Rapid determination of viable but non-culturable Campylobacter jejuni in agri-food products by loop-mediated isothermal amplification coupling propidium monoazide treatment
Bibliographic record
Abstract
Campylobacter is the leading cause of foodborne human diarrhea in Canada and worldwide. This microbe in the viable but non-culturable (VBNC) state can evade the detection by routinely used culture-based methods, remain viable for extended periods of time, resume their metabolic activity and virulence leading to infections and diseases, and subsequently pose a severe concern to public health, food safety and the economy. In this study, an assay combining loop-mediated isothermal amplification (LAMP) and propidium monoazide (PMA) treatment was developed to detect and quantify VBNC C. jejuni. PMA-qLAMP targeting the hipO gene showed 100% specificity to C. jejuni. The limit of detection was determined to be 8.77×10² CFU/mL in pure bacterial culture with good quantitative capacity ranging from 8.77×10² CFU/mL to 8.77×10⁷ CFU/mL. C. jejuni was induced into the VBNC state by incubation in 7% (w/v) NaCl over 48 h. VBNC C. jejuni cells were determined by PMA-qLAMP coupled with the plating assay and spiked into three agri-foods. The limits of detection of PMA-qLAMP were 1.58×10² CFU/mL, 3.78×10² CFU/g and 4.33×10² CFU/g in milk, chicken breast meat and romaine lettuce, respectively. PMA-qLAMP demonstrated a rapid (from 25 to 40 min), specific (100% inclusivity and 100% exclusivity), sensitive (1.58×10² - 8.77×10² CFU/mL) and quantitative (R² of 0.9937–0.9999) detection of VBNC C. jejuni in pure bacterial culture, chicken meat, milk, and lettuce. Considering the rising Campylobacter infections worldwide and serious concerns of VBNC bacteria, this detection method has extensive potential to be applied in the agri-food industry so as to reduce the burden of C. jejuni infections to the public health and economy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".