Pork juice promotes biofilm formation in <i>Listeria monocytogenes</i>
Bibliographic record
Abstract
Abstract A total of 47 Listeria monocytogenes isolates were separated and identified from 153 retailed raw meat samples in Shanghai area with the highest contamination rate in pork meat (20.34%). Using multiplex PCR, these isolates were divided into 3 serogroups: 1/2a‐3a, 1/2b‐3b‐7, and 1/2c‐3c, and 1/2a‐3a was predominant in these isolates. Calgary device was introduced to cultivate biofilm, and crystal violet staining and viable cell enumeration was used to determine the quantity of biofilm formation. Meat juices significantly impacted the biofilm formation, and L. monocytogenes had the highest biofilm‐forming ability in pork juice. However, there was no significant difference on biofilm formation among different serotypes and sources. When cocultured in pork juice, the cell numbers of Salmonella enterica, Staphylococcus aureus, and L. monocytogenes had a significant decrease or decreasing tendency compared to monospecies biofilm, which revealed a competitive interaction among the three species. Practical applications It is aimed to investigate the biofilm formation by L. monocytogenes on plastic surface in different meat juices (beef, chicken, and pork) and the interaction among the multispecies biofilm of Salmonella, Staphylococcus aureus, and L. monocytogenes in pork juice. Meat juice was used as a simulation of the real condition in meat processing environment, and the result can give suggestions to meat industry to keep a close eye on raw pork product.
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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) | 0.001 | 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".