Hybrid paper/PDMS microfluidic device integrated with RNA extraction and recombinase polymerase amplification for detection of norovirus in foods
Bibliographic record
Abstract
ABSTRACT Human norovirus (HuNoV) is recognized as the leading causative agent of foodborne outbreaks of epidemic gastroenteritis. Consequently, there is a high demand for developing point-of-care testing for HuNoV. We developed an origami microfluidic device that facilitates rapid detection of murine norovirus 1 (MNV-1), a surrogate for HuNoV, encompassing the entire process from sample preparation to result visualization. This process includes RNA absorption via a paper strip, RNA amplification using recombinase polymerase amplification (RPA), and a lateral flow assay for signal readout. The on-chip detection of MNV-1 was completed within 35 min, demonstrating 100% specificity to MNV-1 in our settings. The detection limit of this microfluidic device for MNV-1 was 200 PFU/mL, comparable to the in-tube RPA reaction. It also successfully detected MNV-1 in lettuce and raspberries at concentrations of 170 PFU/g and 230 PFU/g, respectively, without requiring extra concentration steps. This device demonstrates high compatibility with isothermal nucleic acid amplification and holds significant potential for detecting foodborne viruses in agri-food products in remote and resource-limited settings. IMPORTANCE HuNoV belongs to the family of Caliciviridae and is a leading cause of acute gastroenteritis that can be transmitted through contaminated foods. HuNoV causes around one out of five cases of acute gastroenteritis that lead to diarrhea and vomiting, placing a substantial burden on the healthcare system worldwide. HuNoV outbreaks can occur when food is contaminated at the source (e.g., wild mussels exposed to polluted water), on farms (e.g., during crop cultivation, harvesting, or livestock handling), during packaging, or at catered events. The research outcomes of this study expand the approaches of HuNoV testing, adding value to the framework for routine testing of food products. This microfluidic device can facilitate the monitoring of HuNoV outbreaks, reduce the economic loss of the agri-food industry, and enhance food safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".