Bioprotective Strategies to Control Listeria monocytogenes in Food Products and Processing Environments
Bibliographic record
Abstract
is a highly pathogenic foodborne bacteria that is responsible for listeriosis, a serious infectious disease characterized by a high mortality rate among vulnerable populations such as the immunocompromised, pregnant women and the elderly. Moreover, its pathogenicity, its capacity to persist in food processing environments and proliferate in adverse conditions like low temperatures and high salt concentrations, and its ability to generate biofilms make it a major contaminant affecting ready-to-eat food products. In response to this potential public health threat, the agrifood industry has traditionally adopted conventional control methods including thermal treatment and chemical preservatives. However, these approaches have their limitations, especially in terms of efficacy, organoleptic impact and consumer acceptability. In this context, innovative biocontrol strategies are increasingly attracting interest among scientific and industrial stakeholders. This review reports a global overview of the mechanisms involved in the pathogenicity and survival abilities of Listeria monocytogenes in food commodities and processing equipment, as well as a current state of the use of protective cultures and antimicrobial peptides as promising biological-based approaches to control and prevent Listeria monocytogenes in food products and food processing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".