Proinflammatory CD14 <sup>high</sup> CD16 <sup>low</sup> monocytes/macrophages prevail in <i>Treponema phagedenis-</i> associated bovine digital dermatitis
Bibliographic record
Abstract
ABSTRACT Digital dermatitis (DD) is a skin disease in cattle characterized by painful inflammatory ulcerative lesions in the feet, mostly associated with local colonization by Treponema spp., including Treponema phagedenis . The reason why most DD lesions remain actively inflamed and progress to chronic conditions despite antibiotic treatment remains unknown. Herein, we show an abundant infiltration of proinflammatory (CD14 high CD16 low ) monocytes/macrophages in active DD lesions, a skin response that was not mitigated by topical treatment with oxytetracycline. The associated bacterium, T. phagedenis , isolated from DD lesions in cattle, when injected subcutaneously into mice, induced abscesses with a local recruitment of Ly6G + neutrophils and proinflammatory (Ly6C high CCR2 + ) monocytes/macrophages, which appeared at infection onset (4 days post challenge) and persisted for at least 7 days post challenge. When exploring the ability of macrophages to regulate inflammation, we showed that bovine blood-derived macrophages challenged with live T. phagedenis or its structural components secreted IL-1β via a mechanism dependent on the NLRP3 inflammasome. This study shows that proinflammatory characteristics of monocytes/macrophages and neutrophils dominate active non-healing ulcerative lesions in active DD, thus likely impeding wound healing after antibiotic treatment.
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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) | 0.002 | 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".