Comparative quantification of <i>Campylobacter jejuni</i> from environmental samples using traditional and molecular biological techniques
Bibliographic record
Abstract
Campylobacter jejuni is one of the most common causes of gastroenteritis in the world. Given the potential risks to human, animal, and environmental health, the development and optimization of methods to quantify this important pathogen in environmental samples is essential. Two of the most commonly used methods for quantifying C. jejuni are selective plate counting and quantitative real-time PCR (qPCR). Unfortunately, little comparative research has been performed to evaluate the accuracy of these methods for quantification of C. jejuni in aqueous and solid matricies. In this study, the limit of detection and the level of resolution obtained using these 2 methods was evaluated for C. jejuni and compared with that of the common indicator organism Escherichia coli. The use of selective plate count media for quantification of C. jejuni resulted in a 0.7-1.2 log underestimation of cell concentrations, compared with qPCR in both water and column leachate samples, whereas E. coli concentrations were found to be similar with either technique. For C. jejuni, only the qPCR assay accurately measured 2-fold changes in cell concentrations in water samples, whereas concentrations of E. coli were accurately measured regardless of method. Based on these data, qPCR assays were found to be more accurate than selective plate counts for quantification of C. jejuni from environmental samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".