Solid-Phase Optical Sensing Techniques for Sensitive Virus Detection
Bibliographic record
Abstract
Viral infections can endanger public health by causing serious illness, leading to pandemics and burdening healthcare systems. Moreover, in the situation of a global spread, disruptions occur in every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has huge implications for saving people’s lives, preventing spread of the diseases, and minimizing social and economic damages. In the last decades, polymerase chain reaction (PCR) based techniques have been frequently used to detect viruses in the clinic. However, in a situation where rapid virus detection is the primary measure in preventing the spread, as in the case of ongoing COVID-19 pandemic, disadvantages of PCR, such as long processing times and requirement of sophisticated laboratory instruments, have been faced. Due to the urgent need for accurate techniques for virus detection, biosensor systems involved in many applications in biological detection are being developed for rapid, real-time, and high-throughput detection of viruses. Among various sensing platforms, optical devices are of great interest due to their advantages such as high sensitivity and direct readout. In the current review, usability of sensing techniques depending on optical phenomena, such as fluorescence-based sensors, surface plasmon resonance (SPR), surface enhanced Raman scattering (SERS), optical resonators and interferometry-based platforms, is discussed for virus diagnostics applications. Then, we focus on an interferometric biosensor developed by our group, 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".