Age-Related Changes in Iron Homeostasis and Cell Death in the Cerebellum of Ceruloplasmin-Deficient Mice
Bibliographic record
Abstract
Iron is essential for a variety of cellular functions, but its levels and bioavailability must be tightly regulated because of its toxic redox activity. A number of transporters, binding proteins, reductases, and ferroxidases help maintain iron homeostasis to prevent cell damage. The multi-copper ferroxidase ceruloplasmin (Cp) converts toxic ferrous iron to its nontoxic ferric form and is required for iron efflux from cells. Absence of this enzyme in humans leads to iron accumulation and neurodegeneration in the CNS. Here we report on the changes that occur in the cerebellum of Cp null (Cp-/-) mice with aging. We show that iron accumulation, which is reflected in increased ferritin expression, occurs mainly in astrocytes by 24 months in Cp-/- mice and is accompanied by a significant loss of these cells. In contrast, Purkinje neurons and the large neurons in the deep nuclei of Cp-/- mice do not accumulate iron but express high levels of the iron importer divalent metal transporter 1, suggesting that these cells may be iron deprived. This is also accompanied by a significant reduction in the number of Purkinje neurons. These data suggest that astrocytes play a central role in the acquisition of iron from the circulation and that two different mechanisms underlie the loss of astrocytes and neurons in Cp-/- mice. These findings provide a better understanding of the degenerative changes seen in humans with aceruloplasminemia and have implications for normal aging and neurodegenerative diseases in which iron accumulation occurs.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".