Development of <i>cpn60</i> -Based Real-Time Quantitative PCR Assays for the Detection of 14 <i>Campylobacter</i> Species and Application to Screening of Canine Fecal Samples
Bibliographic record
Abstract
Campylobacter species are important organisms in both human and animal health. The identification of Campylobacter currently requires the growth of organisms from complex samples and biochemical identification. In many cases, the condition of the sample being tested and/or the fastidious nature of many Campylobacter species has limited the detection of campylobacters in a laboratory setting. To address this, we have designed a set of real-time quantitative PCR (qPCR) assays to detect and quantify 14 Campylobacter species, C. coli, C. concisus, C. curvus, C. fetus, C. gracilis, C. helveticus, C. hyointestinalis, C. jejuni, C. lari, C. mucosalis, C. rectus, C. showae, C. sputorum, and C. upsaliensis, directly from DNA extracted from feces. By use of a region of the cpn60 (also known as hsp60 or groEL) gene, which encodes the universally conserved 60-kDa chaperonin, species-specific assays were designed and validated. These assays were then employed to determine the prevalence of Campylobacter species in fecal samples from dogs. Fecal samples were found to contain detectable and quantifiable levels of C. fetus, C. gracilis, C. helveticus, C. jejuni, C. showae, and C. upsaliensis, with the majority of samples containing multiple Campylobacter species. This study represents the first report of C. fetus, C. gracilis, C. mucosalis, and C. showae detection in dogs and implicates dogs as a reservoir for these species. The qPCR assays described offer investigators a new tool to study many Campylobacter species in a culture-independent manner.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".