Real-Time QCM-D Immunoassay through Oriented Antibody Immobilization Using Cross-Linked Hydrogel Biointerfaces
Bibliographic record
Abstract
This report presents the development of pre-cross-linked and in situ cross-linked polyethyleneimine-carboxymethylcellulose antibody immobilization platforms for real-time QCM-D immunoassay of sepsis-related biomarkers. These platforms differ significantly from recent trends in QCM-based assays, a rapidly expanding field given the affordability and sensitivity of the transduction system, by providing ultrafast biointerface deposition through cross-linking of polysaccharides. Using rhIL-1ra (17 kDa), a known sepsis biomarker, for development, various immunoassay modifications to increase sensitivity were investigated, including the use of Protein A, Protein G, and anti-IgG Fc specific antibody capture ligands for oriented antibody immobilization, higher-frequency QCM-D crystals, and amplification using secondary antibodies. The optimized assay employs Protein A oriented immobilization on pre-cross-linked polymer and secondary antibodies to achieve a detection limit of 25 ng/mL on 5 MHz crystals. Assay repeatability using the optimized chemistry is robust, with no loss in 100 ng/mL antigen detection over 20 cycles of the 10 min sandwich assay. Nonspecific adsorption of human serum albumin, as characterized by ToF-SIMS, is minimal and negligible for the pre-cross-linked and in situ cross-linked compositions, respectively.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".