Detection of Emerging Food Pathogens in Chicken Meat Using Multiplex Polymerase Chain Reaction
Bibliographic record
Abstract
A study was undertaken to develop a multiplex PCR (m-PCR) protocol for simultaneous detection of Campylobacter jejuni and Listeria monocytogenes in chicken meat. The extraction of DNA was carried out using commercial DNA extraction kit, Phenol Chloroform and boiling method. Samples with OD ratio (260:280) between 1.7 and 1.9 were considered good in terms of concentration and purity and were used for PCR amplification. DNA extraction kit and Phenol Chloroform method revealed good OD value were used for sample extraction process. PCR and m-PCR amplification was carried out using genus specific primers were designed by targeting its Hyp (500 bp) and prfA (290 bp) gene for Campylobacter jejuni and Listeria monocytogenes respectively. Electrophoresis of amplified PCR products and gel documentation revealed 500 bp and 290 bp in 2% Agarose. The multiplex PCR technique was standardized using the reference strains with the similar amplification procedure. The minimum detection level (sensitivity) by mPCR for Campylobacter jejuni and Listeria monocytogenes was found to be 0.2 ng/ul and 1.0 ng/ul of DNA in a reaction mixture (25 ul). The developed multiplex PCR technique could detect Campylobacter jejuni and Listeria monocytogenes upto 3 × 105 and 3 × 104 CFU/ml of artificially inoculated meat homogenate. Around 60 chicken meat samples were collected from different regions of chennai and were screened for the presence of Campylobacter jejuni and Listeria monocytogenes. All the samples screened were not positive either for Campylobacter jejuni and Listeria monocytogenes. The negative samples were further checked by culture methods and good correlation between these two methods was observed. Hence, the m-PCR technique developed in this study can be used as a rapid screening test for detection of Campylobacter jejuni and Listeria monocytogenes from chicken meat within 24 hours.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".