Correlation Between Infectivity and qRT-PCR Values for Murine Norovirus Recovered from Frozen Berries
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Bibliographic record
Abstract
Human norovirus (HuNoV) is the leading cause of acute gastroenteritis globally, with frozen berries frequently implicated in foodborne outbreaks. Current surveillance relies on quantitative reverse transcription PCR (qRT-PCR), which cannot differentiate between infectious and non-infectious viral particles, complicating risk assessment. This study is aimed to establish the minimum viral load on frozen berries detectable by qRT-PCR that corresponds to infectious virus, using murine norovirus (MNV) as a surrogate for HuNoV. Frozen raspberries were artificially inoculated with serial dilutions of MNV (7.1-1.0 log PFU/25 g) and processed using the ISO 15216:2017 method. Infectious virus was quantified by plaque assay, and viral RNA was detected by qRT-PCR. The limit of detection (LOD) for cell culture was 3.1 log PFU/25 g, whereas qRT-PCR extended sensitivity to 1.0 log PFU/25 g (Ct value at 36.7 ± 0.6), representing a 2-log difference. Recovery rates for infectious virus exceeded the ISO 15,216 minimum threshold (1%), and PCR inhibition was negligible. We next examined the extraction efficiency for both infectious MNV and its genetic material from frozen strawberries at inoculation levels higher than the LOD, and observed that the viral recovery from frozen strawberries is very similar to viral recovery from frozen raspberries with no significant differences between them. The disparity between LODs indicates that a substantial proportion of MNV genomes detected by qRT-PCR do not represent infectious particles, aligning with previous findings that one PFU may correspond to multiple genome copies. Given that many surveillance studies report high Ct values (> 35), our data suggest that such detections may not indicate viable virus, underscoring the importance of contextualizing qRT-PCR results with epidemiological evidence. These findings highlight the need for cautious interpretation of surveillance data, particularly for public health decision-making.
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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 it