Integrating image analysis and low-cost optical transmittance for quantification of agglutination in a dual-phase automated dynamic inlet microfluidics system
Bibliographic record
Abstract
This study presents an integrated approach for quantifying agglutination over a wide concentration range (0.0128– 5000 μ g /mL) by combining two complementary optical detection techniques within a dual-phase Lab-in-Tubing microfluidic platform. This platform generates continuous trains of droplets inside PTFE tubing. Each droplet contains varying concentrations of Streptavidin-coated Dynabeads and biotinylated Bovine Serum Albumin (bBSA). The tubing-based setup enables droplet-based reactions and analysis in a compact, low-cost, and easily configurable format. The detection system combines image-based analysis with a low-cost optical measurement method. The optical component uses a line break sensor (LBS), a compact light-based device that monitors how much light passes through each droplet. The amount of transmitted light changes based on the agglomerates formed within the droplet. These variations in the light signal indicate the extent of agglutination. Image analysis is performed in MATLAB, where droplet images are processed to extract multiple quantitative features for detailed evaluation of agglutination levels. Image analysis alone accurately distinguishes agglutinated from non-agglutinated droplets across whole concentration range with 100% accuracy and classifies agglutination into three distinct bands (Band 1: 0.0128– 0 . 32 μ g /mL, Band 2: 1.6– 40 μ g /mL, Band 3: 200– 5000 μ g /mL) with 97.5% accuracy. With the developed algorithm, which combines both image analysis and optical measurements, the accuracy of classifying agglutination into the specified bands increases to 97.9%. This hybrid detection framework demonstrates the potential of accessible, scalable agglutination diagnostics using microfluidics and affordable optical tools.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".