Enzymatically-generated fluorescent detection in micro-channels with internal magnetic mixing for the development of parallel microfluidic ELISA
Bibliographic record
Abstract
The Enzyme-Linked Immuno-Sorbent Assay, or ELISA, is commonly utilized to quantify small concentrations of specific proteins for a large variety of purposes, ranging from medical diagnosis to environmental analysis and food safety. However, this technique requires large volumes of costly reagents and long incubation periods. The use of microfluidics permits one to specifically address these drawbacks by decreasing both the volume and the distance of diffusion inside the micro-channels. Existing microfluidic systems are limited by the necessary control of extremely low flow rates to provide sufficient time for the molecules to interact with each other by diffusion only. In this paper, we describe a new microfluidic design for the realization of parallel ELISA in stop-flow conditions. Magnetic beads were used both as a solid phase to support the formation of the reactive immune complex and to achieve a magnetic mixing inside the channels. In order to test the detection procedure, the formation of the immune complex was performed off-chip before the reactive beads were injected into the reaction chamber. Anti-streptavidin antibodies were quantified with low picomolar sensitivity (0.1-6.7 pM), a linear range of 2 orders of magnitude and good reproducibility. This work represents the first step toward a new platform for simple, highly effective and parallel microfluidic ELISA.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".