Quantifying and mapping digital dermatitis–associated bacteria in lesion and nonlesion body sites and dairy farm environment
Bibliographic record
Abstract
The source of infection of digital dermatitis (DD), an infectious lameness condition, is still uncertain. In this cross-sectional study, we aimed to identify potential reservoirs of DD bacteria in dairy cattle body sites with different stages of the disease and farm environments. We collected skin swabs from 85 dairy cows from 5 herds, 3 with and 2 without DD, from foot, hock, and udder cleft skin (with lesions or not), saliva, urine, and feces. We also obtained environmental samples. Real-time quantitative PCR targeted Treponema phagedenis, Treponema medium, Treponema pedis, Porphyromonas levii, Bacteroides pyogenes, Fusobacterium necrophorum, and Fusobacterium mortiferum. Digital dermatitis-associated Treponema spp. were exclusively detected in DD-affected herds in DD-foot and other skin lesions, healthy skin, saliva, and environmental samples. In contrast, the non-Treponema spp. were found in samples from both DD-negative and affected herds. As expected, DD lesions had higher bacterial loads than healthy skin. Interestingly, similar counts were observed in udder cleft lesions, indicating a potential opportunistic behavior on compromised skin. None of the targeted species were detected in fecal samples, but P. levii, B. pyogenes, and F. necrophorum were detected in urine. All 7 species were detected in saliva, although in low quantities. No associations were observed between the presence of each bacterial species in DD lesions and urine; however, there was an association between the presence of DD-Treponema spp. in lesions and saliva, hock, and udder skin. Feces and urine do not seem to be a DD bacteria primary source, but saliva and other skin lesions may play a role. Longitudinal studies would improve our understanding of DD-associated bacteria's transient or persistent presence in these sites. Investigating the sources of DD-associated bacteria will guide future interventions to minimize bacterial shedding and transmission, ultimately more effectively reducing bacterial load, transmission, and sources of infection in dairy herds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".