Comparative Detection and Quantification of Arcobacter butzleri in Stools from Diarrheic and Nondiarrheic People in Southwestern Alberta, Canada
Bibliographic record
Abstract
Arcobacter butzleri has been linked to enteric disease in humans, but its pathogenicity and epidemiology remain poorly understood. The lack of suitable detection methods is a major limitation. Using comparative genome analysis, we developed PCR primers for direct detection and quantification ofA. butzleri DNA in microbiologically complex matrices. These primers, along with existing molecular and culture-based methods, were used to detectA. butzleri and enteric pathogens in stools of diarrheic and nondiarrheic people (n= 1,596) living in southwestern Alberta, Canada, from May to November 2008. In addition, quantitative PCR was used to compare A. butzleridensities in diarrheic and nondiarrheic stools.Arcobacter butzleriwas detected more often by PCR (59.6%) than by isolation methods (0.8%). Comparison by PCR-based detection found no difference in the prevalence ofA. butzleri between diarrheic (56.7%) and nondiarrheic (45.5%) individuals. Rates of detection in diarrheic stools peaked in June (71.1%) and October (68.7%), but there was no statistically significant correlation between the presence ofA. butzleri and patient age, sex, or place of habitation. Densities ofA. butzleriDNA in diarrheic stools (1.6 ± 0.59 log10 copies mg(-1)) were higher (P= 0.007) than in nondiarrheic stools (1.3 ± 0.63 log10copies mg(-1)). Of the 892 diarrheic samples that were positive for A. butzleri, 74.1% were not positive for other bacterial and/or viral pathogens. The current study supports previous work suggesting that A. butzleri pathogenicity is strain specific and/or dependent on other factors, such as the level of host resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".