Iron Binding and Iron Removal Efficiency of Desferrioxamine Based Polymeric Iron Chelators: Influence of Molecular Size and Chelator Density
Bibliographic record
Abstract
Desferrioxamine (DFO) is a clinically approved, high affinity iron chelator used for the treatment of iron overload. Due to its short half-life and toxicity, DFO is administered for 8-12 h per day, 5-7 d per week. In this manuscript, the influence of molecular properties of hyperbranched polyglycerol (HPG)-DFO conjugates on their iron binding by isothermal titration calorimetry, iron removal efficiency from ferritin in presence and absence of a low molecular weight (MW) iron chelator, and protection against iron mediated oxidation of proteins is reported. The iron binding properties of HPG-DFO are slightly altered with size and DFO density of conjugates. The lower MW conjugate shows greater iron removal efficiency at room temperature, however, the efficacy of high MW conjugates increases at physiological temperature. The iron removal from ferritin by HPG-DFO conjugates increases significantly in presence of a low MW chelator, suggesting the potential of combination therapy. The molecular properties of the polymer scaffold also have influence on the prevention of iron mediated oxidation of proteins by the conjugates. The results therefore help to define the iron binding thermodynamics of HPG-DFO and their dependence on MW, and can be extended to improve the general understanding of polymeric chelator-iron interactions in situ.
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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".