations (p < .001) and decreases in the protective plasma antioxidant vitamin E (p < .001). These findings suggest that iron-catalyzed OFR-mediated damage may play a role in damaging the liver in chronic states of iron burden.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".