Multiplexed High-Throughput Electrokinetically-Controlled Immunoassay on a Chip for the Detection of Specific Bacterial Antibodies in Human Serum
Bibliographic record
Abstract
This work presents a multiplexed electrokinetically-controlled heterogeneous immunoassay that can process ten samples in parallel. The immunoassay microchip was soft-lithographically fabricated using poly(dimethylsiloxane) and glass. Controlling parameters of the electrokinetically-driven flow in the microfluidic network was determined by numerically simulating transport processes. Multiple passively adsorbed antigens captured antibodies present in samples, which then bound with TRITC-labeled detection antibodies to generate fluorescent signals. Antibodies against Escherichia coli O157:H7 and Helicobacter pylori were studied as model analytes. After conditions for antigen-coating were optimized, a 24-minute assay detected E. coli O157:H7 antibody in the concentration range of 0.02–10 μg/mL, and H. pylori antibody in the range of 0.1–50 μg/mL. In testing human serum samples, non-specific binding of serum components was effectively suppressed by using 10% (w/v) bovine serum albumin. An accuracy of 100% was achieved in detecting either E. coli O157:H7 antibody or H. pylori antibody from human serum samples. Simultaneous screening of both antibodies was also successfully demonstrated. The immunoassay chip shows an excellent potential for efficiently detecting multiple pathogenic infections in clinical environments.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".