Ferroptosis in Neurological Disease
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it