Digital Microfluidic Platform for Human Plasma Protein Depletion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it