Functionalized gold nanoparticle-enhanced competitive assay for sensitive small-molecule metabolite detection using surface plasmon resonance
Bibliographic record
Abstract
The development of sensing systems for the measurement of small molecules is an active area of research. A sensor based approach for the measurement of metabolites can potentially provide the simplicity and portability required for widespread use. Rapid detection and quantitation of small-molecule metabolites can potentially emerge as an effective way to link the metabolite profile to the disease state. Surface plasmon resonance (SPR) combined with molecular recognition elements to deliver high specificity is a sensing platform that has been widely applied for a large range of biomolecules. However, direct detection of small molecules with SPR challenges the refractive index based detection mechanism. The work described here combines a periplasmic binding protein for recognition with target modified gold nanoparticles (AuNPs) in a competitive assay format for folic acid (FA) detection. Specifically, a SPR imaging substrate containing immobilized folate binding protein (FBP) is used to measure the adsorption of FA conjugated AuNPs. The immobilization of the FBP and the binding of the FA conjugated AuNPs are characterized and optimized. It is shown that free FA in solution can be quantitatively measured by competition for the surface binding sites with the functionalized AuNPs. We demonstrate that the dynamic range can be lowered from micromolar to nanomolar by simply decreasing the concentration of FA conjugated AuNPs, thus lowering the limit of detection to 2.9 nM. This type of competitive assay can be applied to a range of small molecules, which paves the way for future multiplexed analysis of metabolites using SPR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".