Life on the outside: role of biofilms in environmental persistence of Shiga-toxin producing Escherichia coli
Bibliographic record
Abstract
Escherichia coli is a heterogeneous species that can be part of the normal flora of humans but also include strains of medical importance. Among pathogenic members, Shiga-toxin producing E. coli (STEC) are some of the more prominent pathogenic E. coli within the public sphere. STEC disease outbreaks are typically associated with contaminated beef, contaminated drinking water, and contaminated fresh produce. These water- and food-borne pathogens usually colonize cattle asymptomatically; cows will shed STEC in their feces and the subsequent fecal contamination of the environment and processing plants is a major concern for food and public safety. This is especially important because STEC can survive for prolonged periods of time outside its host in environments such as water, produce, and farm soil. Biofilms are hypothesized to be important for survival in the environment especially on produce, in rivers, and in processing plants. Several factors involved in biofilm formation such as curli, cellulose, poly-N-acetyl glucosamine, and colanic acid are involved in plant colonization and adherence to different surfaces often found in meat processing plants. In food processing plants, contamination of beef carcasses occurs at different stages of processing and this is often caused by the formation of STEC biofilms on the surface of several pieces of equipment associated with slaughtering and processing. Biofilms protect bacteria against several challenges, including biocides used in industrial processes. STEC biofilms are less sensitive than planktonic cells to several chemical sanitizers such as quaternary ammonium compounds, peroxyacetic acid, and chlorine compounds. Increased resistance to sanitizers by STEC growing in a biofilm is likely to be a source of contamination in the processing plant. This review focuses on the role of biofilm formation by STEC as a means of persistence outside their animal host and factors associated with biofilm formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".