Development and Optimization of a Novel Immunomagnetic Separation- Bacteriophage Assay for Detection of <i>Salmonella enterica</i> Serovar Enteritidis in Broth
Bibliographic record
Abstract
Salmonella is the second-leading cause of food-borne illness in most developed countries, causing diarrhea, cramps, vomiting, and often fever. Many rapid methods are available for detection of Salmonella in foods, but these methods are often insensitive or expensive or require a high degree of technical ability to perform. In this paper we describe development and characterization of a novel assay that utilizes the normal infection cycle of bacteriophage SJ2 for detection of Salmonella enterica serovar Enteritidis in broth. The assay consists of four main stages: (i) capture and concentration of target cells by using immunomagnetic separation (IMS); (ii) infection of the target bacterium with phage; (iii) amplification and recovery of progeny phage; and (iv) assay of progeny phage on the basis of their effect on a healthy population of host cells (signal-amplifying cells). The end point of the assay can be determined by using either fluorescence or optical density measurements. The detection limit of the assay in broth is less than 10(4) CFU/ml, and the assay can be performed in 4 to 5 h. The results of this study demonstrate that the IMS-bacteriophage assay is a rapid, simple, and sensitive technique for detection of Salmonella serovar Enteritidis in broth cultures which can be applied to preenriched food samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".