Highly accurate multiprotein detection on a digital ELISA platform
Bibliographic record
Abstract
The emerging single-molecule detection platform digital enzyme-linked immunosorbent assay (ELISA) can detect numerous proteins simultaneously at serum concentrations as low as picograms per milliliter. We sought to improve cytokine detection with this platform to aid diagnosis of conditions such as allergy and asthma. We developed a multiple single-molecule detection digital ELISA approach, through the application of encoded magnetic microbeads to simultaneously detect three cytokines in one serum sample. We tested the approach's utility to distinguish asthma-related cytokines in children. Concentrations of interleukin-4 (IL-4) and IL-6 were significantly higher in children with asthma than in healthy controls, while the concentration of interferon-γ (IFN-γ) was significantly lower. Our method has higher accuracy than conventional methods, and our results indicate that the proposed improved high-sensitivity digital ELISA-based diagnosis approach can facilitate early detection and treatment of childhood asthma or related diseases.
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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".