Ultrafast Detection of SARS-CoV-2 Spike Protein (S) and Receptor-Binding Domain (RBD) in Saliva Using Surface-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
Controlling contagious diseases necessitates using diagnostic techniques that can detect infection in the early stages. Although different diagnostic tools exist, there are still challenges related to accuracy, rapidity, cost-effectiveness, and ease of use. Surface-enhanced Raman spectroscopy (SERS) is a rapid, simple, less expensive, and accurate method. We continue our previous work published on SERS detection of the SARS-CoV-2 receptor-binding domain (RBD) in water. In this work, we replace water with saliva to detect SARS-CoV-2 proteins at very low concentrations and during a very short time. We prepared a very low concentration of 10−9 M SARS-CoV-2 spike protein (S) and SARS-CoV-2 receptor-binding domain (RBD) in saliva to mimic a real case scenario. Then, we drop them on a SERS substrate. Using modified SERS measurements on the control and the sample containing the biomolecules, confirmed the sensitivity of the target identification. This technique provides different diagnostic solutions that are fast, simple, non-destructive and ultrasensitive. Simulation of the real-world of silicon wire covered with silver and gold, were performed using an effective and accurate tool, COMSOL Multiphysics software, for the enhancement properties study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".