66 Blood MicroRNAomes Revealed Signatures of Lameness Phenotypes in Feedlot Cattle
Bibliographic record
Abstract
Abstract Lameness is a significant health issue in Canadian feedlots resulting in substantial economic losses. However, the high frequency of misdiagnosis of lameness using traditional methods leads to ineffective treatment, suggesting a new diagnostic method is needed. Growing evidence indicates that microRNAs (miRNAs) can be used as biomarkers for identifying the animals’ physiological status and the diagnosis of certain diseases, but this approach has not been utilized in beef cattle. The objective of this study was to compare blood miRNA profiles between lame and healthy cattle to investigate the relationship between miRNA expression patterns and specific lameness phenotypes. Blood samples were collected from 156 feedlot cattle at 0, 1, 2 and 3 weeks after being diagnosed with either digital dermatitis (DD; n=62), toe tip necrosis syndrome (TTNS; n = 40), or footrot (FR; n = 40) and healthy controls (HC; n = 12) for miRNA libraries construction and sequencing. A total of 314 expressed miRNAs were identified in 89 blood samples collected at week 0 across all groups, with TTN having the largest number of expressed miRNAs (291, P < 0.01) compared to all other groups (HC=276, DD=281, FR=278). Although miRNA profiles did not differ among the lameness types, type-specific miRNAs were identified; 6 in DD, 10 in TTN, 5 in FR and 7 in HC cattle. In addition, 3, 6 and 7 DE miRNAs were detected in DD, TTNS and FR when compared with HC cattle. Most of the DE and group-specific miRNAs are related to inflammation and skin diseases. The DE miRNAs were different between week 0 and all other weeks, indicating miRNA profiles may differ over time and with disease progression and recovery. These findings provide an initial understanding of the relationship between the cattle blood miRNAome and lameness and suggest that miRNA expression holds promise in the discovery of novel biomarkers for identifying lameness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".