Examining the efficiency of porcine gastric mucin-coated magnetic beads in extraction of noroviruses from frozen berries
Bibliographic record
Abstract
Human norovirus is the leading cause of foodborne gastroenteritis worldwide. Due to the low infectious dose of noroviruses, sensitive methodologies are required to detect and characterize small numbers of viral particles that are found in contaminated foods. The ISO 15216 method, which is internationally recognized for detection of foodborne viruses from high-risk food commodities, is based on viral precipitation, followed by RNA extraction and identification of the viral genome by RT-PCR. Although the ISO 15216 method is efficient, it is time consuming and tedious, does not report on the viral infectivity, and is sensitive to the presence of RT-PCR inhibitors. Norovirus capture by the porcine gastric mucin conjugated magnetic beads (PGM-MB) was developed as an alternative virus recovery method. It relies on the integrity of the viral capsid being able to bind to PGM. PGM contains a variety of histo-blood group antigens (HBGAs) that act as norovirus receptors. Therefore, the PGM-MB method allows for extraction of noroviruses, with potentially intact viral capsids, from complex food matrices. The viral genome can then be released through heat-shock of the captured virus. For this reason, we performed a parallel comparison between the ISO 15216 method and the PGM-MB method in isolation and quantification of noroviruses from frozen raspberries. We have demonstrated that the efficiency of the PGM-MB method in extraction of murine norovirus (MNV) and human norovirus GII.4 from raspberries is equal or better than the ISO 15216 method, while the PGM-MB has fewer steps and shorter turnaround time. Moreover, the PGM-MB method is more efficient in removing the inhibitors prior to RT-PCR analysis.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".