Interactions Between Infectious Foodborne Viruses and Bacterial Biofilms Formed on Different Food Contact Surfaces
Bibliographic record
Abstract
Abstract Bacterial biofilms contribute to contamination, spoilage, persistence, and hygiene failure in the food industry, but relatively little is known about the behavior of foodborne viruses evolving in the complex communities that make up biofilm. The aim of this study was to evaluate the association between enteric viruses and biofilms on food contact surfaces. Formed biofilms of mono- and multispecies cultures were prepared on glass, stainless steel, and polystyrene coupons and 105 pfu/ml of murine norovirus, rotavirus, and hepatitis A virus were added and incubated for 15 min, 90 min, and 24 h. The data obtained clearly demonstrate that the presence of biofilms generally influences the adhesion of enteric viruses to different surfaces. Many significant increases in attachment rates were observed, particularly with rotavirus whose rate of viral infectious particles increased 7000 times in the presence of Pseudomonas fluorescens on polystyrene after 24 h of incubation and with hepatitis A virus, which seems to have an affinity for the biofilms formed by lactic acid bacteria. Murine norovirus seems to be the least influenced by the presence of biofilms with few significant increases. However, the different factors surrounding this association are unknown and seem to vary according to the viruses, the environmental conditions, and the composition of the biofilm.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".