Exploring a novel method for inoculation to experimentally induce digital dermatitis in dairy calves
Bibliographic record
Abstract
Digital dermatitis (DD) is a polymicrobial, multifactorial hoof disorder with an undefined etiology, and existing animal models fail to induce a fast, standardized infection in bovine skin. This study explored bacterial inoculation methods for bacterial skin penetration to optimize a DD infection model. A pilot study tested various microneedles in calf feet, identifying tattoo needles as the most effective for skin penetration based on the ink depth and amount. Two animal trials followed. In trial 1, eight calves were inoculated once with DD lesion macerates (n = 5, 10 feet) or a sham-inoculum (n = 3, 6 feet). Light wraps were applied, and calves were monitored for 14 days, but DD lesions failed to develop. In trial 2, five calves underwent a 3-day pretreatment with foot wrapping and daily water application before being inoculated with a mixture of pure isolates (n = 2, 8 feet) or lesion macerates (n = 3, 12 feet). Monitoring continued for three weeks, with swabbing every three days. On day 18, skin biopsies were taken. Once again, DD lesions failed to develop. DD-associated Treponema spp. were detected in swabs from days 2 to 9 in both groups, while non-Treponema anaerobes persisted in the pure isolates group until day 18. In biopsies, only DD-Treponema spp. were detected (12/20), and the presence of spirochetes was confirmed by silver staining. Although tattoo-based inoculation successfully delivered inoculum, bacteria failed to replicate and induce lesions, likely due to unknown missing factors necessary for DD pathogenesis in this model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".