Protective Effect of PEG-EDTA and Its Zinc(II) Complex on Human Cells
Bibliographic record
Abstract
The most widely used chelating agent, ethylenediaminetetraacetic acid (EDTA), can cause mild to serious side effects when used for clinical applications. Introducing a polyethylene glycol (PEG) moiety into the molecular structure of EDTA has been shown to lower its toxicity; however, it is unclear whether this could affect EDTA chelation efficiency due to the steric hindrance and the loss of a coordination site caused by the PEGylation reaction. This research aimed to determine if PEGylation could reduce EDTA toxicity without affecting its chelation efficiency. To this end, effective formation constants were determined for EDTA and PEG-EDTA rare earth metal ion complexes using spectrophotometric and titrimetric methods. The stability of PEG-EDTA complexes with the target metal ions was assessed under different conditions using Fourier-transform infrared spectroscopy. The cytotoxicity and metal detoxification capacity of EDTA, PEG-EDTA, and their zinc(II) complexes were determined in two selected human cell types exposed to toxic heavy metal ions. This study suggests that PEG-EDTA has lower toxicity than EDTA, especially when complexed with a nontoxic metal ion, such as zinc(II), while only slightly losing chelation efficiency, potentially making PEG-EDTA a more favourable metal detoxification reagent for clinical applications.
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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".