Use of Serum MicroRNAs as Biomarker for Hepatobiliary Diseases in Dogs
Bibliographic record
Abstract
BACKGROUND: Current biochemical indicators cannot discriminate between parenchymal, biliary, vascular, and neoplastic hepatobiliary diseases. MicroRNAs are promising new biomarkers for hepatobiliary disease in humans and dogs. OBJECTIVE: To measure serum concentrations of an established group of microRNAs in dogs and to investigate their concentrations in various types of hepatobiliary diseases. ANIMALS: Forty-six client-owned dogs with an established diagnosis of hepatobiliary disease and stored serum samples and eleven client-owned healthy control Labrador Retrievers. METHODS: Retrospective study. Medical records of dogs with parenchymal, biliary, vascular, or neoplastic hepatobiliary diseases and control dogs were reviewed. Concentrations of miR-21, miR-122, miR-126, miR-148a, miR-200c, and miR-222 were quantified in serum by real-time polymerase chain reaction. RESULTS: No different microRNA concentrations were found in the adenoma and congenital portosystemic shunt groups. In all other diseases, miR-122 concentrations were elevated with the highest concentration in the mucocele group (267-fold, CI: 40-1,768, P < .001). In dogs with biliary diseases, miR-21 and miR-222 were only increased in dogs with mucoceles (26-fold, CI: 5-141, P = .005 and 13-fold, CI: 2-70, P = .025, respectively). Uniquely increased microRNAs were found in the hepatocellular carcinoma group (miR-200c, 35-fold increase, CI: 3-382, P = .035) and the chronic hepatitis group (miR-126, 22-fold increase, CI: 5-91, P = .002). CONCLUSIONS AND CLINICAL IMPORTANCE: A microRNA panel consisting of miR-21, miR-122, miR-126, miR-200c, and miR-222 can distinguish between parenchymal, biliary, and neoplastic hepatobiliary diseases. Serum microRNA profiling is a promising new tool that might be a valuable addition to conventional diagnostics to help diagnose various hepatobiliary diseases in dogs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".