In vitro assessment of chelating agents with regard to their abstraction efficiency of Cd2+ bound to plasma proteins
Bibliographic record
Abstract
The exposure of various human populations to Cd(2+) is of increasing health concern. After its gastrointestinal absorption into the bloodstream, Cd(2+) binds to α(2)-macroglobulin and serum albumin. Although animal studies have demonstrated that meso-2,3-dimercaptosuccinic acid (DMSA) and diethylenetriamine pentaacetic acid (DTPA) can effectively mobilize Cd(2+) to urine and decrease the Cd concentrations of the kidneys, the liver and the brain, not much is known about the abstraction of Cd(2+) from blood plasma proteins. We prepared a stock of Cd(2+) spiked rabbit plasma (2.0 μg of Cd(2+)/mL) and analyzed aliquots by size exclusion chromatography coupled on-line to an inductively coupled plasma atomic emission spectrometer (SEC-ICP-AES) while simultaneously monitoring the emission lines of Ca, Cd, Cu, Fe, and Zn. After the addition of 0.33 mM, 0.66 mM or 0.99 mM of DMSA, DTPA, 2,3-dimercapto-1-propanesulfonic acid (DMPS) or N-acetyl-l-cysteine (NAC) to plasma aliquots, the obtained mixtures were analyzed by SEC-ICP-AES after 5 min and 30 min. None of the investigated compounds adversely affected the plasma distribution of Fe at all investigated doses. At 0.33 mM, DTPA was most effective at mobilizing plasma protein bound Cd(2+) to a ~5 kDa Cd-species (100% removal), followed by DMPS (94%), DMSA (83%) and NAC (3%). All investigated compounds also mobilized Zn(2+) from plasma proteins to ~5 kDa Zn-species (DTPA: 80% removal; DMPS: 63%; DMSA: 29% and NAC: 3%). The addition of DTPA resulted in the dose-dependent elution of a [Ca-DTPA](3-) complex. Based on these results, 0.33 mM DMSA represents the best compromise that can be achieved between maximizing the abstraction of Cd(2+) from plasma proteins (83%), while minimizing the mobilization of Zn(2+) from plasma proteins (29%), and avoiding the complexation of Ca(2+).
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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) | 0.001 | 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".