Veillonella dispar and V. atypica increased the growth of Listeria monocytogenes in liquid culture and biofilm conditions
Bibliographic record
Abstract
Listeria monocytogenes (L. monocytogenes) is a foodborne pathogen that causes severe illness in high-risk groups who face a mortality rate of 15% to 20% with exposure to this deadly bacterium. L. monocytogenes poses a significant food safety concern due to its ability to withstand the adverse conditions encountered in food production environments. Prevention of its entry into the ready-to-eat (RTE) processing environment is crucial, and consequently, preventing its establishment within the environmental microbiota of slaughterhouses-the preceding stage in the production chain-is essential. This can be a challenge because L. monocytogenes has the ability to create and persist in biofilms in association with microorganisms. The role of the accompanying microbiota in the survival and density of L. monocytogenes has been shown to range from having antagonistic to synergetic effects. The aim of the present study was to validate a positive association previously identified using bioinformatic tools between the presence of Veillonella spp. on conveyor belt surfaces of the cutting room of a swine slaughterhouse and the relative abundance of L. monocytogenes. Veillonella dispar (V. dispar) and Veillonella atypica (V. atypica) showed statistically significant positive effects on the growth and survival of the pathogen in both planktonic cultures and in biofilms tested under static and dynamic conditions. These effects of Veillonella appear to be mediated through compounds secreted or made available by the bacterium since contact with the supernatants of Veillonella cultures was sufficient to induce L. monocytogenes growth enhancement. This increase is primarily due to the live cell mass, suggesting that Veillonella acts at the L. monocytogenes cell population level rather than on the biofilm matrix. We believe that our results represent a step toward a better L. monocytogenes food safety risk assessment and could contribute to the development of better strategies against this pathogen.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
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".