A microfluidic antibody bioarray for fast detection of human interleukins in low sample volumes
Why this work is in the frame
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Bibliographic record
Abstract
The antibody bioarray is a promising tool for the detection of proteins, which can be used as disease biomarkers. Therefore, we have developed a sandwich immunoassay-type bioarray using a microfluidic method to detect human interleukins that have diagnostic values. In the method development, we studied the effect of different factors that affect capture antibody immobilization, antibody–antigen interactions, and detection methods on the bioarray surface. The fluorescence signal obtained from different detection strategies was compared based on the signal fold-increase. This comparison showed that the use of covalent immobilization of capture antibodies, as opposed to their physical adsorption on the bioarray surface, increases the signal by 1.5 fold. Moreover, the use of protein G to achieve a better oriented immobilized capture antibody has resulted in a more than 3-fold signal enhancement.
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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 it