Single- and Dual-Species Biofilm Formation by Shiga Toxin-Producing Escherichia coli and Salmonella, and Their Susceptibility to an Engineered Peptide WK2
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) and Salmonella enterica are important foodborne pathogens capable of forming both single- and multi-species biofilms. In this study, the mono- and dual-species biofilms were formed by STEC O113:H21 and Salmonella enterica serovar Choleraesuis 10708 on stainless steel in the presence of beef juice over 5 d at 22 °C. The dual-species biofilm mass was substantially (p < 0.05) greater than that produced by STEC O113:H21 or S. Choleraesuis 10708 alone. However, numbers (CFU/mL) of S. Choleraesuis 10708 or STEC O113:H21 cells in the dual-species biofilm were (p < 0.05) lower than their respective counts in single-species biofilms. In multi-species biofilms, the sensitivity of S. Choleraesuis 10708 to the antimicrobial peptide WK2 was reduced, but it was increased for STEC O113:H21. Visualization of the temporal and spatial development of dual-species biofilms using florescent protein labeling confirmed that WK2 reduced cell numbers within biofilms. Collectively, our results highlight the potential risk of cross-contamination by multi-species biofilms to food safety and suggest that WK2 may be developed as a novel antimicrobial or sanitizer for the control of biofilms on stainless steel.
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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) | 0.000 | 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".