Investigating the Polymicrobial Nature of Bovine Digital Dermatitis in Natural Infections and Animal Models
Bibliographic record
Abstract
Digital Dermatitis (DD) is a multifactorial, polymicrobial skin disease on cattle’s feet and is the leading cause of lameness in Canadian dairy cattle, with an etiology that remains unclear. This thesis aimed to investigate the polymicrobial nature of DD by identifying potential bacterial reservoirs and investigating the roles of specific bacteria in lesion initiation in natural infections. Additionally, efforts were made to optimize a bovine infection model. Four studies were conducted, three field studies in commercial dairy farms and an experimental infection study. We relied on quantitative molecular techniques, real-time qPCR, targeting three DD-associated Treponema spp. and four other anaerobes. First, we validated swabs as an alternative to invasive skin biopsies in detecting and quantifying DD bacteria. We then mapped and quantified DD bacteria in potential reservoirs in dairy cows from herds with or without DD. Next, we assessed the temporal changes in bacterial counts preceding lesions and determined whether bacterial presence in reservoirs was persistent or transient in a longitudinal study. Finally, we explored a novel tattoo-based inoculation method to optimize DD bovine models. DD-Treponema spp. were detected only in DD-affected herds, while non-treponemal anaerobes were widespread in both affected and DD-negative herds. Although all target bacterial species were occasionally detected on healthy skin and in saliva, only Porphyromonas levii and Fusobacterium necrophorum seem to persist in these sites. Despite their detection in environmental samples, we did not detect any of the target species in feces. All target species increased in numbers before lesion onset, with sequential colonization starting with non-treponemes two weeks before DD occurred, followed by DD-Treponema spp. Lastly, the tattooing method showed potential for future use in experimental induction of DD by depositing Treponema spp. at the epidermis-dermis junction; however, we were unable to induce lesions in dairy calves likely due to factors missing in our model. While DD etiology remains undetermined, this thesis supports the key role of Treponema spp. in the disease, offers a change in perspective on the involvement of non-treponemes in lesion initiation, discusses the challenges in reproducing this disease experimentally, and proposes a hypothetical scheme of pathogenesis for this complex disease.
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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 | 0.001 |
| 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 | 0.000 | 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".