Reverse transcription – loop-mediated isothermal amplification assay for the rapid detection of pathogenic <i>Listeria monocytogenes</i> in meat products
Bibliographic record
Abstract
This study reports the use of reverse transcription – loop-mediated isothermal amplification (RT–LAMP) to detect Listeria monocytogenes in meat. The assay was designed to target the iap gene of L. monocytogenes, to which four primers, recognizing six distinct iap sites, were designed. We optimized the RT–LAMP conditions and established the following optimal systems: 60 min, 63 °C, 2.0 mmol/L MgSO4, 1.0 mol/L betaine, 2.0 mmol/L dNTPs, 320 U/mL Bst DNA polymerase, 0.4 μmol/L outer primers, and 0.8 μmol/L inner primers. The RT–LAMP amplification products were identified by a visible white Mg2P2O7 precipitate or electrophoresis on a 2% agarose gel. RT–LAMP has a sensitivity of 7.3 × 101 CFU/mL, which is 2-fold higher than that of LAMP. When commercially available raw meat samples (including beef, pork, mutton, and rabbit) were analyzed simultaneously with RT–LAMP and the Chinese National Standard GB 4789.30-2016, their abilities to detect L. monocytogenes were the same. Samples containing L. monocytogenes killed by 15 psi at 121 °C for 15 min were used to confirm the specificity of RT–LAMP for live microorganisms. Thus, we used RT–LAMP to efficiently detect L. monocytogenes in meat products.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".