Infectivity responses of Salmonella enterica to bacteriophages on maize seeds and maize sprouts
Bibliographic record
Abstract
Salmonella enterica (S. enterica) is a major foodborne pathogen leading to a large number of outbreaks and bringing food safety concerns to sprouts. The control of S. enterica on maize sprouts is important because raw maize sprouts have been gaining attention as a novel superfood. Compared to conventional chemical methods, the applications of bacteriophages are regarded as natural and organic. This study investigated the effects of a 2 h phage cocktail (SF1 and SI1, MOI 1000) soaking on reducing the populations of three Salmonella enterica strains: S. Enteritidis S5-483, S. Typhimurium S5-536, and S. Agona PARC5 on maize seeds and during the storage of maize sprouts. The results showed that the phage cocktail treatment effectively reduced populations of S. enterica strains by 1–3 log CFU/g on maize seeds and decreased population of S. Agona PACR5 by 1.16 log CFU/g on maize sprouts from 7.55 log CFU/g at day 0 of the storage period. On the other hand, the upregulations of flagella gene pefA by 1.5-folds and membrane gene lpxA by 23-folds in S. Typhimurium S5-536 indicated a differential response to the phage cocktail treatment. Conversely, stress response genes ompR, rpoS, and recA, as well as the DNA repair gene yafD, were downregulated in S. Agona PARC5. This work shows the use of bacteriophages could contribute as a part of hurdle effect to reduce S. enterica populations and is beneficial to develop strategies for controlling foodborne pathogens in the production and storage of maize sprouts.
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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) | 0.001 | 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".