A Differential Innate Immune Response in Active and Chronic Stages of Bovine Infectious Digital Dermatitis
Bibliographic record
Abstract
Digital dermatitis (DD) commonly associated with Treponema spp. infection is a prevalent infectious bovine foot disease characterized by ulcerative and necrotic lesions. Lesions associated with DD are often classified using the M-stage scoring system, with M0 indicating healthy heel skin and M4 indicating chronic lesions. Current treatments utilizing antimicrobials or chemical footbaths are often ineffective and rarely cure DD lesions. Understanding the function of the innate immune response in the pathogenesis of DD will help to identify novel therapeutic approaches. In this study, the expression of the local innate host defense peptides cathelicidins and β-defensins was investigated in cows with DD and associated with the presence of treponemes and inflammatory reactions. Samples from active ulcerative DD lesions (M2) had considerable epidermal neutrophilic infiltration and increased gene expression of β-defensin tracheal antimicrobial peptides compared to control skin. Samples from acute lesions also had elevated local Cxcl-8 and Tlr4 gene expression and abundant treponemes as identified by direct visualization, immunohistochemistry, and culture. Conversely, the anti-inflammatory peptide Il-10 was elevated in skin from chronic (M4) lesions, whereas bovine cathelicidin myeloid antimicrobial peptide 28 (Bmap-28) was increased in skin from oxytetracycline-treated M2 lesions. Experiments using cultured human keratinocytes challenged with Treponema spp. isolated from clinical cases of bovine DD showed that structural products from treponemes are able to initiate the innate immune response, in part through TLR2 signaling. These findings indicate that neutrophil influx, Cxcl-8, and β-defensin are key markers of active DD. Cathelicidins and Il-10 seem important in response to treatment or during the chronic proliferative stages of the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".