EDTA as a chelating agent in quantitative <sup>1</sup>H-NMR of biologically important ions
Bibliographic record
Abstract
Biologically important ions such as Ca, K, Mg, Fe, and Zn play major roles in numerous biological processes, and their homeostatic balance is necessary for the maintenance of cellular activities. Sudden and severe loss in homeostasis of just one biologically important ion can cause a cascade of negative effects. The ability to quickly, accurately, and reliably quantify biologically important ions in samples of human bio-fluids is something that has been sorely lacking within the field of metabolomics. 1H-NMR spectra. The foundation of our investigation was the a-priori knowledge that free ethylenediaminetetraacetic acid (EDTA) produces two clear single peaks on 1H-NMR spectra, and that EDTA chelated to different ions produces unique 1H-NMR spectral patterns due to 3D conformational changes in the chemical structure of chelated-EDTA and varying degrees of electronegativity. The aim of this study was to develop and test a 1H-NMR-based method, with application specifically to the field of metabolomics, to quantify biologically important ions within the physiological pH range of 6.50–7.50 using EDTA as a chelating agent. Our method produced linear, accurate, precise, and repeatable results for Ca, Mg, and Zn; however, K and Fe did not chelate with EDTA.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".