Bacteriophage cocktail significantly reduces Escherichia coli O157
Bibliographic record
Abstract
Foods contaminated with Escherichia coli O157:H7 cause more than 63,000 foodborne illnesses in the United States every year, resulting in a significant economic impact on medical costs and product liabilities. Efforts to reduce contamination with E. coli O157:H7 have largely focused on washing, application of various antibacterial chemicals, and gamma-irradiation, each of which has practical and environmental drawbacks. A relatively recent, environmentally-friendly approach proposed for eliminating or significantly reducing E. coli O157:H7 contamination of foods is the use of lytic bacteriophages as biocontrol agents. We found that EcoShield™, a commercially available preparation composed of three lytic bacteriophages specific for E. coli O157:H7, significantly (p < 0.05) reduced the levels of the bacterium in experimentally contaminated beef by ≥ 94% and in lettuce by 87% after a five minute contact time. The reduced levels of bacteria were maintained for at least one week at refrigerated temperatures. However, the one-time application of EcoShield™ did not protect the foods from recontamination with E. coli O157:H7. Our results demonstrate that EcoShield™ is effective in significantly reducing contamination of beef and lettuce with E. coli O157:H7, but does not protect against potential later contamination due to, for example, unsanitary handling of the foods post processing.
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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) | 0.000 | 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".