Microbial Dynamics in Mixed-Culture Biofilms of Salmonella Typhimurium and Escherichia coli O157:H7 and Bacteria Surviving Sanitation of Conveyor Belts of Meat Processing Plants
Bibliographic record
Abstract
Biofilm formation can lead to the persistence of Salmonella Typhimurium (ST) and E. coli O157:H7 (O157). This study investigated the impact of meat processing surface bacteria (MPB) on biofilm formation by O157 (non-biofilm former; NF) and ST (strong biofilm former; BF). MPB were recovered from the contacting surfaces (CS), non-contacting surfaces (NCS), and roller surfaces (RS) of a beef plant conveyor belt after sanitation. O157 and ST were co-inoculated with MPB (CO), or after a delay of 48 h (IS), into biofilm reactors containing stainless steel coupons and incubated at 15 °C for up to 144 h. Coupons were withdrawn at various intervals and analyzed by conventional plating and 16S rRNA gene amplicon sequencing. The total bacterial counts in biofilms reached approximately 6.5 log CFU/cm2, regardless of MPB type or development mode. The mean counts for O157 and ST under equivalent conditions mostly did not differ (p > 0.05), except for the IS set at 50 h, where no O157 was recovered. O157 and ST were 1.6 ± 2.1% and 4.7 ± 5.0% (CO) and 1.1 ± 2.2% and 2.0 ± 2.8% (IS) of the final population. Pseudomonas dominated the MPB inocula and biofilms, regardless of MPB type or development mode. Whether or not a pathogen is deemed BF or NF in monoculture, its successful integration into complex multi-species biofilms ultimately depends on the presence of certain other residents within the biofilm.
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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".