Monocyte MRI Relaxation Rates Are Regulated by Extracellular Iron and Hepcidin
Bibliographic record
Abstract
Many chronic inflammatory conditions are mediated by an increase in the number of monocytes in peripheral circulation, differentiation of monocytes to macrophages, and different macrophage subpopulations during pro- and anti-inflammatory stages of tissue injury. When hepcidin secretion is stimulated during inflammation, the iron export protein ferroportin is targeted for degradation on a limited number of cell types, including monocytes and macrophages. Such changes in monocyte iron metabolism raise the possibility of non-invasively tracking the activity of these immune cells using magnetic resonance imaging (MRI). We hypothesized that hepcidin-mediated changes in monocyte iron regulation influence both cellular iron content and MRI relaxation rates. In response to varying conditions of extracellular iron supplementation, ferroportin protein levels in human THP-1 monocytes decreased two- to eightfold, consistent with paracrine/autocrine regulation of iron export. Following hepcidin treatment, ferroportin protein levels further decreased two- to fourfold. This was accompanied by an approximately twofold increase in total transverse relaxation rate, R2*, compared to non-supplemented cells. A positive correlation between total cellular iron content and R2* improved from moderate to strong in the presence of hepcidin. These findings suggest that hepcidin-mediated changes detected in monocytes using MRI could be valuable for in vivo cell tracking of inflammatory responses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".