57 President Oral Presentation Pick: The exploration of blood microRNA profiling in feedlot cattle with different lameness phenotypes
Bibliographic record
Abstract
Abstract Lameness is a significant economic and welfare issue in feedlot production. Effective treatment depends on diagnosing the cause of lameness accurately. There is increasing evidence that circulating microRNAs may serve as useful biomarkers for diagnosing disease. However, their association with lameness in feedlot cattle is currently unknown. The objective of this study was to investigate whether blood miRNA profiles can be associated with a specific lameness phenotype. Blood samples were collected via venepuncture (into Tempus™ Blood RNA Tubes) from 39 feedlot cattle diagnosed with digital dermatitis (DD; n = 2), toe tip necrosis syndrome (TTNS; n = 5), footrot (FR; n = 26) and healthy controls (HC; n = 6) for miRNA profiling using RNA sequencing. Samples were obtained at the time each animal was pulled from their pens for medical treatment. Total RNA was extracted from blood samples and subjected to small RNA libraries construction and RNA-sequencing. The sequence data analysis was performed using a web-based tool, sRNAtoolbox. A total of 596 miRNAs were identified across 39 blood samples with the expression of 444, 437, 465 and 575 miRNAs detected in the HC, DD, TTNS and FR groups, respectively. In addition, group-specific miRNAs were identified with 9 in the DD group, 45 in the TTN group, 28 in the FR group and 2 in the HC group. Moreover, 41, 8 and 36 differential expressed miRNAs (DE miRNAs) were detected in DD, TTNS and FR groups, respectively, when compared to the HC group using DeSeq2. These data suggest that miRNA profiles may differ according to lameness diagnosis in beef cattle. However, further investigation incorporating functional analyses of miRNA, increased sample size and the physiological profile of the animals are required to better understand the relationship between blood miRNA profiles and lameness in feedlot cattle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.266 | 0.076 |
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".