Dielectrophoresis assisted high-throughput detection system for multiplexed immunoassays
Bibliographic record
Abstract
Digital ELISA is introduced as a novel platform with unique advantages for detecting multiple kinds of single-molecule in the sample. How to improve the sensitivity of detection is the direction of current related research. Here, we report an immunoassay method that applied electrokinetic effects to isolate the individual encoded beads and confine in micro-wells to improve the efficiency of cytokines detection simultaneously. The microfluidic design provided a non-uniform electric field to induce dielectrophoresis (DEP) force and to manipulate the beads. Two wavelengths of excitation light excited the encoded beads for simultaneous detection of reporters. The light was confined to the bottom slide via the principle of total internal reflection. Finally, the concentration of captured cytokines was obtained by picking up each bead from the image and then integrating the intensity of fluorescent light emitted from the reporters. The results demonstrated that the fill percentage of encoded beads was raised from 10-20% to 60-80% via DEP effect. By comparing the fluorescence color of the particle, itself and its surface, the concentration of four target cytokines, IL-2, IL-6, IL-10 and TNF-α, were calculated to the pg/ml level. The spike and recovery experiments verified the efficiency, more than 70% of the target molecules were captured. The reliability of our method was verified by flow cytometry as well. In conclusion, we expect the application of DEP can increase the sensitivity of digital ELISA for multiple rapid detection.
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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".