Iron Withdrawal with DIBI, a Novel 3-Hydroxypyridin-4-One Chelator Iron-Binding Polymer, Attenuates Macrophage Inflammatory Responses
Bibliographic record
Abstract
Purpose: Iron is an essential trace element for the inflammatory response to infection. In this study, we determined the effect of the recently developed iron-binding polymer DIBI on the synthesis of inflammatory mediators by RAW 264.7 macrophages and bone marrow-derived macrophages (BMDMs) in response to lipopolysaccharide (LPS) stimulation. Methods: Flow cytometry was used to determine the intracellular labile iron pool, reactive oxygen species production, and cell viability. Cytokine production was measured by quantitative reverse transcription polymerase chain reaction and enzyme-linked immunosorbent assay. Nitric oxide synthesis was determined by the Griess assay. Western blotting was used to assess signal transducer and activator of transcription (STAT) phosphorylation. Results: Macrophages cultured in the presence of DIBI exhibited a rapid and significant reduction in their intracellular labile iron pool. DIBI-treated macrophages showed reduced expression of proinflammatory cytokines interferon-β, interleukin (IL)-1β, and IL-6 in response to LPS. In contrast, exposure to DIBI did not affect LPS-induced expression of tumor necrosis factor-α (TNF-α). The inhibitory effect of DIBI on IL-6 synthesis by LPS-stimulated macrophages was lost when exogenous iron in the form of ferric citrate was added to culture, confirming the selectivity of DIBI for iron. DIBI-treated macrophages showed reduced production of reactive oxygen species and nitric oxide following LPS stimulation. DIBI-treated macrophages also showed a reduction in cytokine-induced activation of STAT 1 and 3, which potentiate LPS-induced inflammatory responses. Conclusion: DIBI-mediated iron withdrawal may be able to blunt the excessive inflammatory response by macrophages in conditions such as systemic inflammatory syndrome.
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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 | 0.000 |
| 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".