RAPID DETECTION OF GERMINATING <i>BACILLUS CEREUS</i> CELLS USING FLUORESCENT <i>IN SITU</i> HYBRIDIZATION
Bibliographic record
Abstract
ABSTRACT Methods for the specific detection of Bacillus spores are needed in many situations such as the recognition of food poisoning. This study presents an experimental design in order to find the best combination of germination conditions leading to a rapid and detectable fluorescent in situ hybridization (FISH) signal from Bacillus cereus spores present in pure cultures and milk samples. B. cereus ATCC 14579 and HER 1414 were incubated in 20 different growth media by using a combination of various germinants such as sugars, amino acids and dipicolinic acid. Also, three different germination factors were tested: incubation temperature, inoculum concentration and a heat shock treatment. Permeabilization procedure and hybridization time were optimized on the best germination condition found. B. cereus‐specific FISH probes were validated under the optimized condition and in detection of spiked B. cereus spores in 1% ultra heat‐treated milk samples. FISH‐labeled cells were detected by using flow cytometry, and the results were confirmed by fluorescence microscopy. The optimal condition allows the detection of B. cereus spores in less than 2 h. Overall, a ninefold reduction in total time for detection was achieved when comparing with previous works. Therefore, the permeabilization and hybridization optimizations mentioned in this study are major improvements for the detection time of B. cereus spores. PRACTICAL APPLICATIONS By using the optimized conditions of germination/outgrowth, permeabilization and hybridization, the detection of 103 cfu/mL of Bacillus cereus spores using fluorescent in situ hybridization is possible within 2 h in milk sample.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".