Proteolytic dysfunction in bovine digital dermatitis caused by Treponema spp. and the therapeutic use of non-antimicrobial matrix metalloproteinase inhibitors
Bibliographic record
Abstract
Digital dermatitis (DD) is a painful hoof disease in cattle, closely linked to Treponema spp., for which the pathogenesis remains poorly understood and effective treatments are lacking. This study identifies a persistent pro-inflammatory proteomic signature in untreated, non-healing active DD lesions in the foot skin of cattle, characterized by increased immune cell infiltration and elevated matrix metalloproteinase (MMP) activity. Treatment of active DD lesions with CMC2.24, a non-antibiotic MMP inhibitor, maintains the lesions in an M2 stage in most cases; however, it reduces histological dermatitis, lowering reactive oxygen species (ROS) levels and downregulating proteomic keratinization pathways. Mechanistically, CMC2.24 dampens inflammatory IL-1β cell responses in bovine macrophages exposed to DD-associated T. phagedenis. Active DD lesions treated with oxytetracycline (OTC) antibiotics remain clinically similar to CMC2.24; however, they progress to chronic M4 stages, becoming keratinized and maintaining inflammatory features, including increased S100A8 expression and reactive oxygen species (ROS) production. These findings integrate clinical and histological observations from treatments with conventional antibiotics and antibiotic-free protease inhibitors, highlighting novel therapies for managing DD and mitigating the exaggerated inflammatory response.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".