A Rapid, Economical, and Reproducible Method for Human Serum Delipidation and Albumin and IgG Removal for Proteomic Analysis
Bibliographic record
Abstract
Serum is a readily available source for diagnostic assays, but the identification of disease-specific serum biomarkers has been impeded by the dominance of human serum albumin (HSA) and immunoglobulin G (IgG) in the serum proteome. Therefore, in order to observe lower-abundance serum proteins, removal or depletion of at least these two proteins is required. However, the depletion method needs to be inexpensive and reproducible. We describe such a protocol that combines delipidation by centrifugation, IgG removal with Protein G Sepharose, and HSA depletion with sodium chloride/ethanol precipitation. The protocol is streamlined to increase reproducibility and is compatible with many proteomic platforms, including two-dimensional gel electrophoresis, and high-performance liquid chromatography either offline or coupled online with a mass spectrometer. The reproducible depletion of lipids, IgG, and HSA permits a higher load of the remaining serum proteins, facilitating the identification of disease biomarkers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".