Methods for Enhanced Fluorescence Detection of Proteins by using Entrapped Gold Nanoparticles in Membranes
Bibliographic record
Abstract
Measuring protein levels from biofluids can provide important insight into human health and disease during various physiological and pathological conditions. In many situations, sensitive methods are required for protein quantification because at the early stages of many diseases, proteins in biofluids are present at very low concentrations. Here, a new and simple method is presented in the form of Basic and Alternative Protocols for an immunoassay performed on a nitrocellulose membrane, followed by the addition of gold nanoparticles prior to measuring fluorescence with a microscope. The assay protocol was optimized to achieve 3D metal-enhanced fluorescence (MEF) with increased antibody-binding capacity and enhanced fluorescence signals, improving assay sensitivity. Using different concentrations of spiked fluorescently labeled IgGs in pooled normal human plasma, a lower detection limit of 29 ng/ml was achieved. This limit of detection was found to be a thousand-fold lower than the conventional 2D assay and one order of magnitude lower than when the assay was performed on a 3D membrane without MEF. This method provides an easy way to improve immunoassay sensitivity, and it can be simply transferred to other labs. It also can extend to fluorescence detection of other analytes beyond proteins. © 2022 Wiley Periodicals LLC. Basic Protocol: Assay in nitrocellulose membrane with entrapped AuNPs using commercially available AuNPs Alternative Protocol: Assay in nitrocellulose membrane with entrapped AuNPs using lab-made AuNPs.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".