<i>Listeria monocytogenes</i> and <i>Salmonella enterica</i> Enteritidis Biofilms Susceptibility to Different Disinfectants and Stress-Response and Virulence Gene Expression of Surviving Cells
Bibliographic record
Abstract
Disinfection of food contact surfaces is a challenging task, aggravated by bacteria's capacity to survive and/or resist antimicrobials by means of mechanisms not yet completely understood. This work evaluated the susceptibility of Listeria monocytogenes and Salmonella enterica biofilms to four disinfectants, and analyzed how those chemical agents influenced stress-response and virulence genes expression by surviving cells. Three strains of each bacterial species mentioned were used, and their biofilms were treated with sodium hypochlorite, benzalkonium chloride, hydrogen peroxide, and triclosan using the Calgary Biofilm Device. Expression of L. monocytogenes and S. enterica stress-response genes cplC and ropS, and virulence genes prfA and avrA, respectively, was analyzed through quantitative real-time polymerase chain reaction. Results showed sodium hypochlorite to have the lowest minimum biofilm eradication concentration values (3.125 μg/ml), whereas triclosan had the worst performance since no S. enterica biofilm eradication was achieved even at the maximum concentration used (4,000 μg/ml). L. monocytogenes stress-response gene and S. enterica virulence gene were significantly upregulated in surviving cells compared with controls. In general, this work points out sodium hypochlorite as the most effective disinfectant against biofilms of both species used, and L. monocytogenes biofilms to be more susceptible to disinfection than S. enterica biofilms. Moreover, it was found that disinfection surviving biofilm cells seem to develop a stress response and/or become more virulent, which may compromise food safety and potentiate public health risk.
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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".