Ferroptosis in Neurological Disease
Bibliographic record
Abstract
Iron accumulation in the CNS occurs in many neurological disorders. It can contribute to neuropathology as iron is a redox-active metal that can generate free radicals. The reasons for the iron buildup in these conditions are varied and depend on which aspects of iron influx, efflux, or sequestration that help maintain iron homeostasis are dysregulated. Iron was shown recently to induce cell death and damage via lipid peroxidation under conditions in which there is deficient glutathione-dependent antioxidant defense. This form of cell death is called ferroptosis. Iron chelation has had limited success in the treatment of neurological disease. There is therefore much interest in ferroptosis as it potentially offers new drugs that could be more effective in reducing iron-mediated lipid peroxidation within the lipid-rich environment of the CNS. In this review, we focus on the molecular mechanisms that induce ferroptosis. We also address how iron enters and leaves the CNS, as well as the evidence for ferroptosis in several neurological disorders. Finally, we highlight biomarkers of ferroptosis and potential therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".