Cp/Heph mutant mice have iron‐induced neurodegeneration diminished by deferiprone
Bibliographic record
Abstract
Brain iron accumulates in several neurodegenerative diseases and can cause oxidative damage, but mechanisms of brain iron homeostasis are incompletely understood. Patients with mutations in the cellular iron-exporting ferroxidase ceruloplasmin (Cp) have brain iron accumulation causing neurodegeneration. Here, we assessed the brains of mice with combined mutation of Cp and its homolog hephaestin. Compared to single mutants, brain iron accumulation was accelerated in double mutants in the cerebellum, substantia nigra, and hippocampus. Iron accumulated within glia, while neurons were iron deficient. There was loss of both neurons and glia. Mice developed ataxia and tremor, and most died by 9 months. Treatment with the oral iron chelator deferiprone diminished brain iron levels, protected against neuron loss, and extended lifespan. Ferroxidases play important, partially overlapping roles in brain iron homeostasis by facilitating iron export from glia, making iron available to neurons. Above: Iron (Fe) normally moves from capillaries to glia to neurons. It is exported from the glia by ferroportin (Fpn) with ferroxidases ceruloplasmin (Cp) and/or Hephaestin (Heph). Below: In mice with mutation of Cp and Heph, iron accumulates in glia, while neurons have low iron levels. Both neurons and glia degenerate and mice become ataxic unless given an iron chelator.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".