Examining the Persistence of Human Coronaviruses on Fresh Produce
Bibliographic record
Abstract
Abstract Human coronaviruses (HCoVs) are mainly associated with respiratory infections. However, there is evidence that highly pathogenic HCoVs, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome (MERS-CoV), infect the gastrointestinal (GI) tract and are shed in the fecal matter of the infected individuals. These observations have raised questions regarding the possibility of fecal-oral route as well as foodborne transmission of SARS-CoV-2 and MERS-CoV. Studies regarding the survival of HCoVs on inanimate surfaces demonstrate that these viruses can remain infectious for hours to days, however, to date, there is no data regarding the viral survival on fresh produce, which is usually consumed raw or with minimal heat processing. To address this knowledge gap, we examined the persistence of HCoV-229E, as a surrogate for highly pathogenic HCoVs, on the surface of commonly consumed fresh produce, including: apples, tomatoes and cucumbers. Herein, we demonstrated that viral infectivity declines within a few hours post-inoculation (p.i) on apples and tomatoes, and no infectious virus was detected at 24h p.i, while the virus persists in infectious form for 72h p.i on cucumbers. The stability of viral RNA was examined by droplet-digital RT-PCR (ddRT-PCR), and it was observed that there is no considerable reduction in viral RNA within 72h p.i.
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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 | 0.000 | 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) | 0.002 | 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 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".