Sample preparation of blood plasma enables baseline separation of iron metalloproteins by SEC-GFAAS
Bibliographic record
Abstract
The analysis of human plasma for biomarkers holds promise to revolutionize disease diagnosis, but is hampered by the inherent complexity of the plasma proteome. One way to overcome this problem is to analyze plasma for a sub-proteome, such as the metalloproteome. Previous studies employing size-exclusion chromatography (SEC) coupled on-line to an inductively coupled plasma-atomic emission spectrometer (ICP-AES) have revealed that plasma contains ~12 copper, iron and zinc metalloproteins. This included the iron metalloproteins transferrin (Tf) and a recently identified haptoglobin-hemoglobin (Hp-Hb) complex, which is formed in plasma when red blood cells rupture. Since this SEC-ICP-AES method required a sample volume of 500 µL to generate diagnostically useful results, we sought to develop an alternative SEC-based hyphenated approach using a smaller SEC column (150 × 5 mm I.D.) and a graphite furnace atomic absorption spectrometer (GFAAS) as the iron-specific detector. A designed interface enabled the integration of the SEC system with the GFAAS. Baseline separation between the Hp-Hb complex and Tf was achieved by developing a sample preparation procedure which involved the chelating agent-based mobilization of Fe from Tf to a small molecular weight Fe complex. Spiking of human plasma (1.0 mL) with red blood cell lysate (1–2 µL) increased only the intensity of the Fe peak corresponding to the Hp-Hb complex, but not that of Tf. Since the developed SEC-GFAAS method requires only 50 µL of plasma for analysis, it can now be employed for the cost-effective quantification of the clinically relevant Hb-Hp complex in human plasma in <50 min.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".