Removal of Fe3+ and Zn2+ from plasma metalloproteins by iron chelating therapeutics depicted with SEC-ICP-AES
Bibliographic record
Abstract
The iron chelation therapy drugs desferrioxamine B (DFO) and deferiprone (DFP) are used to treat iron overload patients, but not much is known about their adverse effects on other essential metals in vivo. After the addition of a clinically relevant dose of DFP or an equimolar dose of DFO to human plasma in vitro, the mixtures were analyzed by size exclusion chromatography (SEC) coupled to an inductively coupled plasma atomic emission spectrometer (ICP-AES). Simultaneous detection of the emission lines of copper, iron and zinc allowed the visualization of changes that these drugs exerted at the metalloprotein level. After the addition of DFP, a <10 kDa novel Fe-peak was detected and identified as (DFP)(3)Fe, whereas DFO resulted in the elution of a much smaller amount of Fe in this elution range. In fact, DFP was approximately 8-times more efficient than DFO regarding the removal of Fe from plasma proteins. The addition of both iron chelators also resulted in the elution of a <10 kDa novel Zn-peak. DFP abstracted twice as much Zn from plasma proteins compared to DFO. The identification of one of these peaks as (DFP)(2)Zn establishes a feasible biomolecular basis for the etiology of Zn-deficiency in patients that undergo long-term treatment with these drugs. Our results demonstrate that the analysis of plasma by SEC-ICP-AES can simultaneously provide insight into the efficacy of chelation therapy drugs and their adverse health effects at the metalloprotein level. Thus, SEC-ICP-AES emerges as a useful analytical tool to visualize health-relevant bioinorganic chemistry-related reactions of medicinal drugs in blood plasma in vitro.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".