Application of real-time PCR for quantitative detection of<i>Campylobacter jejuni</i>in poultry, milk and environmental water
Bibliographic record
Abstract
Campylobacter jejuni is a leading human food-borne pathogen. The rapid and sensitive detection of C. jejuni is necessary for the maintenance of a safe food/water supply. In this article, we present a real-time polymerase chain reaction (PCR) assay for quantitative detection of C. jejuni in naturally contaminated poultry, milk and environmental samples without an enrichment step. The whole assay can be completed in 60 min with a detection limit of approximately 1 CFU. The standard curve correlation coefficient for the threshold cycle versus the copy number of initial C. jejuni cells was 0.988. To test the PCR system, a set of 300 frozen chicken meat samples, 300 milk samples and 300 water samples were screened for the presence of C. jejuni. 30.6% (92/300) of chicken meat samples, 27.3% (82/300) of milk samples, and 13.6% (41/300) of water samples tested positive for C. jejuni. This result indicated that the real-time PCR assay provides a specific, sensitive and rapid method for quantitative detection of C. jejuni. Moreover, it is concluded that retail chicken meat, raw milk and environmental water are commonly contaminated with C. jejuni and could serve as a potential risk for consumers in eastern China, especially if proper hygienic and cooking conditions are not maintained.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".