Long-amplicon propidium monoazide-PCR enumeration assay to detect viable Campylobacter and Salmonella
Bibliographic record
Abstract
AIMS: The effect of amplicon length on the ability of propidium monoazide-PCR (PMA-PCR) to reliably quantify viable cells without interference from dead cells was tested on heat- and ultraviolet (UV)-killed Salmonella enterica and Campylobacter jejuni, two important enteric pathogens of concern in environmental, food and clinical samples. METHODS AND RESULTS: PMA treatment followed by quantitative PCR (qPCR) amplification of short DNA fragments (<200 bp) resulted in incomplete signal inhibition of heat-treated Salm. enterica (3 log reduction) and Camp. jejuni (1 log reduction), whereas PCR amplification of a long DNA fragment (1·5 and 1·6 kb) completely suppressed the dead cell signal. PMA pretreatment of UV-irradiated cells did not affect PCR amplification, but long-amplicon PCR was shown to detect only viable cells for these samples, even without the addition of PMA. CONCLUSIONS: The long-amplicon PMA-PCR method was effective in targeting viable cells following heat and UV treatment and was applicable to enteric pathogens including Salmonella and Campylobacter that are difficult to enumerate using culture-based procedures. SIGNIFICANCE AND IMPACT OF THE STUDY: PCR amplicon length is important for effective removal of the dead cell signal in PMA pretreatment methods that target membrane-damaged cells, and also for inactivation mechanisms that cause direct DNA damage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".