Effects of Beef Juice on Biofilm Formation by Shiga Toxin–Producing <i>Escherichia coli</i> on Stainless Steel
Bibliographic record
Abstract
Shiga toxin–producing Escherichia coli (STEC) are a leading cause of foodborne illnesses worldwide, with beef and beef products as a common food reservoir. STEC strains may be present in beef-processing environments in the form of biofilms. The exudate of raw beef, also referred to as beef juice, has been identified as an important source of bacterial contamination on food-processing surfaces. This study applied beef juice as a food-based model to study its effects on biofilm formation of six STEC isolates on stainless steel. Crystal violet staining and cell enumeration demonstrated that beef juice inhibited the biofilm formation of strains O113, O145, and O91 up to 24 h at 22°C, but that biofilm increased (p < 0.05) thereafter over 72 h. Biofilms formed by O157, O111, and O45 were not affected by the addition of beef juice over the whole incubation period. Electron microscopy showed that the morphology of biofilm cells was altered and more extracellular matrix was produced with beef juice than with M9 medium. The present study demonstrated that beef juice residues on stainless steel can enhance biofilm formation of some STEC strains. Thorough and frequent cleaning of meat residues and exudate during meat production and handling is critical to reduce STEC biofilm formation even at 13°C.
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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 | 0.000 | 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".