Comparison of Treatment Effects of Different Iron Chelators in Experimental Models of Sepsis
Bibliographic record
Abstract
Growing evidence indicates that dysregulated iron metabolism with altered and excess iron availability in some body compartments plays a significant role in the course of infection and sepsis in humans. Given that all bacterial pathogens require iron for growth, that iron withdrawal is a normal component of innate host defenses and that bacterial pathogens have acquired increasing levels of antibiotic resistance, targeting infection and sepsis through use of appropriate iron chelators has potential to provide new therapeutics. We have directly compared the effects of three Food and Drug Administration (FDA)-approved chelators (deferoxamine-DFO; deferiprone-DFP; and deferasirox-DFX), as were developed for treating hematological iron overload conditions, to DIBI, a novel purpose-designed, anti-infective and anti-inflammatory water-soluble hydroxypyridinone containing iron-selective copolymers. Two murine sepsis models, endotoxemia and polymicrobial abdominal sepsis, were utilized to help differentiate anti-inflammatory versus anti-infective activities of the chelators. Leukocyte adhesion, as measured by intravital microscopy, was observed in both models, with DIBI providing the most effective reduction and DFX the poorest. Inflammation in the abdominal sepsis model, assessed by cytokine measurements, indicated exacerbation by DFX and DFO for plasma Interleukin (IL)-6 and reductions to near-control levels for DIBI and DFP. Peritoneal infection burden was reduced 10-fold by DIBI while DFX and DFP provided no reductions. Overall, the results, together with those from other studies, revealed serious limitations for each of the three hematological chelators, i.e., as potentially repurposed for treating infection/sepsis. In contrast, DIBI provided therapeutic benefits, consistent with various in vitro and in vivo results from other studies, supporting the potential for its use in treating sepsis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".