Automated Electroosmotic Digital Optofluidics for Rapid and Label-Free Protein Detection
Bibliographic record
Abstract
Rapid protein detection is crucial for medical diagnosis, clinical trials, and drug development but often faces challenges in balancing sensitivity with multiplex detection, low reagent consumption, and a short detection time. In this work, we developed an automated and sensitive electroosmotic digital optofluidics (e-DOF) platform for rapid and label-free protein biomarker quantification in microliter blood samples. The hyperspectral computation reveals nanoscale morphology changes caused by target protein capture, eliminating multifarious enzyme-linked labeling. Electroosmosis-driven molecular circulation accelerates the immuno-hybridization, enhancing sensitivity (with a detection limit of 0.21 nM) and reducing the detection time to 15 min, compared to 2-3 h for a traditional enzyme-linked immunosorbent assay. In multiplex detection of hepatitis A and E IgM in 17 clinical samples, the results were completely consistent with clinical trial outcomes. This e-DOF system presents an automated, rapid, and label-free platform for multiplex detection in microliter samples, highlighting potential applications in clinical diagnosis and immunoassay research.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".