A quantitative, label-free visual interference colour assay platform for protein targeting and binding assays
Bibliographic record
Abstract
The vast array of immunoassay technologies used to assess protein interactions is costly or platform-specific. We present a label-free visual interference colour assay (VICA) that quantifies peptide and protein interactions by creating an iridescent surface allowing direct visualisation without spectrophotometric optics or microfluidics. A nanoporous aluminium oxide surface is tuned to match the refractive indices of the overlying protein layers to generate visual interference colours. To functionalise the surface, we created an affinity-capture system using a protein A-carboxyglutamic (GLA) construct that orients antibodies to enhance the signal. Using off-the-shelf antibodies, the platform can isolate analytes in buffer, whole blood, or serum. This surface generates a discernible colour change at concentrations as low as 50 femtomoles/mm 2 and can monitor oligomer formation in sequential steps on the same slide. VICA provides comparable kinetic parameters to biolayer interferometry and traditional immunoassays while also allowing characterisation of proteins in large macromolecular complexes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".