Detection and quantification of 14 Campylobacter species in pet dogs reveals an increase in species richness in feces of diarrheic animals
Bibliographic record
Abstract
BACKGROUND: The genus Campylobacter includes many species, some of which are known human and animal pathogens. Even though studies have repeatedly identified domestic dogs as a risk factor for human campylobacteriosis, our understanding of Campylobacter ecology in this reservoir is limited. Work to date has focused primarily on a limited number of species using culture-based methods. To expand our understanding of Campylobacter ecology in dogs, a collection of fecal samples from 70 healthy and 65 diarrheic pet dogs were examined for the presence and levels of 14 Campylobacter species using quantitative PCR. RESULTS: It was found that 58% of healthy dogs and 97% of diarrheic dogs shed detectable levels of Campylobacter spp., with C. coli, C. concisus, C. fetus, C. gracilis, C. helveticus, C. jejuni, C. lari, C. mucosalis, C. showae, C. sputorum and C. upsaliensis levels significantly higher in the diarrheic population. Levels of individual Campylobacter species detected ranged from 103 to 108 organisms per gram of feces. In addition, many individual samples contained multiple species of Campylobacter, with healthy dogs carrying from 0-7 detectable species while diarrheic dogs carried from 0-12 detectable species. CONCLUSIONS: These findings represent the largest number of Campylobacter species specifically tested for in animals and is the first report to determine quantifiable levels of Campylobacter being shed from dogs. This study demonstrates that domestic dogs can carry a wide range of Campylobacter species naturally and that there is a notable increase in species richness detectable in the diarrheic population. With several of the detected Campylobacter species known or emerging pathogens, these results are relevant to both ecological and public health discussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".