Associations between digital dermatitis lesion grades in dairy cattle and the quantities of four Treponema species
Bibliographic record
Abstract
Digital dermatitis (DD) presents as painful, ulcerative or proliferative lesions that lead to bovine lameness affecting economic efficiency and animal welfare. Although DD etiological agent(s) have not been established, it is widely accepted that DD is a polymicrobial disease significantly associated with species of Treponema and the non-linear disease progression may be attributed to interactions among infecting bacteria. We postulated the morphological changes associated with DD lesion grades are related to interactions among infecting species of Treponema. We developed a novel species-specific qPCR that can identify the absolute abundance of the four of the most common species of Treponema in DD, T. phagedenis, T. medium, T. pedis and T. denticola, in a single reaction. We found species abundance and the number of distinct Treponema species present is higher in active, ulcerative lesions than in healing lesions, chronic lesions, and DD-free skin. Treponema spp. were present in both DD-free skin and M3 lesions following treatment with oxytetracycline. We have also found positive correlations among T. phagedenis, T. medium and T. pedis indicating they are significantly more likely to be found together than apart and their absolute quantities tend to increase together, a relationship which is not present with T. denticola. Further, we found Treponema, particularly viable T. denticola, in lesions 5 days post treatment with oxytetracycline (M3). Our findings suggest that pathogenicity may be closely associated with Treponema abundance, particularly T. phagedenis, T. medium and T. pedis, and interactions among them, independent of T. denticola. Our results provide a novel, consistent method to identify species of Treponema within DD lesions and associate Treponema spp. and abundance with morphological changes related to host pathogenicity.
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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 | 0.001 | 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 | 0.001 |
| 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".