Capabilities of Double-Resonance LPG and SPR Methods for Hypersensitive Detection of SARS-CoV-2 Structural Proteins: A Comparative Study
Bibliographic record
Abstract
The danger of the emergence of new viral diseases and their rapid spread demands apparatuses for continuous rapid monitoring in real time. This requires the creation of new bioanalytical methods that overcome the shortcomings of existing ones and are applicable for point-of-care diagnostics. For this purpose, a variety of biosensors have been developed and tested in proof-of-concept studies, but none of them have been introduced for commercial use so far. Given the importance of the problem, in this study, long-period grating (LPG) and surface plasmon resonance (SPR) biosensors, based on antibody detection, were examined, and their capabilities for SARS-CoV-2 structural proteins detection were established. Supersensitive detections of structural proteins in the order of several femtomoles were achieved by the LPG method, while the SPR method demonstrated a sensitivity of about one hundred femtomoles. The studied biosensors are compatible in sensitivity with ELISA and rapid antigen tests but, in contrast, they are quantitative, which makes them applicable for acute SARS-CoV-2 infection detection, especially during the early stages of viral replication.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".