High-Throughput Incubation and Quantification of Agglutination Assays in a Microfluidic System
Bibliographic record
Abstract
In this paper, we present a two-phase microfluidic system capable of incubating and quantifying microbead-based agglutination assays. The microfluidic system is based on a simple fabrication solution, which requires only laboratory tubing filled with carrier oil, driven by negative pressure using a syringe pump. We provide a user-friendly interface, in which a pipette is used to insert single droplets of a 1.25-µL volume into a system that is continuously running and therefore works entirely on demand without the need for stopping, resetting or washing the system. These assays are incubated by highly efficient passive mixing with a sample-to-answer time of 2.5 min, a 5⁻10-fold improvement over traditional agglutination assays. We study system parameters such as channel length, incubation time and flow speed to select optimal assay conditions, using the streptavidin-biotin interaction as a model analyte quantified using optical image processing. We then investigate the effect of changing the concentration of both analyte and microbead concentrations, with a minimum detection limit of 100 ng/mL. The system can be both low- and high-throughput, depending on the rate at which assays are inserted. In our experiments, we were able to easily produce throughputs of 360 assays per hour by simple manual pipetting, which could be increased even further by automation and parallelization. Agglutination assays are a versatile tool, capable of detecting an ever-growing catalog of infectious diseases, proteins and metabolites. A system such as this one is a step towards being able to produce high-throughput microfluidic diagnostic solutions with widespread adoption. The development of analytical techniques in the microfluidic format, such as the one presented in this work, is an important step in being able to continuously monitor the performance and microfluidic outputs of organ-on-chip devices.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".