Digital SERS Protocol Using Au Nanoparticle-Based Extrinsic Raman Labels for the Determination of SARS-CoV-2 Spike Protein in Saliva Samples
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS)-based immunoassays have several advantages, such as high sensitivity and multiplex capabilities; they are emerging as a potential avenue for early disease diagnosis and screening. Here, we focused on SERS-based heterogeneous immunoassays, in which the number of extrinsic Raman labels (ERLs) at the sensor surface is related to the concentration of the intended target. The ERLs are made of gold nanoparticles and are constructed to be selective to the target and to boost the signal of a Raman reporter. However, as the concentration of the target biomarker decreases, the number of ERLs per unit of area (mm 2 ) also decreases, leading to a small number of ERLs being probed within an exciting laser spot. This poor sampling adds to the large intensity variations inherent to the SERS effect, resulting in a loss in the linearity between the SERS signal and the marker/target analyte concentration. This characteristic has rendered SERS-based immunoassays unreliable for quantification at low bioanalyte concentrations. We propose the use of a digital quantification protocol to overcome this problem. A SERS-based sandwich immunoassay was developed for the detection of the SARS-CoV-2 S1–S2 spike protein in saliva. A conventional data analysis that relates SERS intensities to concentration was compared to the digital protocol for the same dataset. The digital SERS assay presented an LOD of 6.3 ng·mL –1 or 34.9 pM and an LOQ of 19.0 ng·mL –1 or 105.7 pM within a 95% confidence level. These metrics show an 11-fold improvement compared to the conventional data analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".