Affinity-mass spectrometric technologies for quantitative proteomics in biological fluids
Bibliographic record
Abstract
Abstract Proteins are the functional molecules in organisms and are therefore excellent biomarker candidates for a diversity of diseases. Immunoassays and mass spectrometry (MS) are two major technologies being used in proteomics; however, they either lack specificity or sensitivity. An emerging trend is to combine immunoassays with MS (which we call “affinity-MS”). This is an important milestone in quantitative proteomics, making it possible to measure low-abundance proteins with high specificity. The targeted enrichment and the assignment of mass-to-charge ratios to different molecules provide two selection criteria, making affinity-MS highly specific. Picogram-per-milliliter limits of detection have been obtained for many proteins. Furthermore, multiplexing capacity of >150 proteins has been achieved. This article reviews different formats of affinity-enrichment methods, and demonstrates how they are interfaced with both electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) MS. The pros and cons of these techniques are compared, and future prospectives are 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".