"HIGHLY SENSITIVE, MULTIPLEXED DETECTION OF CIRCULATING BIOMARKERS USING A GOLD-NANOPARTICLE-EMBEDDED MEMBRANE"
Bibliographic record
Abstract
Biofluids in early disease stages may contain protein biomarkers at sub-pg/mL concentrations, demanding highly sensitive methods for detection.In early-stage cancer, tumors are small and can grow for 10 years before detection with traditional gold standard ELISA technique [1].Further, complex diseases often require multiplexed detection of proteins for accurate diagnosis.Gold nanoparticles (AuNPs) interact strongly with fluorescent molecules and can greatly enhance signals when placed within 5-90 nm through the phenomenon of metal enhanced fluorescence (MEF) [2].However, existing MEF-based platforms are developed as 2D substrates and require complex micro/nanofabrication, limiting their assay sensitivity and usability.Nitrocellulose membranes are highly porous 3D structures providing increased surface area and molecule binding capacity.In this work, we developed a novel gold-nanoparticle-embedded membrane (GEM, Figure 1) platform, which offers (1) increased sensitivity.Subpg/mL detection limits were achieved benefiting from both the 3D membrane structure and the AuNP induced MEF;(2) high multiplexing capabilities with minimal cross-reactions achieved through compartmentalization and microarrays; (3) low sample consumption; (4) simple-to-implement with basic lab equipment [2-3].
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".