Exploring a novel method for inoculation to experimentally induce digital dermatitis in dairy calves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it