PSXIV-11 Microbial Characterization of Digital Dermatitis and Foot rot in Feedlot Cattle
Bibliographic record
Abstract
Abstract Lameness is a significant health issue in feedlot cattle, having major economic, labor, and welfare implications. Digital dermatitis (DD), a lesion of the plantar surface of the foot, and foot rot (FR), which affects the interdigital cleft, are common infectious causes of feedlot lameness with bacterial origins. These hoof lesions can occur alone or in combination in the same hoof (DD+FR). Over a one-year period (2019-2020), 208 hoof swabs were obtained from cattle with FR (n = 55), DD (n = 18), and DD+FR (n = 29), and 5 completely healthy cattle (controls) from three southern Alberta commercial feedlots. 16S rRNA gene sequencing was used to characterize the bacterial microbiota of DD, FR, and DD+FR lesions relative to corresponding healthy skin controls found within the same affected animals. DD and FR lesion microbiotas showed reduced diversity compared with corresponding control skin. Multiple Treponema (members of Spirochaetaceae Family) species were differentially abundant in DD lesions compared with contralateral control skin. FR lesions are characterized by an abundance of Fusobacterium necrophorum and Porphyromonas levii, compared with contralateral control skin. The microbiome of DD+FR lesions was similar to FR lesions, where F. necrophorum and P. levii were increased compared with contralateral control skin. Other differentially abundant taxa (Helcococcus, Succiniclasticum spp.) found in FR and DD+FR lesions had been previously associated with DD, suggesting there may be an overlap of bacteria that have a role in both types of hoof lesions. The findings support previous literature showing that FR and DD are polybacterial in nature. The identification of novel bacterial species of DD and FR lesions could contribute information for the development of more targeted and effective prevention and treatment strategies. A novel finding of the sequencing analysis for hooves with concurrent DD and FR infection was characterized.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".