Evaluation of a new automated viral RNA extraction platform for hepatitis A virus and human norovirus in testing of berries, lettuce, and oysters
Bibliographic record
Abstract
Fruits, vegetables, and shellfish are often associated with outbreaks of illness caused particularly by human norovirus (HuNoV) and hepatitis A virus (HAV), the leading causative agents of foodborne illness worldwide. The aim of this study was to evaluate a new automated nucleic acid extraction platform (EGENE-UP EASYPREP) for enteric viruses in several at-risk food matrices and to test its limit of detection in comparison to a semi-automated method (EGENE-UP) using Boom methodology for nucleic acid extraction as suggested in the reference method ISO 15216-2:2019. Fresh and frozen raspberries, frozen blackberries, romaine lettuce and oyster digestive glands were artificially contaminated with HAV, HuNoV GII.4 or HuNoV GI.7 at 102, 103 or 104 genome copies/sample. Virus was then recovered from the food matrix using the ISO method. Viral RNA extracted from frozen berry samples by the automated system was purified on a column for additional removal of RT-qPCR inhibitors. For fresh raspberry, oysters, and romaine lettuce, the two extraction platforms were deemed equivalent. For frozen raspberry, the automated platform appeared to be more efficient for viral recovery, particularly for HAV and HuNoV GI at lower concentrations. With frozen blackberries, the two platforms may be considered equivalent for all targeted viruses. However, the automated method led to less sample-associated inhibition of the PCR, 56.5 % of samples versus 95.0 % for the semi-automated. We thus found that the automated extraction can be performed easily by users while obtaining equivalent or even superior results to the ISO 15216-2:2019 method, and therefore appears to be suitable for routine sanitary monitoring in food processing and for tracing outbreaks of illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".