A “chemical nose” biosensor for detecting proteins in complex mixtures
Bibliographic record
Abstract
A growing understanding of the fundamental role of proteins in diseases has advanced the development of quantitative protein assays in the medical field. Current techniques for protein analysis include enzyme-linked immunosorbent assays (ELISA), flow cytometry, mass spectrometry, and immunohistochemistry. However, many of these conventional strategies require specialized training, expensive antibodies, or sophisticated equipment, raising assay costs and limiting their application to laboratory analysis. Here, we present the application of a "chemical nose" type colorimetric gold nanoparticle sensor for detection, quantification, and identification of single proteins, protein mixtures, and proteins within the complex environment of human serum. The unique interactions between a mixture of two different gold nanoparticle morphologies (spherical and branched) and six separate proteins (bovine serum albumin, human serum albumin, immunoglobulin G, fibrinogen, lysozyme, and hemoglobin) generated distinguishable protein- and concentration-dependent absorption spectra, even at nanomolar concentrations. Furthermore, we show that this response is sensitive to the relative abundance of different proteins in solution, permitting analysis of protein mixtures. Finally, we demonstrate the ability to distinguish human serum samples with and without a clinically relevant two-fold increase in immunoglobulin G, without the use of expensive reagents or complicated sample processing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".