Dysregulated Iron Homeostasis as Common Disease Etiology and Promising Therapeutic Target
Bibliographic record
Abstract
Iron is irreplaceably required for animal and human cells as it provides the activity center for a wide variety of essential enzymes needed for energy production, nucleic acid synthesis, carbon metabolism and cellular defense. However, iron is toxic when present in excess and its uptake and storage must, therefore, be tightly regulated to avoid damage. A growing body of evidence indicates that iron dysregulation leading to excess quantities of free reactive iron is responsible for a wide range of otherwise discrete diseases. Iron excess can promote proliferative diseases such as infections and cancer by supplying iron to pathogens or cancer cells. Toxicity from reactive iron plays roles in the pathogenesis of various metabolic, neurological and inflammatory diseases. Interestingly, a common underlying aspect of these conditions is availability of excess reactive iron. This underpinning aspect provides a potential new therapeutic avenue. Existing hematologically used iron chelators to take up excess iron have shown serious limitations for use but new purpose-designed chelators in development show promise for suppressing microbial pathogen and cancer cell growth, and also for relieving iron-induced toxicity in neurological and other diseases. Hepcidin and hepcidin agonists are also showing promise for relieving iron dysregulation. Harnessing iron-driven reactive oxygen species (ROS) generation with ferroptosis has shown promise for selective destruction of cancer cells. We review biological iron requirements, iron regulation and the nature of iron dysregulation in various diseases. Current results pertaining to potential new therapies are also reviewed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".