Reducing Nonspecific Adhesion on Cross-Linked Hydrogel Platforms for Real-Time Immunoassay in Serum
Bibliographic record
Abstract
Biointerfaces that limit nonspecific adhesion of serum proteins have been developed by relying solely on cross-linked hydrogels. In addition to being characterized for adhesion of serum proteins, immunoassay sensitivity was also investigated through a sandwich assay for rhIL-1ra. Among the compositions developed, the optimal surface is comprised of pre-cross-linked carboxymethylcellulose (CMC) and polyethyleneimine (PEI) overlaid on a cross-linked layer of poly(ethylene glycol) (PEG) and PEI and employs an anti-IgG Fc specific ligand for oriented antibody immobilization; viscoelastic modeling provides a thickness estimate of 5 nm for the hydrogel alone, rising to 33 nm after the deposition of antibodies. Alternate compositions employing a Protein A ligand and PEG at the exposed surface of the biointerface were disfavored due to an 8-fold increase in serum adhesion and retarded immobilization kinetics, respectively. Through the rapid deposition provided by hydrogels, construction of the entire biointerface, including receptor immobilization, can be completed in 1 h. Based on QCM-D measurements, estimated nonspecific serum adsorption using these compositions is as low as 1.1 ng/mm2. The immunoassay as developed requires 10 min, providing a detection limit of 500 ng/mL rhIL-1ra in 25% human serum using only 5 microg of the secondary antibody.
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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) | 0.001 | 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) | 0.001 | 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".