Counteracting Bacterial Motility: A Promising Strategy to Narrow Listeria monocytogenes Biofilm in Food Processing Industry
Bibliographic record
Abstract
Listeria monocytogenes (L. monocytogenes) is often associated with processed food as it can form biofilms that represent a source of contamination at all stages of the manufacturing chain. The control and prevention of biofilms in food-processing plants are of utmost importance. This study explores the efficacy of prospect molecules for counteracting bacterial mechanisms leading to biofilm formation. The compounds included the phytomolecule tomatidine, zinc chloride (ZnCl2), ethylenediaminetetraacetic acid (EDTA), and a more complexed mixture of bacterial compounds from coagulase-negative staphylococci (CNS exoproducts). Significant inhibition of L. monocytogenes biofilm formation was evidenced using a microfluidic system and confocal microscopic analyses (p < 0.001). Active molecules were effective at an early stage of biofilm development (≥50% of inhibition) but failed to disperse mature biofilms of L. monocytogenes. According to our findings, prevention of surface attachment was associated with a disruption of bacterial motility. Indeed, agar cell motility assays demonstrated the effectiveness of these molecules. Overall, results highlighted the critical role of motility in biofilm formation and allow to consider flagellum-mediated motility as a promising molecular target in control strategies against L. monocytogenes in food processing environments.
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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".