Use of Human Intestinal Enteroids for Recovery of Infectious Human Norovirus from Berries and Lettuce
Bibliographic record
Abstract
Norovirus (NoV) is the leading cause of viral foodborne gastroenteritis globally. Currently, the gold standard for detecting NoV in clinical, food, and environmental samples is via molecular-based methods, primarily RT-PCR. Nevertheless, there is a great need for confirmatory assays that can determine the infectivity of viral particles recovered from contaminated matrices. The use of the human intestinal enteroids system (HIEs) has allowed for the expansion of norovirus replication, although it still suffers from limitations of strain preferences and the requirement of high titer stocks for infection. In this study, we wanted to explore the feasibility of using the HIEs to support the replication of NoV that had been recovered from representative food matrices that have been associated with foodborne illness. We first confirmed that HIEs can support the replication of several strains of NoV as measured by RT-qPCR. We subsequently chose two of those strains that reproducibly replicated, GII.4 and GII.6, to evaluate in a TCID50 assay and for future experiments. Infectious NoV could be recovered and quantified in the HIEs from lettuce, frozen raspberries, or frozen strawberries seeded with high titers of either of these strains. While many experimental challenges still remain to be overcome, the results of this study represent an important step toward the detection of infectious norovirus from representative produce items.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".