Recent Insights into Interactions of Deferoxamine with Cellular and Plasma Iron Pools: Implications for Clinical Use
Why this work is in the frame
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Bibliographic record
Abstract
Despite the availability of deferoxamine (DFO) for more than three decades, its rates of interaction with cellular iron pools in different tissues, and the effects of its pharmacokinetics on the interaction with plasma iron pools, remain incompletely understood. The positive charge of DFO, together with the negative resting potential in vertebrate cells, favors cellular uptake, whereas the low lipophilicity and high molecular weight counter this effect. The findings presented suggest a facilitated uptake of DFO into hepatocytes, being several hundred-fold faster than into red cells. Antibodies that selectively recognize ferrioxamine (FO) show that initial hepatocellular iron chelation is cytosolic, but later transposes to lysosomal and ultimately canalicular compartments. Strong FO staining is visible in myocytes within 4-8 h after commencing a subcutaneous DFO infusion, indicating effective chelation of myocyte iron. A methodology was developed to study the interaction of DFO and its metabolites with plasma iron pools by stabilizing DFO with aluminum ions, thereby preventing iron shuttling from non-transferrin-bound iron (NTBI) onto DFO after plasma collection. DFO removes only about a third of NTBI rapidly, and NTBI is rarely cleared completely. Increasing DFO dosing does not increase NTBI removal, but instead leads to a greater rebound in NTBI on cessation of intravenous infusion. Thus, intermittent infusions of high-dose DFO are less desirable than continuous infusions at low doses, particularly in high-risk patients. Here the benefits of continuous DFO on heart function occur before changes in T2*-visible storage iron, consistent with early removal of a toxic labile iron pool within myocytes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it