Mining biomarkers in human sera using proteomic tools
Bibliographic record
Abstract
One of the major difficulties in mining low abundance biomarkers from serum or plasma is due to the fact that a small number of proteins such as albumin, alpha2-macroglobulin, transferrin, and immunoglobulins, may represent as much as 80% of the total serum protein. The large quantity of these proteins makes it difficult to identify low abundance proteins in serum using traditional 2-dimensional electrophoresis. We recently used a combination of multidimensional liquid chromatography and gel electrophoresis coupled to matrix-assisted laser desorption/ionization-quadrupole-time of flight and Ion Trap liquid chromatography-tandem mass spectrometry to identify protein markers in sera of Alzheimer's disease (AD), insulin resistance/type-2 diabetes (IR/D2), and congestive heart failure (CHF) patients. We identified 8 proteins that exhibit higher levels in control sera and 36 proteins that exhibit higher levels in disease sera. For example, haptoglobin and hemoglobin are elevated in sera of AD, IR/D2, and CHF patients. The levels of several other proteins including fibrinogen and its fragments, alpha 2-macroglobulin, transthyretin, pro-platelet basic protein, protease inhibitors clade A and C, as well as proteins involved in the classical complement pathway such as complement C3, C4, and C1 inhibitor, were found to differ between IR/D2 and control sera. The sera levels of proteins, such as the 10 kDa subunit of vitronectin, alpha 1-acid glycoprotein, apolipoprotein B100, fragment of factor H, and histidine-rich glycoprotein were observed to be different between AD and controls. The differences observed in these biomarker candidates were confirmed by Western blot and the enzyme-linked immunosorbent assay. The biological meaning of the proteomic changes in the disease states and the potential use of these changes as diagnostic tools or for therapeutic intervention will be discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".