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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".