Rapid Detection of Norovirus Using Paper-based Microfluidic Device
Bibliographic record
Abstract
Abstract Noroviruses (NoV) are the leading cause of outbreak of acute gastroenteritis worldwide. A substantial effort has been made in the development of analytical devices for rapid and sensitive food safety monitoring via the detection of foodborne bacteria, viruses and parasites. Conventional analytical approaches for noroviruses suffer from some critical weaknesses: labor-intensive, time-consuming, and relatively low sensitivity. In this study, we developed a rapid and highly sensitive biosensor towards point-of-care device for noroviruses based on 6-carboxyfluorescein (6-FAM) labeled aptamer and nanomaterials, multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO). In an assay, the fluorescence of 6-FAM labeled aptamer was quenched by MWCNTs or GO via fluorescence resonance energy transfer (FRET). In the presence of norovirus, the fluorescence would be recovered due to the release of the 6-FAM labeled aptamer from MWCNTs or GO. An easy-to-make paper-based microfluidic platform made by nitrocellulose membrane was used to conduct the assay. The quantitative detection of norovirus virus-like particles (NoV VLPs) was successfully performed. A linear range of 0-12.9 μg/mL with a detection limit of 40 pM and 30 pM was achieved for the MWCNTs and GO based paper sensors, respectively. The results suggested the developed paper-based microfluidic device is simple, cost-effective and holds the potential of rapid in situ visual determination for noroviruses with remarkable sensitivity and specificity, which provides a new way for early identification of NoV and thereby an early intervention for preventing the spread of an outbreak.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".