A multipathogen selective enrichment broth for simultaneous growth of<i>Salmonella enterica</i>serovar Enteritidis,<i>Staphylococcus aureus</i>, and<i>Listeria monocytogenes</i>
Bibliographic record
Abstract
A selective enrichment broth (SSL) was formulated to allow concurrent growth of 3 prominent food-borne pathogens: Salmonella enterica serovar Enteritidis, Staphylococcus aureus, and Listeria monocytogenes. Nalidixic acid, lithium chloride, and potassium tellurite were added as the selective agents, while sodium pyruvate and mannitol were employed as the supplemented elements. In the individual growth trial, the target pathogens were capable of growing in SSL to as high as 7-8 log(10) colony-forming units (CFU)/mL after 24 h incubation at 37 degrees C when being inoculated at 50-100 CFU/mL. In the simultaneous growth trial, the 3 combined target pathogens showed similar growth rates. The results show that SSL could support the successful simultaneous enrichment of 3 pathogens; however, SSL inhibited the growth of nontarget bacteria. In the artificial contaminated raw beef and ready-to-eat chicken, a high recovery of these 3 target pathogens was obtained in SSL. Finally, Salmonella Enteritidis, Staphylococcus aureus, and L. monocytogenes were detected from 710 suspicious food samples by SSL with real-time PCR, and no false-positive or -negative results were reported. In summary, SSL has been shown to be a suitable broth for the simultaneous detection of the 3 prominent food-borne pathogens by multipathogen detection on a single-assay platform.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".