Analysis of the plasma metalloproteome by SEC–ICP-AES: bridging proteomics and metabolomics
Bibliographic record
Abstract
Although blood plasma inherently contains protein biomarkers for human disease diagnosis, their determination is difficult since more than 3700 proteins are commonly present. The associated protein-separation problem can, however, be dramatically simplified by analyzing plasma for a subproteome, such as those proteins that contain bound metals. To this end, the analysis of plasma by size-exclusion chromatography (SEC) coupled with an inductively coupled plasma atomic-emission spectrometer (ICP-AES), which served as the simultaneous Cu-, Fe- and Zn-specific detector, revealed the presence of approximately 12 metalloproteins within 25 min. In the context of modern proteomics research, SEC-ICP-AES therefore represents a viable proteomic approach that can be applied to diagnose human diseases that are associated with increased or decreased concentrations of certain plasma metalloproteins. Furthermore, SEC-ICP-AES can be employed to probe the effect of environmental chemicals or drugs in blood at the metalloprotein level, which makes it a versatile research tool for applications in toxicology, applied medicine, pharmacology and nutritional science.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".