Hepcidin-mediated Iron Regulation in P19 Cells is Detectable by Magnetic Resonance Imaging
Bibliographic record
Abstract
Magnetic resonance imaging can be used to track cellular activities in the body using iron-based contrast agents. However, multiple intrinsic cellular iron handling mechanisms may also influence the detection of magnetic resonance (MR) contrast: a need to differentiate among those mechanisms exists. In hepcidin-mediated inflammation, for example, downregulation of iron export in monocytes and macrophages involves post-translational degradation of ferroportin. We examined the influence of hepcidin endocrine activity on iron regulation and MR transverse relaxation rates in multi-potent P19 cells, which display high iron import and export activities, similar to alternatively-activated macrophages. Iron import and export were examined in cultured P19 cells in the presence and absence of iron-supplemented medium, respectively. Western blots indicated the levels of transferrin receptor, ferroportin and ubiquitin in the presence and absence of extracellular hepcidin. Total cellular iron was measured by inductively-coupled plasma mass spectrometry and correlated to transverse relaxation rates at 3 Tesla using a gelatin phantom. Under varying conditions of iron supplementation, the level of ferroportin in P19 cells responds to hepcidin regulation, consistent with degradation through a ubiquitin-mediated pathway. This response of P19 cells to hepcidin is similar to that of classically-activated macrophages. The correlation between total cellular iron content and MR transverse relaxation rates was different in hepcidin-treated and untreated P19 cells: slope, Pearson correlation coefficient and relaxation rate were all affected. These findings may provide a tool to non-invasively distinguish changes in endogenous iron contrast arising from hepcidin-ferroportin interactions, with potential utility in monitoring of different macrophage phenotypes involved in pro- and anti-inflammatory signaling. In addition, this work demonstrates that transverse relaxivity is not only influenced by the amount of cellular iron but also by its metabolism.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".