Rapid identification of <i>Listeria</i> species and screening for variants by melting curve and high-resolution melting curve analyses of the intergenic spacer region of the rRNA gene
Bibliographic record
Abstract
The presence of any Listeria species in food may be an indicator of poor hygiene in food processing facilities. The biochemical identification of Listeria species is laborious and time consuming. Therefore, the development of novel identification methods that are rapid and simple to perform would be an asset. In this study, large intergenic spacer region amplicons of 343-374 bp were generated from 207 Listeria isolates. The melting curve analysis of these amplicons specifically classified all isolates into 6 Listeria species and generated 11 high-resolution melting (HRM) curve profiles. In this study, 3 HRM profiles were found in Listeria monocytogenes and Listeria innocua, and 2 were found in Listeria seeligeri. Sequencing of the amplicons representing these profiles revealed that each profile related to a unique sequence. The smallest difference recognized in this study was 1 nt. The results represented in this study show that HRM curve analysis of Listeria intergenic spacer sequences is a simple, quick, and reproducible method of simultaneously identifying 6 Listeria species and screening for variants. In particular, the completion of both reaction and analysis in a closed tube saves time by eliminating the separate steps and lowers the risk of contamination.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".