Automated Digital Microfluidic Platform for Magnetic-Particle-Based Immunoassays with Optimization by Design of Experiments
Bibliographic record
Abstract
We introduce an automated digital microfluidic (DMF) platform capable of performing immunoassays from sample to analysis with minimal manual intervention. This platform features (a) a 90 Pogo pin interface for digital microfluidic control, (b) an integrated (and motorized) photomultiplier tube for chemiluminescent detection, and (c) a magnetic lens assembly which focuses magnetic fields into a narrow region on the surface of the DMF device, facilitating up to eight simultaneous digital microfluidic magnetic separations. The new platform was used to implement a three-level full factorial design of experiments (DOE) optimization for thyroid-stimulating hormone immunoassays, varying (1) the analyte concentration, (2) the sample incubation time, and (3) the sample volume, resulting in an optimized protocol that reduced the detection limit and sample incubation time by up to 5-fold and 2-fold, respectively, relative to those from previous work. To our knowledge, this is the first report of a DOE optimization for immunoassays in a microfluidic system of any format. We propose that this new platform paves the way for a benchtop tool that is useful for implementing immunoassays in near-patient settings, including community hospitals, physicians' offices, and small clinical laboratories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".