Remarkable effect of mobile phase buffer on the SEC-ICP-AES derived Cu, Fe and Zn-metalloproteome pattern of rabbit blood plasma
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Bibliographic record
Abstract
The development of an analytical method to quantify the major Cu, Fe and Zn-containing metalloproteins in mammalian plasma has been recently reported. This method is based on the separation of plasma proteins by size exclusion chromatography (SEC) followed by the on-line detection of the metalloproteins by an inductively coupled plasma atomic emission spectrometer (ICP-AES). To assess whether the mobile phase buffer can affect the SEC-ICP-AES-derived metalloproteome pattern, thawed rabbit plasma was analyzed using phosphate buffered saline (PBS)-buffer (0.15 M, pH 7.4), Tris-buffer (0.1 and 0.05 M, pH 7.4), Hepes-buffer (0.1 M, pH 7.4) or Mops-buffer (0.1 M, pH 7.4). In contrast to the Cu-specific chromatograms, the Fe and Zn-specific chromatograms that were obtained with Tris, Hepes and Mops-buffer were considerably different from those attained with PBS-buffer. The Tris, Hepes and Mops-buffer mediated redistribution of ~25% plasma Zn(2+) from <100 kDa to >100-600 kDa plasma proteins and to a smaller extent to a <10 kDa (Tris)(2)Zn(2+)-complex can be rationalized in terms of the abstraction of Zn(2+) from the weak binding site on albumin. In contrast, only Hepes and Mops-buffer redistributed ~20% of plasma Fe(3+) from the <100 kDa to the >600 kDa elution range. Based on these results and considering that the utilization of PBS-buffer has previously resulted in the detection of a number of Cu, Fe and Zn-containing metalloentities in rabbit plasma that was most consistent with literature data, this mobile phase buffer is recommended for metallomic studies regarding mammalian blood plasma.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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