Digital Microfluidic Platform for Human Plasma Protein Depletion
Bibliographic record
Abstract
Many important biomarkers for disease diagnosis are present at low concentrations in human serum. These biomarkers are masked in proteomic analysis by highly abundant proteins such as human serum albumin (HSA) and immunoglobulins (IgGs) which account for up to 80% of the total protein content of serum. Traditional depletion methods using macro-scale LC-columns for highly abundant proteins involve slow separations which impart considerable dilution to the samples. Furthermore, most techniques lack the ability to process multiple samples simultaneously. We present a method of protein depletion using superparamagnetic beads coated in anti-HSA, Protein A, and Protein G, manipulated by digital microfluidics (DMF). The depletion process was capable of up to 95% protein depletion efficiency for IgG and HSA in 10 min for four samples simultaneously, which resulted in an approximately 4-fold increase in signal-to-noise ratio in MALDI-MS analysis for a low abundance protein, hemopexin. This rapid and automated method has the potential to greatly improve the process of biomarker identification.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".