Solid-Phase Optical Sensing Techniques for Sensitive Virus Detection
Bibliographic record
Abstract
Viral infections can pose a major threat to public health by causing serious illness, leading to pandemics, and burdening healthcare systems. The global spread of such infections causes disruptions to every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has significant implications for saving lives, preventing the spread of the diseases, and minimizing social and economic damages. Polymerase chain reaction (PCR)-based techniques are commonly used to detect viruses in the clinic. However, PCR has several drawbacks, as highlighted during the recent COVID-19 pandemic, such as long processing times and the requirement for sophisticated laboratory instruments. Therefore, there is an urgent need for fast and accurate techniques for virus detection. For this purpose, a variety of biosensor systems are being developed to provide rapid, sensitive, and high-throughput viral diagnostic platforms, enabling quick diagnosis and efficient control of the virus's spread. Optical devices, in particular, are of great interest due to their advantages such as high sensitivity and direct readout. The current review discusses solid-phase optical sensing techniques for virus detection, including fluorescence-based sensors, surface plasmon resonance (SPR), surface-enhanced Raman scattering (SERS), optical resonators, and interferometry-based platforms. Then, we focus on an interferometric biosensor developed by our group, the single-particle interferometric reflectance imaging sensor (SP-IRIS), which has the capability to visualize single nanoparticles, to demonstrate its application for digital virus detection.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".