The Role of Iron Regulation in Immunometabolism and Immune-Related Disease
Bibliographic record
Abstract
Immunometabolism explores how the intracellular metabolic pathways in immune cells can regulate their function under different micro-environmental and (patho-)-physiological conditions (Pearce, 2010; Buck et al., 2015; O'Neill and Pearce, 2016). In the last decade great advances have been made in studying and manipulating metabolic programs in immune cells. Immunometabolism has primarily focused on glycolysis, the TCA cycle and oxidative phosphorylation (OXPHOS) as well as free fatty acid synthesis and oxidation. These pathways are important for providing the energy needs of cell growth, membrane rigidity, cytokine production and proliferation. In this review, we will however, highlight the specific role of iron metabolism at the cellular and organismal level, as well as how the bioavailability of this metal orchestrates complex metabolic programs in immune cell homeostasis and inflammation. We will also discuss how dysregulation of iron metabolism contributes to alterations in the immune system and how these novel insights into iron regulation can be targeted to metabolically manipulate immune cell function under pathophysiological conditions, providing new therapeutic opportunities for autoimmunity and cancer.
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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) | 0.002 | 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".