Remarkable effect of mobile phase buffer on the SEC-ICP-AES derived Cu, Fe and Zn-metalloproteome pattern of rabbit blood plasma
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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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".