Enhanced Immunoassay Using a Rotating Paper Platform for Quantitative Determination of Low Abundance Protein Biomarkers
Bibliographic record
Abstract
The changing concentrations of circulating protein biomarkers have been correlated with a variety of diseases. Quantitative bioassays capable of sensitive and specific determination of protein biomarkers at low levels can be essential for therapeutic treatments that can improve outcomes for patients. Herein, we describe the investigation of a rotating paper device (RPD) for quantitative determination of targeted proteins at the fM concentration level. The RPD consists of two circular papers each separately supported with a plastic disc. Protein detection is conducted via enhanced immunoassay using amplification in a sequential workflow, which includes a sandwich immunoassay in the upper paper and a signal amplification reaction in the lower paper. The sandwich immunoassay is conducted using biobarcode nanoparticles (BNPs) and results in the release of reporter oligonucleotides from BNPs. These oligonucleotides are transferred to the bottom paper, where they engage in a target recycling methodology that leads to the production of a colorimetric signal. The assay was evaluated for quantitation of interleukin-6 (IL-6), a cytokine biomarker in serum. A limit of detection of 63 fM and a dynamic range of 200 fM-8 pM was observed for the assay. The specificity of the assay was successfully verified against several common protein biomarkers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".